Metabolic Pathway Determination and Flux Analysis in Nonmodel Microorganisms Through 13C-Isotope Labeling

Author(s):  
Xueyang Feng ◽  
Wei-Qin Zhuang ◽  
Peter Colletti ◽  
Yinjie J. Tang
2009 ◽  
Vol 152 (2) ◽  
pp. 602-619 ◽  
Author(s):  
Shyam K. Masakapalli ◽  
Pascaline Le Lay ◽  
Joanna E. Huddleston ◽  
Naomi L. Pollock ◽  
Nicholas J. Kruger ◽  
...  

2018 ◽  
Vol 7 (6) ◽  
pp. 667-671 ◽  
Author(s):  
Wenxuan Zhou ◽  
Kun Wang ◽  
Shijun Wang ◽  
Shichen Yuan ◽  
Wei Chen ◽  
...  

2016 ◽  
Vol 113 (51) ◽  
pp. 14710-14715 ◽  
Author(s):  
Jianhai Du ◽  
Aya Yanagida ◽  
Kaitlen Knight ◽  
Abbi L. Engel ◽  
Anh Huan Vo ◽  
...  

The retinal pigment epithelium (RPE) is a monolayer of pigmented cells that requires an active metabolism to maintain outer retinal homeostasis and compensate for oxidative stress. Using13C metabolic flux analysis in human RPE cells, we found that RPE has an exceptionally high capacity for reductive carboxylation, a metabolic pathway that has recently garnered significant interest because of its role in cancer cell survival. The capacity for reductive carboxylation in RPE exceeds that of all other cells tested, including retina, neural tissue, glial cells, and a cancer cell line. Loss of reductive carboxylation disrupts redox balance and increases RPE sensitivity to oxidative damage, suggesting that deficiencies of reductive carboxylation may contribute to RPE cell death. Supporting reductive carboxylation by supplementation with an NAD+precursor or its substrate α-ketoglutarate or treatment with a poly(ADP ribose) polymerase inhibitor protects reductive carboxylation and RPE viability from excessive oxidative stress. The ability of these treatments to rescue RPE could be the basis for an effective strategy to treat blinding diseases caused by RPE dysfunction.


2006 ◽  
Vol 73 (3) ◽  
pp. 718-729 ◽  
Author(s):  
Yinjie J. Tang ◽  
Judy S. Hwang ◽  
David E. Wemmer ◽  
Jay D. Keasling

ABSTRACT The central metabolic fluxes of Shewanella oneidensis MR-1 were examined under carbon-limited (aerobic) and oxygen-limited (microaerobic) chemostat conditions, using 13C-labeled lactate as the sole carbon source. The carbon labeling patterns of key amino acids in biomass were probed using both gas chromatography-mass spectrometry (GC-MS) and 13C nuclear magnetic resonance (NMR). Based on the genome annotation, a metabolic pathway model was constructed to quantify the central metabolic flux distributions. The model showed that the tricarboxylic acid (TCA) cycle is the major carbon metabolism route under both conditions. The Entner-Doudoroff and pentose phosphate pathways were utilized primarily for biomass synthesis (with a flux below 5% of the lactate uptake rate). The anaplerotic reactions (pyruvate to malate and oxaloacetate to phosphoenolpyruvate) and the glyoxylate shunt were active. Under carbon-limited conditions, a substantial amount (9% of the lactate uptake rate) of carbon entered the highly reversible serine metabolic pathway. Under microaerobic conditions, fluxes through the TCA cycle decreased and acetate production increased compared to what was found for carbon-limited conditions, and the flux from glyoxylate to glycine (serine-glyoxylate aminotransferase) became measurable. Although the flux distributions under aerobic, microaerobic, and shake flask culture conditions were different, the relative flux ratios for some central metabolic reactions did not differ significantly (in particular, between the shake flask and aerobic-chemostat groups). Hence, the central metabolism of S. oneidensis appears to be robust to environmental changes. Our study also demonstrates the merit of coupling GC-MS with 13C NMR for metabolic flux analysis to reduce the use of 13C-labeled substrates and to obtain more-accurate flux values.


2019 ◽  
Author(s):  
Shiyu Liu ◽  
Ziwei Dai ◽  
Daniel E. Cooper ◽  
David G. Kirsch ◽  
Jason W. Locasale

ABSTRACTThe carbon source for catabolism in vivo is a fundamental question in metabolic physiology. Limited by data and rigorous mathematical analysis, controversy exists over the nutritional sources for carbon in the tricarboxylic acid (TCA) cycle under physiological settings. Using isotope-labeling data in vivo across several experimental conditions, we construct multiple models of central carbon metabolism and develop methods based on metabolic flux analysis (MFA) to solve for the preferences of glucose, lactate, and other nutrients used in the TCA cycle across many tissues. We show that in nearly all circumstances, glucose contributes more than lactate as a nutrient source for the TCA cycle. This conclusion is verified in different animal strains from different studies, different administrations of 13C glucose, and is extended to multiple tissue types. Thus, this quantitative analysis of organismal metabolism defines the relative contributions of nutrient fluxes in physiology, provides a resource for analysis of in vivo isotope tracing data, and concludes that glucose is the major nutrient used for catabolism in mammals.


Metabolites ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 447
Author(s):  
Yujue Wang ◽  
Fredric E. Wondisford ◽  
Chi Song ◽  
Teng Zhang ◽  
Xiaoyang Su

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.


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