Metabolic Fluxes in an Illuminated Arabidopsis Rosette

• Published in: The Plant cell Vol. 25; no. 2; pp. 694 - 714
• Main Authors: Szecowka, Marek, Heise, Robert, Tohge, Takayuki, Nunes-Nesi, Adriano, Vosloh, Daniel, Huege, Jan, Feil, Regina, Lunn, John, Nikoloski, Zoran, Stitt, Mark, Fernie, Alisdair R., Arrivault, Stéphanie
• Format: Journal Article
• Language: English
• Published: United States American Society of Plant Biologists 01-02-2013
• Subjects: Amino acid metabolism; Amino acids; Arabidopsis - growth & development; Arabidopsis - metabolism; Carbohydrate Metabolism; Carbon Dioxide - chemistry; Carbon Isotopes; Cell Compartmentation; Gas Chromatography-Mass Spectrometry; Inositol - chemistry; Inositol - metabolism; Isotope Labeling - methods; Isotopic labeling; Kinetics; Metabolism; Metabolites; Metabolome; Models, Biological; Molecular Biology - methods; Organic acids; Photosynthesis; Plants; Product labeling; Starch - metabolism; Starches
• ISSN: 1040-4651; 1532-298X
• DOI: 10.1105/tpc.112.106989

Photosynthesis is the basis for life, and its optimization is a key biotechnological aim given the problems of population explosion and environmental deterioration. We describe a method to resolve intracellular fluxes in intact Arabidopsis thaliana rosettes based on time-dependent labeling patterns in the metabolome. Plants photosynthesizing under limiting irradiance and ambient CO₂ in a custom-built chamber were transferred into a ¹³CO₂-enriched environment. The isotope labeling patterns of 40 metabolites were obtained using liquid or gas chromatography coupled to mass spectrometry. Labeling kinetics revealed striking differences between metabolites. At a qualitative level, they matched expectations in terms of pathway topology and stoichiometry, but some unexpected features point to the complexity of subcellular and cellular compartmentation. To achieve quantitative insights, the data set was used for estimating fluxes in the framework of kinetic flux profiling. We benchmarked flux estimates to four classically determined flux signatures of photosynthesis and assessed the robustness of the estimates with respect to different features of the underlying metabolic model and the time-resolved data set.