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Quantitative Isotope-Dilution High-Resolution-Mass-Spectrometry Analysis of Multiple Intracellular Metabolites in Clostridium autoethanogenum with Uniformly 13C-Labeled Standards Derived from Spirulina.

Sarah SchatschneiderSalah AbdelrazigLaudina SafoAnne M HenstraThomas MillatDong-Hyun KimKlaus WinzerNigel P MintonDavid A Barrett
Published in: Analytical chemistry (2018)
We have investigated the applicability of commercially available lyophilized spirulina ( Arthrospira platensis), a microorganism uniformly labeled with 13C, as a readily accessible source of multiple 13C-labeled metabolites suitable as internal standards for the quantitative determination of intracellular bacterial metabolites. Metabolites of interest were analyzed by hydrophilic-interaction liquid chromatography coupled with high-resolution mass spectrometry. Multiple internal standards obtained from uniformly (U)-13C-labeled extracts from spirulina were used to enable isotope-dilution mass spectrometry (IDMS) in the identification and quantification of intracellular metabolites. Extraction of the intracellular metabolites of Clostridium autoethanogenum using 2:1:1 chloroform/methanol/water was found to be the optimal method in comparison with freeze-thaw, homogenization, and sonication methods. The limits of quantification were ≤1 μM with excellent linearity for all of the calibration curves ( R2 ≥ 0.99) for 74 metabolites. The precision and accuracy were found to be within relative standard deviations (RSDs) of 15% for 49 of the metabolites and within RSDs of 20% for all of the metabolites. The method was applied to study the effects of feeding different levels of carbon monoxide (as a carbon source) on the central metabolism and Wood-Ljungdahl pathway of C. autoethanogenum grown in continuous culture over 35 days. Using LC-IDMS with U-13C spirulina allowed the successful quantification of 52 metabolites in the samples, including amino acids, carboxylic acids, sugar phosphates, purines, and pyrimidines. The method provided absolute quantitative data on intracellular metabolites that was suitable for computational modeling to understand and optimize the C. autoethanogenum metabolic pathways active in gas fermentation.
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