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Quantitative Bioreactor Monitoring of Intracellular Bacterial Metabolites in Clostridiumautoethanogenum Using Liquid Chromatography–Isotope Dilution Mass Spectrometry

[Image: see text] We report a liquid chromatography–isotope dilution mass spectrometry method for the simultaneous quantification of 131 intracellular bacterial metabolites of Clostridium autoethanogenum. A comprehensive mixture of uniformly (13)C-labeled internal standards (U-(13)C IS) was biosynth...

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Detalles Bibliográficos
Autores principales: Safo, Laudina, Abdelrazig, Salah, Grosse-Honebrink, Alexander, Millat, Thomas, Henstra, Anne M., Norman, Rupert, Thomas, Neil R., Winzer, Klaus, Minton, Nigel P., Kim, Dong-Hyun, Barrett, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173575/
https://www.ncbi.nlm.nih.gov/pubmed/34095647
http://dx.doi.org/10.1021/acsomega.0c05588
Descripción
Sumario:[Image: see text] We report a liquid chromatography–isotope dilution mass spectrometry method for the simultaneous quantification of 131 intracellular bacterial metabolites of Clostridium autoethanogenum. A comprehensive mixture of uniformly (13)C-labeled internal standards (U-(13)C IS) was biosynthesized from the closely related bacterium Clostridium pasteurianum using 4% (13)C–glucose as a carbon source. The U-(13)C IS mixture combined with (12)C authentic standards was used to validate the linearity, precision, accuracy, repeatability, limits of detection, and quantification for each metabolite. A robust-fitting algorithm was employed to reduce the weight of the outliers on the quantification data. The metabolite calibration curves were linear with R(2) ≥ 0.99, limits of detection were ≤1.0 μM, limits of quantification were ≤10 μM, and precision/accuracy was within RSDs of 15% for all metabolites. The method was subsequently applied for the daily monitoring of the intracellular metabolites of C. autoethanogenum during a CO gas fermentation over 40 days as part of a study to optimize biofuel production. The concentrations of the metabolites were estimated at steady states of different pH levels using the robust-fitting mathematical approach, and we demonstrate improved accuracy of results compared to conventional regression. Metabolic pathway analysis showed that reactions of the incomplete (branched) tricarboxylic acid “cycle” were the most affected pathways associated with the pH shift in the bioreactor fermentation of C. autoethanogenum and the concomitant changes in ethanol production.