Pseudomonas putida KT2440 Fluxomics
EMSL Project ID
51792
Abstract
13C Metabolic Flux Analysis (13C MFA) has provided critical information needed to constrain genome scale metabolic models, which can yield non-intuitive solutions to improve metabolite flux towards a desired product. A constrained genome scale metabolic model has been described for the emerging industrial organism, Pseudomonas putida KT2440, curated using 13C glucose data. While glucose is a common microbial carbon feed, other carbon sources could be used to support microbial bioconversion processes, such as those derived from lignocellulosic biomass, to meet economic constraints imposed on the bioconversion of plant-derived carbon streams to commodity chemicals. No data has been published in this organism to answer this query using aromatic compounds, but several recent studies have described methods for metabolite quenching necessary for additional flux characterization. To address these limitations, experimentally-derived fluxomics data for aromatic compounds would extend the predictive power of the P. putida KT2440 models to utilize monomeric aromatic compounds as carbon sources. In this EMSL-BRC (JBEI) pilot proposal, the overarching aim of this work is to to characterize the metabolic flux of P. putida KT2440 when fed 13C 4-hydroxybenzoate (4-HB), a candidate lignin-derived monomeric aromatic compound. However, we must first ensure our metabolite quenching methods enable a fair comparison with existing 13C glucose data. In year 1 we will initially validate our methods to quantify 13C-glucose metabolic flux analysis with WT and contrast changes with a metabolically rewired strain. These experiments have the potential to better characterize important physiological parameters of an emerging industrial microbe when cultivated on industrially relevant lignin-derived carbon streams and improve the fidelity of predictive engineering strategies.Project Details
Project type
BRC Research
Start Date
2021-02-05
End Date
2021-12-31
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members