Abstract
Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.
Highlights
The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found
Traditional high-throughput screening approaches in metabolic engineering have primarily relied upon basic physiology markers of strain performance
All cultures were grown in 25 mL of unlabeled or labeled glucose M9 minimal media (Sambrook and Russell, 2001) with trace elements (Fong et al, 2005) and sampled from a heat block in 50 mL autoclaved tubes that were maintained at 37 °C and aerated using magnetics
Summary
Traditional high-throughput screening approaches in metabolic engineering have primarily relied upon basic physiology markers of strain performance. Physiological markers often include basic input/ output parameters such as growth rate, substrate uptake rate, product excretion rate, yield, and productivity. While useful, these markers give little information on the underlying strain physiology. Omics data types (e.g., transcriptomics, metabolomics, fluxomics, etc.,) could potentially provide a richer and deeper understanding of strain performance, which would allow more informed engineering decisions (McCloskey et al, 2013). Of the various -omics data types available, metabolomics provides the greatest potential to gain rich and deep insight on strain physiology at a lower cost and higher throughput (Fuhrer et al, 2011; Guder et al, 2017; Link et al, 2015)
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