RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli

Abstract

A B S T R A C TFast 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.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

“…Additionally, the directionality of reversible reactions and the reaction Gibbs energy can be used to find enzymes that are likely to be allosterically regulated, since these reactions are generally thought to be far from equilibrium. For example, McCloskey et al [97] used an E. coli genome-scale model as an integration platform to calculate the Gibbs free energy of the metabolic reactions in different E. coli strains. They showed significant differences in reaction thermodynamics, among them such drastic changes that led to reversing the reaction directionality.…”

“…Kinetic model Personalized kinetic model parametrization and analysis of red blood cells [91] Kinetic model IMCA approach to trace back the changes that led to the observed phenotype [92] Constraint-based model Constraint-based modelling approach and single-point extracellular metabolomics [93] Constraint-based model Tool for system thermodynamic analysis of quantitative metabolomics [96] Thermodynamic FVA Genome-scale thermodynamic FVA applied to integrate the metabolomics data of different industrial strains of E. coli [97] Thermodynamic EFM Combination of thermodynamic and EFM analysis [99] Constraint-based model Genome-scale thermodynamic CBM applied to integrate metabolomics of E. coli to research aerobic and anaerobic metabolism [98] Time-series metabolomics data integration Stoichiometric MFA stMFA of carbon central metabolism in mammalian cell culture [101] Dynamic stoichiometric MFA Dynamic stMFA of carbon central metabolism used to study the effect of the temperature shift on CHO [103] FBA Genome-scale FBA for cancer cell line metabolism analysis [104] dFBA dFBA at a genome scale used to study diauxic growth in E. coli [46] MetDFBA dFBA variation used to integrate time-series metabolomics data [108] uFBA dFBA variation used to integrate time-series metabolomics data and study the metabolism of red blood cells [110] M-DFBA dFBA variation to integrate time-series metabolomics data to study myocardial metabolism under normal and ischemic conditions [107] R-DFBA dFBA variation to integrate time-series metabolomics data [106] FBA with flux activity coefficients FBA with time-course metabolomics measurement cues for altered flux activity around a metabolite to study the metabolism of pluripotent stem cells [111] Kinetic model (Michaelis-Menten laws). Parameters known (sampled across the literature values to account for uncertainty) Kinetic models used to find key regulations in the metabolism to study the response of metabolism on oxidative stress in E. coli [112] Kinetic model…”

The Rocky Road From Fed-Batch to Continuous Processing With E. coli

“…As seven volume changes in a steady-state recombinant protein production in single-vessel E. coli cultivations are regarded as a steady-state (Vemuri et al, 2006), studies in this field are tricky to implement, as cultivations might suffer from unexpected metabolic shifts before reaching a “steady-state” mode. Characterizing intracellular fluxes via a metabolome analysis indicates a high diversity occurring between different strains (Basan, 2018; McCloskey et al, 2018). The metabolome analysis state, which we use to deal with a highly complex system as different intracellular pathways are up- or downregulated, is very dependent on the host and the target protein (McCloskey et al, 2018).…”

“…Characterizing intracellular fluxes via a metabolome analysis indicates a high diversity occurring between different strains (Basan, 2018; McCloskey et al, 2018). The metabolome analysis state, which we use to deal with a highly complex system as different intracellular pathways are up- or downregulated, is very dependent on the host and the target protein (McCloskey et al, 2018). As shifts already occur without RPP, showing that cells try to adapt to the environment in chemostat cultivation, we hypothesize that the production of recombinant proteins will increase these shifts to a maximum.…”

An orthogonal amber initiator tRNA functions similarly across diverse <em>Escherichia coli</em> laboratory strains

“…In this work, we transformed E. coli K-12, and four other generally regarded as safe (GRAS) laboratory strains, with a plasmid expressing the amber initiator tRNA and evaluated its functionality and growth effects on the bacteria. We performed these tests because, despite these strains all belonging to E. coli phylogenetic group A, it is well known that there is significant variation between even closely related E. coli strains in their metabolism [3] [4] [5], transcriptional response to exogenous DNA expression [6] and rates of amber stop codon suppression [7]. We found that the amber initiator functions similarly across the five strains, effectively initiating translation at the orthogonal UAG start codon and that it had modest growth-slowing effects in the Crooks, W, and K-12 strains.…”