Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes

Abstract

Escherichia coli strains are widely used in academic research and biotechnology. New technologies for quantifying strain-specific differences and their underlying contributing factors promise greater understanding of how these differences significantly impact physiology, synthetic biology, metabolic engineering, and process design. Here, we quantified strain-specific differences in seven widely used strains of E. coli (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110, and W) using genomics, phenomics, transcriptomics, and genome-scale modelling. Metabolic physiology and gene expression varied widely with downstream implications for productivity, product yield, and titre. These differences could be linked to differential regulatory structure. Analysing high-flux reactions and expression of encoding genes resulted in a correlated and quantitative link between these sets, with strain-specific caveats. Integrated modelling revealed that certain strains are better suited to produce given compounds or express desired constructs considering native expression states of pathways that enable high-production phenotypes. This study yields a framework for quantitatively comparing strains in a species with implications for strain selection.

Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring

“…The K-12 strains produced approximately 0.7 mol acetate per mol glucose, in good agreement with previous reports (Chen et al, 2011; Leighty and Antoniewicz, 2013; Rahman and Shimizu, 2008). The BL21 strain produced significantly less acetate (0.39 mol/mol), a phenotype which has also been well characterized previously (Monk et al, 2016; Waegeman et al, 2012, 2011). In fact, previous studies have reported even lower acetate yields of 0.2 mol/mol and less, which may indicate a relatively larger variability in BL21 strains compared to K-12 strains.…”

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

“…This is most likely due to utilization of the Phosphoenolpyruvate Carboxylase (PPC) in MG1655 and W3110 (McCloskey et al, 2016a(McCloskey et al, , 2016b, and overall increased levels of TCA cycle intermediates in strain C. E. coli MG1655 and W3110 were also found to have the highest levels of L-arginine. It should be noted that when compared to the transcript levels or flux predictions of the amino acid producing pathways (Monk et al, 2016), little correlation between the absolute metabolite concentrations was found, which indicates the difficulty in predicting metabolite levels through indirect evidence as is consistent with previous works (Daran-Lapujade et al, 2007;Hackett et al, 2016). Levels of central metabolism intermediates differed vastly between the strains (Fig.…”

“…Hierarchical clustering (Fig. 4A) reveals that similar to gene expression levels, the levels of intracellular metabolites do not reflective genomic distances between the strains (Monk et al, 2016). The hierarchical clustering is also consistent with how the strains group by Partial Least Squares Differential Analysis (PLS-DA, Fig.…”

“…Choi et al, 2016;Park et al, 2011Park et al, , 2007Yim et al, 2011) owing to the rich toolset available for gene and expression modification (Datsenko and Wanner, 2000;Jiang et al, 2015;Ronda et al, 2016;Wang et al, 2009) as well as accumulated historical knowledge base of biochemical understanding (Guo et al, 2013;Kanehisa et al, 2017;Keseler et al, 2013;Monk et al, 2017). Seven commonly used strains in biotechnology applications include K-12 MG1655, K-12 W3110, K-12 DH5a, BL21, C, Crooks, and W. It has been shown that particular strains are better suited for different applications (Monk et al, 2016). For example, DH5a is often preferred in cloning applications (Song et al, 2015;Taylor et al, 1993), while BL21 is often preferred for expression of recombinant proteins (Marisch et al, 2013a;Robichon et al, 2011).…”

“…It has also been shown that choice of host strain for production of a given compound can significantly impact production titers (Na et al, 2013). Various comparisons between the seven industrial strains of E. coli under bioprocess conditions or as host strains for a particular metabolic engineering endeavor have been described (Chae et al, 2010;Fathi-Roudsari et al, 2016;Marisch et al, 2013aMarisch et al, , 2013bMonk et al, 2016). However, a comprehensive comparison of the metabolomes between the strains does not yet exist.…”