Metabolic analysis of adaptive evolution for in silico‐designed lactate‐producing strains

Abstract

Experimental evolution is now frequently applied to many biological systems to achieve desired objectives. To obtain optimized performance for metabolite production, a successful strategy has been recently developed that couples metabolic engineering techniques with laboratory evolution of microorganisms. Previously, we reported the growth characteristics of three lactate-producing, adaptively evolved Escherichia coli mutant strains designed by the OptKnock computational algorithm. Here, we describe the use of (13)C-labeled experiments and mass distribution measurements to study the evolutionary effects on the fluxome of these differently designed strains. Metabolic flux ratios and intracellular flux distributions as well as physiological data were used to elucidate metabolic responses over the course of adaptive evolution and metabolic differences among strains. The study of 3 unevolved and 12 evolved engineered strains as well as a wild-type strain suggests that evolution resulted in remarkable improvements in both substrate utilization rate and the proportion of glycolytic flux to total glucose utilization flux. Among three strain designs, the most significant increases in the fraction of glucose catabolized through glycolysis (>50%) and the glycolytic fluxes (>twofold) were observed in phosphotransacetylase and phosphofructokinase 1 (PFK1) double deletion (pta- pfkA) strains, which were likely attributed to the dramatic evolutionary increase in gene expression and catalytic activity of the minor PFK encoded by pfkB. These fluxomic studies also revealed the important role of acetate synthetic pathway in anaerobic lactate production. Moreover, flux analysis suggested that independent of genetic background, optimal relative flux distributions in cells could be achieved faster than physiological parameters such as nutrient utilization rate.

Metabolic Characterization of Escherichia coli Strains Adapted to Growth on Lactate

“…After filtration, 3 l of derivatized sample was injected for the gas chromatography-mass spectrometry (GC-MS) assay. The GC-MS analysis was performed with a Trace GC/Trace MS Plus system (Thermo Electron Corporation, Waltham, MA), and mass spectral data were obtained for fragments of most derivatized amino acids, as previously reported (20). Prior to analysis of cellular metabolism, the raw mass isotopomer distribution vector (MDV) (fractional abundance set for amino acid fragment with different mass numbers) was corrected for the natural isotope abundances of carbon atoms introduced from the derivatization reagent and all noncarbon atoms involved in the whole fragment (30).…”

“…Recently, 13 C isotopomer-based flux analyses have been successfully applied to investigate metabolic responses and underlying mechanisms in various prokaryotic and eukaryotic systems (4,9,12,26,29,33). We have previously studied the metabolic flux states for several gene deletion mutants of Escherichia coli that evolved on glucose (16,20). These flux analyses indicated that these mutant strains adapted to the evolutionary process by activating either latent pathways or pathway of which the major enzyme was deleted.…”

Predicting evolution from genomics: experimental evolution of bacteriophage T7

“…Bacteria offer the advantage that a great deal is known about metabolism, at the genetic and biochemical levels, as well as at a system-wide level through metabolic flux (Dykhuizen et al, 1987); consequently, the metabolic bases of evolution have been a focus of study (Maharjan and Ferenci, 2003;Hua et al, 2006Hua et al, , 2007Maharjan et al, 2006). This multi-level understanding facilitates the interpretation of evolution.…”

Gene Expression Profiling and the Use of Genome-Scale In Silico Models ofEscherichia colifor Analysis: Providing Context for Content

“…In the second case, a computational algorithm was used to predict gene deletion mutant strains of E. coli that would produce and excrete lactate in order to grow optimally (42). The gene expression data from a strain predicted with the OptKnock algorithm were much more consistent with lactate production than the gene expression data from wildtype E. coli (P ϭ 0.0014).…”

“…The utility of this method was further validated with a double-gene-deletion strain (⌬pfkA ⌬pta) (34,42) for which there were no flux data. By using only uptake and secretion rates for a few metabolites, bottleneck reactions (e.g., those of pyruvate dehydrogenase and ATP synthase) were predicted, and the genes associated with these reactions were evaluated in rela- VOL.…”