In silico design of anaerobic growth-coupled product formation in Escherichia coli: experimental validation using a simple polyol, glycerol

Opening a Novel Biosynthetic Pathway to Dihydroxyacetone and Glycerol in Escherichia coli Mutants through Expression of a Gene Variant (fsaAA129S) for Fructose 6-Phosphate Aldolase

Phosphofructokinase (PFK) plays a pivotal role in glycolysis. By deletion of the genes pfkA, pfkB (encoding the two PFK isoenzymes), and zwf (glucose 6-phosphate dehydrogenase) in Escherichia coli K-12, a mutant strain (GL3) with a complete block in glucose catabolism was created. Introduction of plasmid-borne copies of the fsaA wild type gene (encoding E. coli fructose 6-phosphate aldolase, FSAA) did not allow a bypass by splitting fructose 6-phosphate (F6P) into dihydroxyacetone (DHA) and glyceraldehyde 3-phosphate (G3P). Although FSAA enzyme activity was detected, growth on glucose was not reestablished. A mutant allele encoding for FSAA with an amino acid exchange (Ala129Ser) which showed increased catalytic efficiency for F6P, allowed growth on glucose with a µ of about 0.12 h⁻¹. A GL3 derivative with a chromosomally integrated copy of fsaA^A129S (GL4) grew with 0.05 h⁻¹ on glucose. A mutant strain from GL4 where dhaKLM genes were deleted (GL5) excreted DHA. By deletion of the gene glpK (glycerol kinase) and overexpression of gldA (of glycerol dehydrogenase), a strain (GL7) was created which showed glycerol formation (21.8 mM; yield approximately 70% of the theoretically maximal value) as main end product when grown on glucose. A new-to-nature pathway from glucose to glycerol was created.

Genetic Optimization Algorithm for Metabolic Engineering Revisited

To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives; (ii) the identification of gene target-sets according to logical gene-protein-reaction associations; (iii) minimization of the number of network perturbations; and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.