In silico identification of gene targets to enhance C12 fatty acid
production in Escherichia coli
The global interest in fatty acids is steadily rising due to their
wealth of industrial potential ranging from cosmetics to biofuels.
Unfortunately, certain fatty acids, such as monounsaturated C12, cannot
be produced cost and energy-efficiently using conventional methods.
Biosynthesis of fatty acids using microorganisms can overcome this
drawback. However, rewiring a microbe’s metabolome for increased
production remains challenging. To overcome this, sophisticated
genome-wide metabolic network models have become available. These models
predict the effect of genetic perturbations on the metabolism, thereby
serving as a guide for metabolic pathways optimization. In this work, we
used constraint-based modeling in combination with the algorithm
Optknock to identify gene deletions in Escherichia coli that
improve the C12 fatty acid production. Nine gene targets were identified
that, when deleted, were predicted to increase C12 titers. Targets play
a role in anaplerotic reactions, amino acid synthesis, carbon metabolism
and cofactor-balancing. Subsequently, we constructed the corresponding
(combinatorial) deletion mutants to validate the in silico
predictions in vivo. Our highest producer (Δ maeB Δ
ndk Δ pykA) reaches a titer of 6.7 mg/L, corresponding to
a 7.5-fold increase in C12 fatty acid production. This study
demonstrates that model-guided metabolic engineering is a useful tool to
improve C12 fatty acid production.