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Biosensor-Driven Adaptive Laboratory Evolution of Amino Acid Production in Corynebacterium Glutamicum

Rational engineering of microbial cell factories requires a detailed knowledge of the underlying metabolic networks. However, the high complexity of microbial physiology reduces rational targets to a low number of well-described gene functions. Random mutagenesis as well as adaptive laboratory evolution expand the toolbox of engineering approaches, which allow for a system-wide improvement of microbial production strains. So far, adaptive laboratory evolution was only applied for the improvement of fitness-linked production phenotypes, e.g. by exposure to thermal, solvent or chemical stressors (1).

In a recent study, we established a biosensor-driven adaptive laboratory evolution strategy to improve growth and production of inconspicuous, small metabolites. Sensor cells with the highest fluorescent output and hence, increased metabolite production, were iteratively isolated by fluorescence-activated cell sorting (FACS) and re-cultivated. Thereby an artificial selective pressure was imposed on the fluorescent output of the transcription factor-based biosensor. This strategy was successfully applied to the pyruvate-dehydrogenase-deficient L-valine production strain Corynebacterium glutamicum ΔaceE using the Lrp biosensor (2), which visualizes the intracellular concentration of branched-chain amino acids and L-methionine (3). Evolved strains featured improved growth, on average about 25% increased production and 3-4-fold reduced by-product (L-alanine) formation. Out of seven identified mutations, four mutations were reintroduced into the cured ΔaceE background. One mutation in the global regulator GlxR was attributed to the reduced by-product formation. Remarkably, a loss-of-function mutation within the urease-accessory protein UreD was found to improve L-valine production by about 100%. Altogether, these results emphasize biosensor-driven adaptive laboratory evolution as a straightforward approach to improve metabolic cell factories.

(1)   Abatemarco, J. A. Hill and H.S. Alper (2013) Expanding the metabolic engineering toolbox with directed evolution. Biotechnol J 8, 1397-1410.

(2)   Mahr, R., C. Gätgens, J. Gätgens, T. Polen, J. Kalinowski, and J. Frunzke*, (2015) Biosensor-driven adaptive laboratory evolution of l-valine production in Corynebacterium glutamicum. Metab Eng 32:184-194. doi: 10.1016/j.ymben.2015.09.017

(3)   Mustafi, N., A. Grünberger, D. Kohlheyer, M. Bott & J. Frunzke*, (2012) The development and application of a single-cell biosensor for the detection of L-methionine and branched-chain amino acids. Metab Eng 14: 449-457.