Application of a Genetically-Encoded Metabolite Sensor for Single Cell Analysis and Development of Production Strains

The analysis of microbial metabolite production is typically performed using bulk techniques, which obscure information with respect to single cell behavior. We have developed a genetically-encoded biosensor based on the transcriptional regulator Lrp (leucine-responsive protein) of Corynebacterium glutamicum (1). The sensor allows the intracellular detection of methionine and branched-chain amino acids at the single cell level by converting this information into a fluorescent signal. The suitability of the biosensor to mirror different intracellular concentrations of effector amino acids was applied to study population dynamics of the valine producer strain C. glutamicum ΔaceE lacking the pyruvate dehydrogenase complex.

Flow cytometry based monitoring of biosensor cells during lab-scale fed-batch cultivation revealed the appearance of subpopulations varying in productivity and viability. In addition, live cell imaging studies using microfluidic chip devices displayed different types of non-producing cells within isogenic microcolonies of C. glutamicum ΔaceE (2). The appearance of non-productive subpopulations might strongly impact the performance and stability of the production process; therefore, genetically-encoded biosensors have the great potential to reveal bottlenecks for improving fermentation processes.

In further studies, the Lrp-biosensor was applied to improve growth and productivity of C. glutamicum ΔaceE by in vivo evolution. Using fluorescent-activated cell sorting (FACS), stationary phase cells with the highest fluorescent output were iteratively isolated and (re-)cultivated. Isolated strains revealed a significantly increased growth rate, shortened lag-phase and an increased final optical density. The L-valine production of some strains was increased up to 100% compared to the parental strain while the formation of by-products (L-alanine) was reduced. These results emphasize biosensor-based strain evolution as a straightforward approach to improve growth and productivity of microbial production strains.

(1) Mustafi et al., 2012 Met Eng 14 (4), 449-457.

(2) Mustafi et al., 2014 PLoS One 9 (1): e85731.