Design of a genetically encoded biosensor to establish a high-throughput screening platform for L-cysteine overproduction

Abstract

Metabolic engineering seeks to rewire the metabolic network of cells for the efficient production of value-added compounds from renewable substrates. However, it remains challenging to evaluate and identify strains with the desired phenotype from the vast rational or random mutagenesis library. One effective approach to resolve this bottleneck is to design an efficient high-throughput screening (HTS) method to rapidly detect and analyze target candidates. L-cysteine is an important sulfur-containing amino acid and has been widely used in agriculture, pharmaceuticals, cosmetics, and food additive industries. However, HTS methods that enable monitoring of L-cysteine levels and screening of the enzyme variants and strains to confer superior L-cysteine biosynthesis remain unavailable, greatly limiting the development of efficient microbial cell factories for L-cysteine production at the industrial scale. Here, we took advantage of the L-cysteine-responsive transcriptional regulator CcdR to develop a genetically encoded biosensor for engineering and screening the L-cysteine overproducer. The in vivo L-cysteine-responsive assays and in vitro electrophoretic mobility shift assay (EMSA) and DNase I footprint analysis indicated that CcdR is a transcriptional activator that specifically interacts with L-cysteine and binds to its regulatory region to induce the expression of target genes. To improve the response performance of the L-cysteine biosensor, multilevel optimization strategies were performed, including regulator engineering by semi-rational design and systematic optimization of the genetic elements by modulating the promoter and RBS combination. As a result, the dynamic range and sensitivity of the biosensor were significantly improved. Using this the excellent L-cysteine biosensor, a HTS platform was established by coupling with fluorescence-activated cell sorting (FACS) and was successfully applied to achieve direct evolution of the key enzyme in the L-cysteine biosynthetic pathway to increase its catalytic performance and to screen the high L-cysteine-producing strains from the random mutagenesis library. These results presented a paradigm of design and optimization of biosensors to dynamically detect metabolite concentrations and provided a promising tool enabling HTS and metabolic regulation to construct L-cysteine hyperproducing strains to satisfy industrial demand.