Unlocking the potential of cyanobacteria: a high-throughput strategy for enhancing biocatalytic performance through genetic optimization

Abstract

Cyanobacteria show promise as hosts for whole-cell biocatalysis. Their photoautotrophic metabolism can be leveraged for a sustainable production process. Despite advancements, performance still lags behind heterotrophic hosts. A key challenge is the limited ability to overexpress recombinant enzymes, which also hinders their biocatalytic efficiency. To address this, we generated large-scale expression libraries and developed a high-throughput method combining fluorescence-activated cell sorting (FACS) and deep sequencing in Synechocystis sp. PCC 6803 (Syn. 6803) to screen and optimize its genetic background. We apply this approach to enhance expression and biocatalyst performance for three enzymes: the ketoreductase LfSDR1M50, enoate reductase YqjM, and Baeyer–Villiger monooxygenase (BVMO) CHMOmut. Diverse genetic combinations yielded significant improvements: optimizing LfSDR1M50 expression showed a 17-fold increase to 39.2 U gcell dry weight (CDW)–1. In vivo activity of Syn. YqjM was improved 16-fold to 58.7 U gCDW–1 and, for Syn. CHMOmut, a 1.5-fold increase to 7.3 U gCDW–1 was achieved by tailored genetic design. Thus, this strategy offers a pathway to optimize cyanobacteria as expression hosts, paving the way for broader applications in other cyanobacteria strains and larger libraries.