Combinatorial assembly platform enabling engineering of genetically stable metabolic pathways in cyanobacteria

Abstract

Abstract Cyanobacteria are simple, efficient, genetically-tractable photosynthetic microorganisms which in principle represent ideal biocatalysts for CO2 capture and conversion. However, in practice, genetic instability and low productivity are key, linked problems in engineered cyanobacteria. We took a massively parallel approach, generating and characterising libraries of synthetic promoters and RBSs for the cyanobacterium Synechocystis sp. PCC 6803, and assembling a sparse combinatorial library of millions of metabolic pathway-encoding construct variants. Genetic instability was observed for some variants, which is expected when variants cause metabolic burden. Surprisingly however, in a single combinatorial round without iterative optimisation, 80% of variants chosen at random and cultured photoautotrophically over many generations accumulated the target terpenoid lycopene from atmospheric CO2, apparently overcoming genetic instability. This large-scale parallel metabolic engineering of cyanobacteria provides a new platform for development of genetically stable cyanobacterial biocatalysts for sustainable light-driven production of valuable products directly from CO2, avoiding fossil carbon or competition with food production.