Mevalonate, an intermediate in the mevalonate pathway of eukaryotes and archaea, is a precursor in the production of biologically important terpenes, such as the anticancer agent taxol and the antimalarial prostratin. These molecules are naturally produced by plants, where producing large quantities can prove problematic due to challenges in plant culture such as aggregation and oxygen transfer. As a result, more robust bacterial production appears to be a promising avenue for optimization. The upper mevalonate pathway consists of three genes responsible for the conversion of acetyl-CoA to mevalonate. Mevalonate is then converted by the lower pathway to isopentyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), which are used by the cell for terpene and steroid production. Many optimization strategies have been employed to increase mevalonate production across multiple species including both bacteria and yeast by focusing on fermentation optimization and pathway gene homologs. Here, we have modified the previously described ePathOptimize[SKV1] system, in conjunction with a Golden Gate cloning strategy, to construct a combinatorial promoter library, consisting of six promoters of variable strength, to modulate the expression of each of three pathway genes from the upper yeast mevalonate pathway to optimize mevalonate production in E. coli. A novel biosensor was developed to facilitate screening of a large library of 216 promoter gene combinations for high-producing strains. Our biosensor couples a promoter identified from transcriptomic (RNA-Seq) data with a fluorescent reporter protein to facilitate high throughput library screening. This work represents the first utilization of RNA-Seq for the optimization of the mevalonate pathway. The optimization of mevalonate production is an important first step for the production of higher complexity terpenes such as taxol and its precursors in E. coli.

Optimization of heterologous mevalonate production in E. coli with a combinatorial promoter library and novel biosensor

255th National Meeting (2018)

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