Browsing by Subject "Design of experiment"

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    Rational Engineering of Expression Level of Multi-Gene Systems encoding Natural Product Biosynthesis in Streptomyces
    (2020-08) Hsu, Suzie
    It has been shown that genomes of bacteria, especially members of the Streptomyces genus, harbor unprecedented numbers of biosynthetic gene clusters (BGCs) potentially encoding novel compounds. However, cloning and controlled expression of these large BGCs in a heterologous host require tedious optimization on a case-by-case basis. This dissertation presents a synthetic biology platform to rapidly reconstitute BGCs by refactoring and physically piecing DNA fragments together in a hierarchical manner. The two core technologies are (i) a high-throughput DNA assembly pipeline for high GC organisms and (ii) synthetic genetic elements to control gene expression in Streptomyces. As a proof of concept, a small library of synthetic gene clusters was constructed to encode ent-atisanoic acid, a late-stage intermediate of the neuroprotectant serofendic acid. We successfully controlled the relative expression level of individual genes, identified the tailoring enzyme required for the oxidation as well as demonstrated the utility of this DNA assembly pipeline. Next, we rationally optimize isoprenoid biosynthesis by perturbing relative expression of eight enzymes in the methylerythritol phosphate (MEP) pathway. One of the Design of Experiment (DoE) methods called Plackett-Burman design was used to guide the optimization effort for this eight-gene system. A five-level Plackett-Burman design was used to guide the design of 125 unique synthetic gene clusters encoding the MEP pathway, which was required to fully screen the effects of expression of each gene on the output measured by isoprenoid titer. In the eight-gene pathway, each gene has one of five expression levels. Total screening of the entire pathway variants has revealed a surprising degree of robustness in actinobacterial secondary metabolism. In sum, the DNA assembly pipeline will become a powerful tool to fuel future rational optimization efforts of multi-gene systems, including large BGCs.