Metabolic Engineering of Yeast to Maximize Precursor Formation and Polyketide Production
- Author(s): McTaggart, Tami Lee
- Advisor(s): Da Silva, Nancy A
- et al.
Increased demand for petroleum resources coupled with political, environmental and supply-chain concerns has increased the investment and interest in bio-based chemical production. In particular, polyketide production in microorganisms facilitates the expansion of sustainable, local and scalable chemical production due to the versatility of polyketides as precursors in catalytic conversion to a range of high-value chemicals including pharmaceuticals, food additives and plastics. For these reasons, optimization of polyketide production in yeast aids the sustainability and affordability of various consumer products. This work engineers and evaluates the yeasts Saccharomyces cerevisiae and Kluyveromyces marxianus for the production of triacetic acid lactone (TAL), a platform chemical precursor to 1,3-pentadiene, sorbic acid and other commodity and high-value products. When considering the production of a specific product, one fundamental choice is host selection. In conjunction with traditional host S. cerevisiae, we explore development of the TAL production platform in a thermotolerant, rapidly growing and industrially relevant yeast K. marxianus. Prior to metabolic engineering, we demonstrate production of 1.24 g/L TAL from xylose, a low-cost substrate. The yield of this process on xylose (0.0295 mol TAL/mol carbon) is comparable to the state-of-the art in other hosts from glucose and demonstrates a breakthrough for xylose utilization in yeast.
To build on this versatile K. marxianus platform, and to improve the S. cerevisiae one, CRISPR-Cas9 was utilized to introduce two heterologous enzymes that divert carbon away from CO2 production, resulting in a 60% improvement in TAL yield. This pathway was then introduced into a genome scale model and flux balance analysis was performed using OptKnock, resulting in new gene knockout targets that are complementary to this new pathway. In parallel to both rationally designed and computational-based metabolic engineering strategies, we developed a novel growth selection method which is built from a genome-wide CRISPR-dCas9 library and a FapR/FapO malonyl-CoA sensor native to Bacillus subtilis. This method couples growth rate with intracellular malonyl-CoA levels such that over many generations, yeast strains with improved malonyl-CoA are enriched within a population and can be identified through next generation sequencing (NGS).
Due to significant developments in CRISPR-Cas9, metabolic modeling and sequencing affordability, metabolic engineering can advance via many different approaches including rational, computational and genome-scale methods. In this work, we incorporate each of these strategies to improve the understanding of both the S. cerevisiae and K. marxianus platforms for the production of polyketides and to develop a framework for enhanced production of other acetyl- and malonyl-CoA based products.