(152d) Optimization of a Tunable RNAi Screening Approach for Systems Engineering of Complex Phenotypes in Yeast

Authors: 
Crook, N., The University of Texas at Austin
Sun, J., University of Illinois at Urbana-Champaign
Schmitz, A., The University of Texas at Austin
Alper, H., The University of Texas at Austin
Complex phenotypes are notoriously difficult to rationally engineer as they result from the combined activities of multiple gene products, the identities and roles of which are often unknown. However, most current methods for non-rational strain improvement suffer from a variety of limitations, including laborious transfer of beneficial mutations to alternative strains, restricted (often binary) exploration of expression space, requirement for detailed knowledge of a strainâ??s genetics, or inefficiency in eukaryotic hosts. To address these issues, we synthetically optimized RNA interference (RNAi) in yeast to enable genome-wide screening of downregulation space for effectors of complex phenotypes, and demonstrated this approach by identifying novel targets for improved 1-butanol and isobutanol tolerance. First, we optimized the efficiency and tunability of RNAi library screening in yeast, ultimately enabling downregulation efficiencies from 0% to 94% to be attained through tuning dsRNA cassette design and expression level. Using this system, we generated a genome-wide RNAi library from yeast cDNA which collectively spanned a wide range of downregulation capacities. By screening this library in inhibitory concentrations of isobutanol, we identified key regulators of isobutanol tolerance in a single round, with downregulation of recovered genes conferring up to 64% increased growth rate in 12 g/L isobutanol. We further found, through two iterative rounds of screening this RNAi library, that the combined downregulation of two genes improves growth rate up to 3100% in 10 g/L 1-butanol. For both compounds, the best-performing hits downregulate multiple homologous genes and elicit phenotypes which are fundamentally different from that of the corresponding gene knockout, thus validating the utility of the RNAi screening approach for yeast strain optimization. Collectively, this work improves the tunability of RNAi in yeast, uncovers novel effectors of butanol tolerance, and demonstrates a powerful approach for improving complex phenotypes in yeast.