Fine-tuning the stress response of Saccharomyces cerevisiae using CRISPR interference technology
Conference poster, 2019
Efficient biochemical conversion of renewable carbon sources is crucial for the transition into an entirely renewable energy system and a resource-efficient society. However, the substitution of fossil-based chemicals with renewable biochemicals requires the production to be significantly more efficient and price competitive. Remediation of several technical bottlenecks is needed before this can be accomplished. Production of second-generation biochemicals (made from lignocellulosic biomass) is challenging due to presence of inhibitors in lignocellulosic hydrolysates. Weak acids, furans and phenolic compounds that are formed or released during hydrolysis of biomass are toxic for the producing cells and leads to suboptimal yield and productivity obtained during fermentation. In this project, we are trying to fine tune the expression of stress related genes to boost the stress tolerance in Saccharomyces cerevisiae using the CRISPR interference (CRISPRi) technology. CRISPRi is a genetic perturbation technique that allows sequence-specific repression or activation of gene expression, achieved by a catalytically inactive Cas9 protein fused to a repressor or activator, which can be targeted to any genetic loci using an sgRNA. Using a high-throughput yeast transformation method developed in our laboratory, we are generating a CRISPRi strain library. Each strain in this library has altered regulation for at-least one stress related gene. Next, high-throughput phenotypic evaluation of this library is performed by growing the strains under the exposure of inhibitors relevant to lignocellulosic hydrolysates. Here, we will demonstrate our primary CRISPRi library data. Further, we will explain the high-throughput methodologies for generating the CRISPRi mutants and to study their hydrolysate tolerance, adaptation and ethanol production capacity at microscale. In future, we will perform transcriptomics analysis of the most tolerant mutants to link superior phenotypes to the transcriptomic landscape. Subsequently, this novel information will be used as a resource to accelerate the design-build-test-learn cycle used for developing industrial yeast strains for efficient conversion of lignocellulosic hydrolysate.
high-throughput phenotypic evaluation