CRISPR based technologies for high-throughput metabolic engineering of yeast

Doctoral thesis, 2022

The yeast Saccharomyces cerevisiae is a commonly used microorganism for metabolic engineering applications since it has a very well-studied metabolism and it can be easily genetically modified. This led to its use as a cell factory for the production of a wide variety of industrially relevant chemicals such as fuels, cosmetics and food additives. However, there is always space for improvement of the productivity metrics of the products of interest. Some of the challenges that have to be addressed are the efficient rewiring of the metabolism for improvement of the metabolic fluxes and the performance improvement of the enzymes of interest. The development of metabolite biosensors that can connect the levels of a metabolite of interest to a readable output, has allowed the use of high-throughput screening methods in yeast such as Fluorescence Activated Cell Sorting (FACS) to identify cells with higher metabolite levels. Moreover, the CRISPR/Cas9 technology that has emerged the last decade has not only sped up the introduction of genomic modifications for strain engineering, but it has been further developed for other purposes such as gene expression fine tuning or base editing. Those applications of CRISPR/Cas9 can be coupled to high-throughput screening methods and can give new insights into metabolic engineering challenges.

This study aimed to develop various CRISPR/Cas9-based tools in yeast along with their implementation in high-throughput setups for solving metabolic engineering challenges. At the same time, a modular cloning system for CRISPR/Cas9-based tools was developed for making the molecular cloning of those tools more fast, flexible and simple to use. Hyperactive variants of cytidine and adenine deaminases were explored for the construction of broad range CRISPR base editors in yeast for in vivo mutagenesis. Also, gRNA libraries were used in two different setups: with transcriptional activator dCas9-VPR for transcription optimization and with broad range base editors for directed evolution of a gene of choice.

In summary, this work explores some of the possibilities that CRISPR tools can offer when combined with gRNA libraries and at the same time it aims to contribute to the systematization of the experimental workflow for CRISPR applications in yeast.