Novel biosensors for optimizing yeast cell factories

Florian David, Verena Siewers, Jens Nielsen

Systems & Synthetic Biology Group, Department of Chemical and Biological Engineering,
Chalmers University of Technology, Sweden
The development of efficient cell factories is fundamental for establishing a biosustainable economy. Inverse metabolic engineering is used to uncover new targets by high-throughput screening of versatile genetic libraries. It relies on the selection of a high-producing phenotype from a diverse, previously generated, population of cells. While techniques for generating these diverse cell populations are well established, inverse metabolic engineering suffers from the lack of sufficiently sensitive, selective high-throughput measurement technologies to screen efficiently for high producers. In order to generate a multi-dimensional sensor system, we are engineering new transcription factor based sensors for key metabolites and combine them with fluorescent protein reporters. This novel technology is allowing a direct dynamic feedback of the production of the target compound. Both upstream and downstream optimizations of the particular metabolic pathways become possible.
We are aiming at optimizing the production of fatty acid derived compounds in Saccharomyces cerevisiae. Intracellular biosensors sensing limiting key intermediates like Malonyl-CoA and Acyl-CoA are used for multi-dimensional high producer screening. The used transcription factor based sensors originate from bacterial systems. They were characterized in terms of sensitivity and quantitative responses to extracellular and intracellular fatty acids. Yeast cells equipped with the sensor system were transformed with a cDNA library thereby creating a phenotypic diversity of cells. After sorting and enriching the cells by Fluorescent Activated Cell Sorting (FACS) cell populations were extensively characterized by PCR and followed up high throughput sequencing. The information gained will be applied to redesign naïve strains for verification of target genes and further knowledge based rational strain improvement.