Genetically-Encoded Biosensors for Yeast Cell Factory Optimization

 Francesca Ambri,¹ Michael K. Jensen,¹ Jay D. Keasling^1,2,3,4,5,*

¹ The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark
² Joint BioEnergy Institute, Emeryville, CA, USA
³ Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
⁴ Department of Chemical and Biomolecular Engineering & ⁵ Department of Bioengineering University of California, Berkeley, CA, USA

In the last two decades S. cerevisiae has become a commercial workhorse for producing fuels, chemicals, and pharmaceuticals from renewable feedstock (1). However, several factors limit the current adoption of yeast as a chassis for biobased production. One of the main bottlenecks encountered when attempting to engineer yeast for heterologous production of valuable molecules is that only very rarely do such products offer easily identifiable phenotypes that correlate with the productivity of the cell. As such, screening and selection of optimal cell design within a population of engineered variants can become a slow and costly procedure, ultimately hampering the identification of optimal cell designs. To balance the diversity by which genomes can be altered nowadays (1), metabolic engineers are in need of a platform that can couple input (ie. production; metabolite perturbations) with a high-throughput screenable output (ie. GFP; antibiotic resistance). Opposed to eukaryotes, prokaryotes retain an enormous pool of ligand-binding regulators harboring a huge potential reservoir of transcriptional regulators that can be tapped from for the development of biosensors in eukaryotes (2).

Recent studies have shown that performance of transplanted prokaryote transcription factor-based biosensors is largely dependent on the transcriptional level and principally on the operator positioning within the promoter region that lies upstream the reporter gene (3). To confirm and expand these findings we have systematically designed and characterized a test-bed library composed of 30 prokaryote transcriptional regulators for (i) optimal expression level, (ii) common optimal engineering of reporter promoters, and for (iii) enhanced dynamic output ranges. Taken together, our results aim to identify a universally applicable pipeline for optimal transplantation of prokaryotic regulators useful for high-throughput screening in eukaryotes.

1. Zhang, J., Jensen, M. K., & Keasling, J. D. (2015). Development of biosensors and their application in metabolic engineering. Current Opinion in Chemical Biology, 28, 1–8. http://doi.org/10.1016/j.cbpa.2015.05.013
2. Stanton, B. C., Nielsen, A. a K., Tamsir, A., Clancy, K., Peterson, T., & Voigt, C. a. (2014). Genomic mining of prokaryotic repressors for orthogonal logic gates. Nature Chemical Biology, 10(2), 99–105. http://doi.org/10.1038/nchembio.1411
3. Skjoedt, M. L., Snoek, T., Kildegaard, K. R., Arsovska, D., Eichenberger, M., Goedecke, T. J., Keasling, J. D. (2016). Engineering prokaryotic transcriptional activators as metabolite biosensors in yeast, 12(September). http://doi.org/10.1038/nchembio.2177