New genetically-encoded biosensors for yeast cell factory optimization

Francesca Ambri

Research output: Book/ReportPh.D. thesisResearch

41 Downloads (Pure)

Abstract

The baker’s yeast Saccharomyces cerevisiae was the first eukaryotic organism to have its genome completely sequenced. Its extensive characterization as a model organism naturally led to its adoption as industrial workhorse in cell factories for the biosustainable production of valueadded compounds. The development of cell factories is notoriously laborious and predominantly dependent on costly and time-consuming screening techniques for the identification of improved
yeast strains. The establishment of biosensor systems as high-throughput screening tool is auspicious to advance cell factories optimization. This thesis embodies some of the research I have partaken during my Ph.D. towards both the
optimization of biosensors’ response curve, and the characterization of universal patterns for a rapid implementation of novel prokaryote biosensor systems in yeast. The biosensors discussed in this thesis are prokaryotic mono-component regulatory systems in which an allosteric transcription factor (aTF), by binding the ligand (input) through its effector binding domain (EBD), triggers either its release or binding of specific DNA sequences (operators) to perturb transcription of a target gene (output). The more detailed presentation of the system components and mechanism is included in the first work presented in this thesis, and, using BenM as example, serves as an introductory guide to the experimental design for biosensor systems implementation in yeast. The work presented in the second chapter of this thesis, aiming at investigating the repercussions that different operator placements along the sequence of a defined promoter have on gene transcription, resulted in the identification of potential common designs and in the implementation of 2 novel biosensors. The VanR repressor displayed an increased reporter gene expression of 9-fold, and the activator PcaQ displayed a remarkable 20-fold increase. The study presented in the third chapter of this thesis successfully focused on using FACSbased toggle selection to enrich a BenM EBD mutated population for the isolation of biosensor variants with user-defined response curves and specificity. Combined, these results led to conceptualize powerful future applications for the use of biosensor systems established in this thesis in assisting in vivo evolution strategies, also described in this thesis.
Original languageEnglish
Place of PublicationKgs. Lyngby
PublisherTechnical University of Denmark
Number of pages140
Publication statusPublished - 2019

Projects

New Genetically-Encoded Biosensors for Yeast Cell Factory Optimization

Ambri, F., Jensen, M. K., Keasling, J., Herrgard, M., Jensen, N. B. & Mey, M. D.

Offentlig finansiering

01/11/201511/12/2019

Project: PhD

Cite this

Ambri, F. (2019). New genetically-encoded biosensors for yeast cell factory optimization. Technical University of Denmark.