Metabolite biosensors for cell factory development
This thesis focuses on different aspects of utilizing and engineering metabolite-responsive transcription factor-based biosensors for facilitating the development of Saccharomyces cerevisiae as a cell factory. To that end, we improved the dynamic range of a malonyl-CoA-responsive biosensor by i) evaluating different binding site locations of the bacterial transcription factor FapR within different yeast promoters and by ii) using a chimeric transcription factor based on a native repressor system from S. cerevisiae. Furthermore, we suggest the possibility of using the CRISPR (Clustered Regulatory Interspaced Short Palindromic Repeats)/Cas9 system to facilitate biosensor development by guiding binding site positioning. We also employed an acyl-CoA-responsive biosensor based on the bacterial transcription factor FadR to screen for genes boosting the fatty acyl-CoA levels, which are precursors for industrially relevant compounds such as fatty alcohols. The possibility of developing fatty acid-responsive biosensors based on other transcription factors, including the endogenous transcription factor Mga2, has also been addressed. Finally, we looked into the potential of developing an alkane-responsive biosensor based on a system from Yarrowia lipolytica. Overall, this thesis provides answers, discussions and potential future directions on using and engineering metabolite biosensors for cell factory development.
metabolite-responsive transcription factor-based biosensors
Chalmers, Biologi och bioteknik, Systembiologi
The challenge, however, lies in creating robust cell factories that allow for cost-competitive production. Despite great advances in biological engineering, it still takes immense amount of research, effort and resources to develop a cell factory that is suitable for industrial production. This is mainly due to the complexity of living systems and their highly dynamic metabolism, making biological engineering unpredictive and trial-and-error part of the everyday process. Therefore, instead of targeting specific parts of the genome, researchers are moving more towards approaches where many different combinations and genetic variants are randomly created simultaneously. However, to be able to analyze all different variants, it is necessary to have tools that allows for efficient analysis. Genetically encoded biosensors, also known as metabolite biosensors, are promising tools for efficiently analysing a large number of different cells. Ideally, metabolite biosensors would transform any sought after cellular changes to clear output signals, such as a fluorescent signal or improved growth fitness, enabling fast identification of promising cell factories using less time and resources. In reality, however, developing and optimizing a biosensor for a specific purpose is challenging.
The main work in this thesis has been focused on developing metabolite-responsive transcription factor-based biosensors, specifically for fatty molecules such as fatty acids and alkanes as these are industrially relevant compounds. Overall, this thesis highlights both the challenges and potentials of biosensors.
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4903
Opponent: Michael Krogh Jensen