Fatty acid responsive biosensors in Saccharomyces cerevisiae for accelerating cell factory development

Abstract

The increasing synergy between metabolic engineering and synthetic biology has made it possible to accelerate the development of cell factories. However, the process of constructing cell factories for production of desired compounds is time-consuming. To make the process more efficient, high-throughput (HT) screening approaches need to be established. Genetically encoded biosensors have great potential as HT screening tools when combined with other technologies, such as FACS (fluorescence-activated cell sorting). In this study, transcription factor (TF) – based biosensors responsive to fatty acids in Saccharomyces cerevisiae were constructed. The first biosensor is based on the OLE1 promoter from S. cerevisiae, and it was shown here to be sensitivity to saturated fatty acids (SFA). Additional biosensors constructed in this study are based on TFs from Escherichia coli, which includes FadR (Fatty acid degradation repressor) and FabR (Fatty acid biosynthesis repressor). Here, the functionality for both TFs was observed as a decrease in fluorescence was clearly seen, which indicates that the TFs have bound to their BSs in the promoter, thus reducing its activity. The sensitivity of FabR towards SFA was also clearly shown using a modified background strains that produces higher levels of SFA compared to the wild-type (WT) strain. The sensitivity was observed by an increase in fluorescence, which implies that FabR was released from its BSs. Further on, using the same modified background strain it was shown that FadR is less responsive to SFAs as the fluorescence decreased, demonstrating that FadR had bound more tightly to its BSs. The ultimate goal of this study was to combine the bacterial based biosensors as one biosensor and use it for determining the specific amounts of SFAs and unsaturated fatty acids (UFA). However, this was not fully accomplished due to some obstacles, such as low fluorescence output. Although the design needs to be further optimized, it was shown here that the biosensors based on pOLE1, FabR and FadR are good candidates for this purpose.