Genetically-Encoded Biosensors for Branched-Chain Amino Acid Metabolism and Branched-Chain Higher Alcohol Production

Abstract

Metabolic engineering is the process of manipulating organism metabolism to control or increase chemical production via biosynthetic pathways. One pathway of interest is yeast branched-chain amino acid (BCAA) pathway, with isobutanol and isopentanol as two valuable degradation products. These are considered branchedchain higher alcohols (BCHAs) and can serve as ”next-generation” biofuels to aid the transition from fossil fuels to more sustainable energy sources. The BCAA pathway has been engineered in Saccharomyces cerevisiae yeast strains to enhance BCHA production through enhanced expression of enzymes encoded by ILV5, ILV2, and ILV3. These enzymes enable the conversion of pyruvate to isobutanol precursor α- KIV (α-ketoisovalerate) and isopentanol precursor α-KIC (α-ketoisocarpoate). With the development of new technology to create vast DNA libraries, biosensors serve as tools to aid in screening for the highest producers.

A previously reported biosensor in the Avalos Laboratory harnesses a yeast transcription factor’s (Leu3p) response to BCAA intermediate 2-isopropylmalate (α-IPM) as an indicator for BCHA production. This biosensor was published as a successful tool for predicting lower and higher BCHA producers, based on a downstream fluorescent reporter gene. However, the previously designed biosensor faced limitations in terms of the dynamic range for which the biosensor was able to output a correlated fluorescence with varied α-IPM amounts, likely due to potential cross-talk with other yeast transcription factors. To overcome these limitations, improved biosensors for isopentanol and isobutanol were developed. These are based on an engineered Leu3p that uses a DNA binding domain and binding site from bacteria. Overcoming these challenges enables the advancement of cybergenetic control of BCHA processes— whereby synthetic biologists have in vivo regulation of biomolecular systems with control system feedback. The combination of both computational and genetic tools allows for characterization of biosensors to further advance BCHA production.