Encoding Genetic Circuits with DNA Barcodes Paves the Way for Machine Learning-Assisted Metabolite Biosensor Response Curve Profiling in Yeast

作者：Yikang Zhou, Yaomeng Yuan, Yinan Wu, Lu Li, Aysha Jameel, Xin‐Hui Xing, Chong Zhang · 发表于：ACS Synthetic Biology · 年份：2022 · DOI：10.1021/acssynbio.1c00595 · 被引用次数：26 · 研究领域：Gene Regulatory Network Analysis、Viral Infectious Diseases and Gene Expression in Insects、Microbial Metabolic Engineering and Bioproduction

Genetically encoded biosensors are valuable tools used in the precise engineering of metabolism. Although a large number of biosensors have been developed, the fine-tuning of their dose–response curves, which promotes the applications of biosensors in various scenarios, still remains challenging. To address this issue, we leverage a DNA trackable assembly method and fluorescence-activated cell sorting coupled with next-generation sequencing (FACS-seq) technology to set up a novel workflow for construction and comprehensive characterization of thousands of biosensors in a massively parallel manner. An FapR- fapO- based malonyl-CoA biosensor was used as proof of concept to construct a trackable combinatorial library, containing 5184 combinations with 6 levels of transcription factor dosage, 4 different operator positions, and 216 possible upstream enhancer sequence (UAS) designs. By applying the FACS-seq technique, the response curves of 2632 biosensors out of 5184 combinations were successfully characterized to provide large-scale genotype–phenotype association data of the designed biosensors. Finally, machine-learning algorithms were applied to predict the genotype–phenotype relationships of the uncharacterized combinations to generate a panoramic scanning map of the combinatorial space. With the assistance of our novel workflow, a malonyl-CoA biosensor with the largest dynamic response range was successfully obtained. Moreover, feature importance analysis revealed that the r...