Transcriptional Biomarker Discovery Towards Building A Load Stress Reporting System for Engineered Escherichia coli Strains

Abstract

Foreign proteins are produced by inserting synthetic constructs into host bacteria in biotechnology applications. This process can cause resource competition between synthetic circuits and host cells, placing a metabolic burden on the host cells which may result load stress and detrimental physiological changes. Consequently, the host bacteria can experience slow growth, while the synthetic system may suffer from suboptimal function and reduced productivity. To address this issue, we developed machine learning strategies to select a minimal number of genes that could serve as biomarkers for the design of load stress reporters. We identified pairs of biomarkers that showed discriminative capacity to detect the load stress states induced in 41 engineered E. coli strains. These biomarker genes are mainly involved in Envelope stress response, Ion transport, Energy production and conversion.