Optimization of Heavy Metal Sensors Based on Transcription Factors and Cell-Free Expression Systems.

ABSTRACT

Many bacterial mechanisms for highly specific and sensitive detection of heavy metals and other hazards have been reengineered to serve as sensors. In some cases, these sensors have been implemented in cell-free expression systems, enabling easier design optimization and deployment in low-resource settings through lyophilization. Here, we apply the advantages of cell-free expression systems to optimize sensors based on three separate bacterial response mechanisms for arsenic, cadmium, and mercury. We achieved detection limits below the World Health Organization-recommended levels for arsenic and mercury and below the short-term US Military Exposure Guideline levels for all three. The optimization of each sensor was approached differently, leading to observations useful for the development of future sensors (1) there can be a strong dependence of specificity on the particular cell-free expression system used, (2) tuning of relative concentrations of the sensing and reporter elements improves sensitivity, and (3) sensor performance can vary significantly with linear vs plasmid DNA. In addition, we show that simply combining DNA for the three sensors into a single reaction enables detection of each target heavy metal without any further optimization. This combined approach could lead to sensors that detect a range of hazards at once, such as a panel of water contaminants or all known variants of a target virus. For low-resource settings, such "all-hazard" sensors in a cheap, easy-to-use format could have high utility.