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

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.