Anthropogenic activities have led to increased environmental contamination, resulting in a need for constant monitoring of dangerous analytes. Heavy metal contamination is of particular concern to human health as they are non-biodegradable and retained within ecological systems. Synthetic biology offers the ability to repurpose microbial genetic responses to heavy metals, enabling development of heavy metal microbial biosensors.

 

Utilising synthetic biology capabilities, de novo biosensing constructs have been developed for arsenic, cadmium, chromium, lead, mercury and zinc, for detection of heavy metal ions at levels deemed dangerous by the World Health Organisation to human drinking and ground water. Synthetic pathways have been developed consisting of a modular input combined with a detectable output. In these systems, an increase of heavy metal ions into the bacterial cell will result in the expression of a fluorescent protein allowing both qualitative and quantitative analysis. Initially, designs were focused on single-input/output constructs; however the development of multiplexed biosensors that can detect multiple inputs and/or provide multiple outputs will allow more dynamic sensing.

 

These biosensing constructs have been developed such that they are transferable between a broad range of environmentally relevant microbes including Shewanella oneidensis, Pseudomonas and Bacillus spp. To expand this range further, and mitigate the deleterious affect of the heavy metals, microbes isolated from known contaminated environments, with a higher tolerance towards heavy metals, are being utilised. As a result whole cell biosensors can be produced with sensing capabilities in a broader range of environments.

Using defined input and output modules along synthetic biology principles is allowing the targeted development of a range of microbial biosensors to analytes in the environment. Future developments of these sensors will allow detection of a wide range of contaminants in soil and aquatic environments leading to long-term monitoring devices allowing real-time analysis.