Toxicant identification by a luminescent bacterial bioreporter panel: application of pattern classification...

Area of Science:

• Environmental microbiology
• Biosensor technology
• Computational toxicology

Background:

• Genetically engineered microorganisms (GEMs) offer potential for environmental monitoring by detecting toxic chemicals.
• Bacterial bioreporters using luxCDABE fusions report chemical presence via luminescence but cannot identify specific compounds.

Purpose of the Study:

• To develop a method for identifying specific toxic chemicals using a panel of bacterial bioreporters.
• To evaluate the effectiveness of machine learning algorithms in classifying toxicant-induced stress responses.

Main Methods:

• Five strains of Escherichia coli, engineered with stress-responsive promoters fused to luxCDABE, were exposed to five model toxicants and a control.
• Bacterial luminescence patterns were analyzed using Bayesian decision theory and a nearest-neighbor technique.
• The performance of machine learning classifiers was assessed in terms of accuracy, error rate, and false negatives.

Main Results:

• Each toxicant generated a unique luminescent "fingerprint" based on the activation of different promoters.
• Bayesian classifiers achieved high accuracy (error rate <3% at 95% confidence) in identifying toxicants within 30 minutes.
• The system demonstrated zero false negatives and performed well in both tap water and wastewater samples.

Conclusions:

• A panel of bacterial bioreporters combined with machine learning provides a robust method for identifying specific environmental toxicants.
• This approach significantly advances the development of whole-cell biosensor arrays for environmental monitoring.
• The developed pattern classification algorithms are crucial for integrating reporter cells into future biosensing applications.