Broad spectrum detection and “barcoding” of water pollutants by a genome-wide bacterial sensor array

•A genome-wide Escherichia coli reporter array was exposed to pollutants of diverse classes.•Each pollutant class induced a unique expression pattern of E. coli genes.•A machine learning model detected and classified pollutants with high accuracy.•Sensitivity was higher for pollutants defined by mode of action.

An approach for the rapid detection and classification of a broad spectrum of water pollutants, based on a genome-wide reporter bacterial live cell array, is proposed and demonstrated. An array of ca. 2000 Escherichia coli fluorescent transcriptional reporters was exposed to 25 toxic compounds as well as to unpolluted water, and its responses were recorded after 3 h. The 25 toxic compounds represented 5 pollutant classes: genotoxicants, metals, detergents, alcohols, and monoaromatic hydrocarbons. Identifying unique gene expression patterns, a nearest neighbour-based model detected pollutant presence and predicted class attribution with an estimated accuracy of 87%. Sensitivity and positive predictive values varied among classes, being higher for pollutant classes that were defined by mode of action than for those defined by structure only. Sensitivity for unpolluted water was 0.90 and the positive predictive value was 0.79. All pollutant classes induced the transcription of a statistically significant proportion of membrane associated genes; in addition, the sets of genes responsive to genotoxicants, detergents and alcohols were enriched with genes involved in DNA repair, iron utilization and the translation machinery, respectively. Following further development, a methodology of the type described herein may be suitable for integration in water monitoring schemes in conjunction with existing analytical and biological detection techniques.

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