Work

Adaptive laboratory evolution enables optimization of arsenite whole-cell biosensors

Year: 2026

Type: article

Abstract: Whole-cell microbial biosensors provide a portable and cost-effective platform for monitoring toxic metal(loid)s in the environment. Numerous microbial metal sensors have already achieved high analytical sensitivity and strong reporter outputs. Nevertheless, conventional strategies for discovering and further optimizing sensing elements often depend on the construction and screening of mutant libraries. Here, we developed an efficient optimization strategy that uses adaptive laboratory evolution (ALE) to generate arsenite-resistant Escherichia coli ( E. coli ). Serial passage of E. coli MC4100 under progressively increasing arsenite stress yielded five independently evolved resistant strains (AsR1–AsR5). Whole-genome resequencing identified mutations in the arsenite-permeable channel gene glpF and in the ars operon promoter (P ars ). Mechanistic analyses showed that GlpF overexpression increased intracellular arsenic accumulation but did not improve reporter fluorescence. In contrast, the promoter substitutions − 30G > T and − 54T > C, located within the − 10 element and immediately upstream of the − 35 element, respectively, markedly increased downstream transcription. Introduction of the mutant promoters into biosensor plasmids generated the reporter strains G30T and T54C, which exhibited up to a 6-fold increase in induced fluorescence together with reduced basal leakage relative to the wild-type sensor. Site-saturation mutagenesis further confirmed that G30T and T54C were among the highest-output variants in the tested library. Both optimized biosensors retained high specificity for arsenite over other metal(loid) ions, showed a linear response from 0.1 to 16 µM, and achieved limits of detection of 66.7 and 57.9 nM, respectively. These results support ALE as a candidate-discovery strategy for identifying adaptive regulatory variants that can subsequently be reconstructed in reporter circuits and evaluated for biosensor optimization.

Source: AMB Express

DOI: 10.1186/s13568-026-02135-0