Abstract
Heavy metal contamination remains a persistent threat to water quality and ecological safety. Although electroactive microorganism (EAM)-based sensing technologies have shown promise for toxicity monitoring, their practical application is constrained by limited sensitivity. Here, we developed a strategy to improve monitoring performance by identifying and exploiting heavy metal-sensitive EAMs from mixed microbial communities. We successfully identified and isolated Acinetobacter baylyi C8 as a highly sensitive strain by integrating metagenomic analysis of mixed EAM communities under metal stress with electrochemical plate-based isolation. In bioelectrochemical sensing systems, C8 showed current-inhibition responses to eight representative heavy metals that were approximately one order of magnitude higher than those of mixed EAMs. For Cd2+ and Pb2+, good linear responses were obtained, with detection limits of 0.0157 and 0.0205 mg/L, respectively. C8 also responded consistently to binary and ternary heavy metal mixtures, while common coexisting ions caused little interference with Cd2+ and Pb2+ responses. Stable performance was maintained in drinking water, river water, and lake water, with deviations below 5 %. Further analyses showed that the high sensitivity of C8 was associated with low protein content in extracellular polymeric substances (EPS), greater intracellular metal accumulation, and stronger oxidative, metabolic, and electron-transfer disturbances. Overall, this study provides a practical route for identifying heavy metal-sensitive EAMs from mixed communities and highlights their potential for improving bioelectrochemical monitoring of heavy metal toxicity in water environments.