Demonstrates a novel biosensing method for PCB126 in human serum, highlighting innovative biomonitoring efficiency.
Polychlorinated biphenyl 126 (PCB126), a potent dioxin-like pollutant classified as a Group 1 human carcinogen by IARC, is associated with cardiovascular impairments, developmental and reproductive toxicity, endocrine disruption, and multi-system organ damage. Conventional detection methods (e.g., GC-MS, GC-HRMS) rely on sophisticated instrumentation and centralized laboratories, and they only quantify the parent compound without providing metabolism-informed exposure data-a critical gap for accurate health risk assessment. To address this, we report a metabolism-guided DNA nanodevice that integrates CYP1A1-driven biocatalytic activation of PCB126 with AAG-specific excision of the resulting quinone-DNA adducts and G-quadruplex/hemin-catalyzed signal amplification on a structurally defined DNA tetrahedral nanostructure. Unlike conventional DNAzyme sensors that depend on direct molecular recognition, this work harnesses CYP1A1 metabolic selectivity as the primary recognition element, transducing the actual genotoxic process-quinone-DNA adduct formation and AAG-mediated excision-rather than the mere physical presence of the pollutant, into a quantifiable colorimetric signal. Following optimization (2 h metabolic activation, 60 min enzymatic excision), the system achieves a detection limit of 0.14 pM and a linear range of 0.1–100 pM. The sensor exhibits exceptional specificity, showing a 36- to 64-fold higher response to PCB126 than to structurally similar congeners, and yields satisfactory recoveries (93.1–107.9%) in spiked human serum. Using a smartphone for image capture and Spotxel® Reader for quantification, the platform enables point-of-care amenable (POC-amenable) biomonitoring without centralized instrumentation. This metabolism-actuated, DNA nanostructure-based biosensing strategy provides a sensitive, biologically relevant, and deployable solution for PCB126 surveillance and establishes a versatile framework for next-generation environmentally informed biosensors.
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Zhao et al. (2026) studied this question.
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