A triple-helix molecular switch-based fluorescent aptasensor using Klenow fragment-assisted target recycling and...

MRSA is an antibiotic resistant bacterium that poses a significant threat to the environment and human health due to its bioaccumulation and potential widespread contamination. The prompt and accurate identification of MRSA is essential for enhancing environmental monitoring and clinical management. Here, we develop a triple-helix molecular switch (THMS) fluorescent aptasensor for the determination of MRSA using Klenow fragment (KF)-assisted target recycling and Ribonuclease H (Rnase H)-powered DNA walker cascade amplification. In this method, the target opens the THMS by specifically binding with the aptamer, resulting in the release of target/aptamer complex and DNA walker. KF then initiates the target recycling process via strand-displacement polymerization reaction under the assistance of carboxyfluorescein (FAM)-labeled primer and dNTPs, creating plenty of double-stranded DNA (dsDNA) products. These dsDNA products show low affinity to graphene oxide (GO) and generate strong fluorescence. This fluorescence is considerably significantly amplified in the presence of SYBR Green I (SGI), attributable to the synergistic interaction between dsDNA and SGI. In the interim, Rnase H drives the released DNA walker to automatically walk on the carboxylated graphene oxide surface by cleaving FAM-labeled RNA signal probe (SP), causing the FAMs to dissociate from the carboxylated graphene oxide (CGO). Therefore, fluorescent signal originating from the two reaction pathways can be detected at excitation/emission wavelengths of 480/514 nm. The target measured by this strategy demonstrates a broad linear working range from 10² colony-forming units (CFU)/mL to 10⁷ CFU/mL, with a detection limit (LOD) of 15 CFU/mL. Moreover, this method performs well in milk and pus sample analysis. These results reveal that this aptasensor is highly specific and sensitive for detecting MRSA and is endowed with good potential for food monitoring and clinical diagnosis applications.