A novel <i>Pyrococcus furiosus</i> argonaute-based method for rapid and sensitive detection of <i>Mycoplasma pneumoniae</i> and a macrolide-resistance-related mutation

<i>Mycoplasma pneumoniae</i> (MP) is a major cause of acute respiratory diseases. While macrolide resistance in MP has been recognized for decades, the recent surge in its prevalence has emerged as a growing clinical and public health concern. Rapid, sensitive, and specific detection of MP and macrolide-resistant <i>Mycoplasma pneumoniae</i> (MRMP) are crucial for preventing the spread of the disease. By leveraging the precise programmable recognition and cleavage capabilities of <i>Pyrococcus furiosus</i> Argonaute nuclease (PfAgo), we have established a short-PCR- based method that targets both the RepMP1 gene and the A2063G mutation in the 23S rRNA, enabling efficient detection of MP and MRMP. The high GC content around the 2063 site in the 23S rRNA complicates primer and PfAgo guide DNA (gDNA) design, posing challenges to the differentiation between mutant and wild-type strains. Through meticulous design and screening of PfAgo gDNA, along with optimization of reaction conditions, we achieved a high sensitivity, where the method detects as few as 0.5 copies per reaction for MP, 5 copies per reaction for the A2063G mutation, and 14 copies per reaction for wild-type MP. The entire detection procedure can be completed in 53 min using either extracted DNA or extraction-free oropharyngeal swab samples, with no cross-reaction with the tested eight common pathogens. This methodology has been successfully applied to the detection of MP and MRMP in 58 oropharyngeal swabs, providing robust support for epidemic control of MP and offering critical guidance for the appropriate treatment of MRMP cases.IMPORTANCEThe PCR-PfAgo system developed in this study establishes a rapid, sensitive, and specific detection platform that is crucial for early identification of <i>Mycoplasma pneumoniae</i> and the macrolide resistance-associated A2063G mutation, thereby guiding clinical treatment and controlling the spread of resistant strains. With advantages including enhanced sensitivity and superior specificity, the system accurately discriminates between resistant and sensitive strains, providing critical guidance for the timely and appropriate use of antibiotics in respiratory infections. This work demonstrates the feasibility of Argonaute protein-based systems for clinical diagnostics and provides a scalable framework for detecting other pathogens and resistance markers, laying the groundwork for future developments in multiplex detection, instrument-free readout, and prospective clinical validation.