Rapid detection of mexX in Pseudomonas aeruginosa based on CRISPR-Cas13a coupled with recombinase polymerase amplification

Front Microbiol. 2024 Jan 31:15:1341179. doi: 10.3389/fmicb.2024.1341179. eCollection 2024.

Abstract

The principal pathogen responsible for chronic urinary tract infections, immunocompromised hosts, and cystic fibrosis patients is Pseudomonas aeruginosa, which is difficult to eradicate. Due to the extensive use of antibiotics, multidrug-resistant P. aeruginosa has evolved, complicating clinical therapy. Therefore, a rapid and efficient approach for detecting P. aeruginosa strains and their resistance genes is necessary for early clinical diagnosis and appropriate treatment. This study combines recombinase polymerase amplification (RPA) and clustered regularly interspaced short palindromic repeats-association protein 13a (CRISPR-Cas13a) to establish a one-tube and two-step reaction systems for detecting the mexX gene in P. aeruginosa. The test times for one-tube and two-step RPA-Cas13a methods were 5 and 40 min (including a 30 min RPA amplification reaction), respectively. Both methods outperform Quantitative Real-time Polymerase Chain Reactions (qRT-PCR) and traditional PCR. The limit of detection (LoD) of P. aeruginosa genome in one-tube and two-step RPA-Cas13a is 10 aM and 1 aM, respectively. Meanwhile, the designed primers have a high specificity for P. aeruginosa mexX gene. These two methods were also verified with actual samples isolated from industrial settings and demonstrated great accuracy. Furthermore, the results of the two-step RPA-Cas13a assay could also be visualized using a commercial lateral flow dipstick with a LoD of 10 fM, which is a useful adjunt to the gold-standard qRT-PCR assay in field detection. Taken together, the procedure developed in this study using RPA and CRISPR-Cas13a provides a simple and fast way for detecting resistance genes.

Keywords: Crispr-cas13a; MexX; Pseudomonas aeruginosa; detection; recombinase polymerase amplification.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the Research and Development Plan in Key Areas of Guangdong Province (No. 2022B1111040002), Key-Area Research and Development Program of Guangdong Province (Nos. 2020B020226008, 2022B0202040002, and 2018B020206001), Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515030059), Natural Science Foundation of Guangdong Province (No. 2023A1515012057), and GDAS’ Project of Science and Technology Development (No. 2022GDASZH-2022010202).