2011•Journal of Clinical MicrobiologyOpen access

Multiplex Real-Time PCR Melting Curve Assay To Detect Drug-Resistant Mutations of Mycobacterium tuberculosis

Tao Luo, Lili Jiang, Wei‐Ming Sun, Guoliang Fu, Jian Mei, Qian Gao

Open full text 45 citations

Abstract

Early diagnosis of drug-resistant Mycobacterium tuberculosis is urgently needed to optimize treatment regimens and to prevent the transmission of resistant strains. Real-time PCR assays have been developed to detect drug resistance rapidly, but none of them have been widely applied due to their complexity, high cost, or requirement for advanced instruments. In this study, we developed a real-time PCR method based on melting curve analysis of dually labeled probes. Six probes targeting the rpoB 81-bp core region, katG315, the inhA promoter, the ahpC promoter, and embB306 were designed and validated with clinical isolates. First, 10 multidrug-resistant (MDR) strains with a wide mutation spectrum were used to analyze the melting temperature (T(m)) deviations of different mutations by single real-time PCR. All mutations can be detected by significant T(m) reductions compared to the wild type. Then, three duplex real-time PCRs, with two probes in each, were developed to detect mutations in 158 MDR isolates. Comparison of the results with the sequencing data showed that all mutations covered by the six probes were detected with 100% sensitivity and 100% specificity. Our method provided a new way to rapidly detect drug-resistant mutations in M. tuberculosis. Compared to other real-time PCR methods, we use fewer probes, which are labeled with the same fluorophore, guaranteeing that this assay can be used for detection in a single fluorescent channel or can be run on single-channel instruments. In conclusion, we have developed a widely applicable real-time PCR assay to detect drug-resistant mutations in M. tuberculosis.

About this research paper

What this paper is about

Early diagnosis of drug-resistant Mycobacterium tuberculosis is urgently needed to optimize treatment regimens and to prevent the transmission of resistant strains. Real-time PCR assays have been developed to detect drug resistance rapidly, but none of them have been widely applied due to their complexity, high cost, or requirement for advanced instruments. In this study, we developed a real-time PCR method based on melting curve analysis of dually labeled probes. Six probes targeting the rpoB 81-bp core region, katG315, the inhA promoter, the ahpC promoter, and embB306 were designed and validated with clinical isolates. First, 10 multidrug-resistant (MDR) strains with a wide mutation spectrum were used to analyze the melting temperature (T(m)) deviations of different mutations by single real-time PCR. All mutations can be detected by significant T(m) reductions compared to the wild type. Then, three duplex real-time PCRs, with two probes in each, were developed to detect mutations in 158 MDR isolates. Comparison of the results with the sequencing data showed that all mutations covered by the six probes were detected with 100% sensitivity and 100% specificity. Our method provided a new way to rapidly detect drug-resistant mutations in M. tuberculosis. Compared to other real-time PCR methods, we use fewer probes, which are labeled with the same fluorophore, guaranteeing that this assay can be used for detection in a single fluorescent channel or can be run on single-channel instruments. In conclusion, we have developed a widely applicable real-time PCR assay to detect drug-resistant mutations in M. tuberculosis.

Why it matters

OpenAlex reports 45 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Early diagnosis of drug-resistant Mycobacterium tuberculosis is urgently needed to optimize treatment regimens and to prevent the transmission of resistant strains. Real-time PCR assays have been developed to detect drug resistance rapidly, but none of them have been widely applied due to their complexity, high cost, or requirement for advanced instruments. In this study, we developed a real-time PCR method based on melting curve analysis of dually labeled probes. Six probes targeting the rpoB 81-bp core region, katG315, the inhA promoter, the ahpC promoter, and embB306 were designed and validated with clinical isolates. First, 10 multidrug-resistant (MDR) strains with a wide mutation spectrum were used to analyze the melting temperature (T(m)) deviations of different mutations by single real-time PCR. All mutations can be detected by significant T(m) reductions compared to the wild type. Then, three duplex real-time PCRs, with two probes in each, were developed to detect mutations in 158 MDR isolates. Comparison of the results with the sequencing data showed that all mutations covered by the six probes were detected with 100% sensitivity and 100% specificity. Our method provided a new way to rapidly detect drug-resistant mutations in M. tuberculosis. Compared to other real-time PCR methods, we use fewer probes, which are labeled with the same fluorophore, guaranteeing that this assay can be used for detection in a single fluorescent channel or can be run on single-channel instruments. In conclusion, we have developed a widely applicable real-time PCR assay to detect drug-resistant mutations in M. tuberculosis.

Key concepts: Melting curve analysis, rpoB, Multiplex, Mycobacterium tuberculosis, Biology, INHA, Real-time polymerase chain reaction, Polymerase chain reaction

Related papers

Back to paper searchBrowse research topicsOriginal source
Multiplex Real-Time PCR Melting Curve Assay To Detect Drug-Resistant Mutations of Mycobacterium tuberculosis — Research Paper | ScholarLens