Literature DB >> 30333128

Rapid Identification of Clinically Relevant Mycobacterium Species by Multicolor Melting Curve Analysis.

Ye Xu1,2,3, Bin Liang1,2,3, Chen Du1,2, Xueshan Tian1,2, Xingshan Cai4, Yanjie Hou5, Hui Li6, Rongrong Zheng7, Junlian Li8, Yuqin Liu5, Kaili Wang5, Muhammad Ammar Athar1, Yaoju Tan9, Qingge Li10,2,3.   

Abstract

The sustained increase in the incidence of nontuberculous mycobacterial (NTM) infection and the difficulty in distinguishing these infections from tuberculosis constitute an urgent need for NTM species-level identification. The MeltPro Myco assay is the first diagnostic system that identifies 19 clinically relevant mycobacteria in a single reaction based on multicolor melting curve analysis run on a real-time PCR platform. The assay was comprehensively evaluated regarding its analytical and clinical performances. The MeltPro Myco assay accurately identified 51 reference mycobacterial strains to the species/genus level and showed no cross-reactivity with 16 nonmycobacterial strains. The limit of detection was 300 bacilli/ml, and 1% of the minor species was detected in the case of mixed infections. Clinical studies using 1,163 isolates collected from five geographically distinct health care units showed that the MeltPro Myco assay correctly identified 1,159 (99.7%) samples. Further testing with 94 smear-positive sputum samples showed that all samples were correctly identified. Additionally, the entire assay can be performed within 3 h. The results of this study confirmed the efficacy of this assay in the reliable identification of mycobacteria, suggesting that it might potentially be used as a screening tool in regions endemic for tuberculosis.
Copyright © 2019 American Society for Microbiology.

Entities:  

Keywords:  melting curve analysis; nontuberculous mycobacteria; real-time PCR; species identification

Mesh:

Substances:

Year:  2019        PMID: 30333128      PMCID: PMC6322455          DOI: 10.1128/JCM.01096-18

Source DB:  PubMed          Journal:  J Clin Microbiol        ISSN: 0095-1137            Impact factor:   5.948


  35 in total

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Journal:  Science       Date:  2016-11-11       Impact factor: 47.728

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