Characterization of the Genetic Diversity of Extensively-Drug Resistant Mycobacterium tuberculosis Clinical Isolates from Pulmonary Tuberculosis Patients in Peru

Objectives The prevalence of drug-resistant TB in Shaanxi Province is higher than other areas. This study was aimed to investigate the genetic diversity and epidemiology of Mycobacterium tuberculosis clinical strains in Shaanxi Province, China. Methods From January to December 2016, a total of 298 Mycobacterium tuberculosis clinical isolates from smear-positive pulmonary tuberculosis patients were genotyped by Mcspoligotyping and 15-locus VNTR. Results We found that the Beijing family strains was the most prominent family(81.54%, 243/298). Other family strains included T family(9.06%, 27/298), U family(0.67%, 2/298), LAM9 family(0.34%, 1/298) and Manu family(0.34%, 1/298). The rates of multidrug-resistant (MDR) M.Tuberculosis, age, type of case and education between Beijing and non-Beijing family strains were not statistically different, while the distribution in the three different regions among these was statistically significant. VNTR results showed that strains were classified into 280 genotypes, and 33 (11.07%) strains could be grouped into 14 clusters. 11 of the 15-VNTR loci were highly or moderately discriminative according to the Hunter-Gaston discriminatory index. Conclusions We concluded that the Beijing family genotype was the most prevalent genotype and 15-locus VNTR typing might be suitable for genotyping of M. tuberculosis in Shaanxi Province. There was less association between Beijing family genotypes and drug resistance in our study area.

Download Full-text

Evaluation of Pyrosequencing for Detecting Extensively Drug-Resistant Mycobacterium tuberculosis among Clinical Isolates from Four High-Burden Countries

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.03614-14 ◽

2014 ◽

Vol 59 (1) ◽

pp. 414-420 ◽

Cited By ~ 20

Author(s):

Kanchan Ajbani ◽

Shou-Yean Grace Lin ◽

Camilla Rodrigues ◽

Duylinh Nguyen ◽

Francine Arroyo ◽

...

Keyword(s):

Drug Resistance ◽

Mycobacterium Tuberculosis ◽

The Philippines ◽

Drug Susceptibility Testing ◽

Clinical Isolates ◽

Drug Resistant ◽

Second Line ◽

Additional Advantage ◽

Content Type ◽

Extensively Drug Resistant

ABSTRACTReliable molecular diagnostics, which detect specific mutations associated with drug resistance, are promising technologies for the rapid identification and monitoring of drug resistance inMycobacterium tuberculosisisolates. Pyrosequencing (PSQ) has the ability to detect mutations associated with first- and second-line anti-tuberculosis (TB) drugs, with the additional advantage of being rapidly adaptable for the identification of new mutations. The aim of this project was to evaluate the performance of PSQ in predicting phenotypic drug resistance in multidrug- and extensively drug-resistant tuberculosis (M/XDR-TB) clinical isolates from India, South Africa, Moldova, and the Philippines. A total of 187 archived isolates were run through a PSQ assay in order to identifyM. tuberculosis(via the IS6110marker), and to detect mutations associated with M/XDR-TB within small stretches of nucleotides in selected loci. The molecular targets includedkatG, theinhApromoter and theahpC-oxyRintergenic region for isoniazid (INH) resistance; therpoBcore region for rifampin (RIF) resistance;gyrAfor fluoroquinolone (FQ) resistance; andrrsfor amikacin (AMK), capreomycin (CAP), and kanamycin (KAN) resistance. PSQ data were compared to phenotypic mycobacterial growth indicator tube (MGIT) 960 drug susceptibility testing results for performance analysis. The PSQ assay illustrated good sensitivity for the detection of resistance to INH (94%), RIF (96%), FQ (93%), AMK (84%), CAP (88%), and KAN (68%). The specificities of the assay were 96% for INH, 100% for RIF, FQ, AMK, and KAN, and 97% for CAP. PSQ is a highly efficient diagnostic tool that reveals specific nucleotide changes associated with resistance to the first- and second-line anti-TB drug medications. This methodology has the potential to be linked to mutation-specific clinical interpretation algorithms for rapid treatment decisions.

Download Full-text