A quantitative and efficient approach to select MIRU–VNTR loci based on accumulation of the percentage differences of strains for discriminating divergent Mycobacterium tuberculosis sublineages

Abstract

Although several optimal mycobacterial interspersed repetitive units–variable number tandem repeat (MIRU–VNTR) loci have been suggested for genotyping homogenous Mycobacterium tuberculosis, including the Beijing genotype, a more efficient and convenient selection strategy for identifying optimal VNTR loci is needed. Here 281 M. tuberculosis isolates were analyzed. Beijing genotype and non-Beijing genotypes were identified, as well as Beijing sublineages, according to single nucleotide polymorphisms. A total of 22 MIRU–VNTR loci were used for genotyping. To efficiently select optimal MIRU–VNTR loci, we established accumulations of percentage differences (APDs) between the strains among the different genotypes. In addition, we constructed a minimum spanning tree for clustering analysis of the VNTR profiles. Our findings showed that eight MIRU–VNTR loci displayed disparities in h values of ≥0.2 between the Beijing genotype and non-Beijing genotype isolates. To efficiently discriminate Beijing and non-Beijing genotypes, an optimal VNTR set was established by adding loci with APDs ranging from 87.2% to 58.8%, resulting in the construction of a nine-locus set. We also found that QUB11a is a powerful locus for separating ST10s (including ST10, STF and STCH1) and ST22s (including ST22 and ST8) strains, whereas a combination of QUB11a, QUB4156, QUB18, Mtub21 and QUB26 could efficiently discriminate Beijing sublineages. Our findings suggested that two nine-locus sets were not only efficient for distinguishing the Beijing genotype from non-Beijing genotype strains, but were also suitable for sublineage genotyping with different discriminatory powers. These results indicate that APD represents a quantitative and efficient approach for selecting MIRU–VNTR loci to discriminate between divergent M. tuberculosis sublineages. Emerging Microbes & Infections (2017) 6, e68; doi:10.1038/emi.2017.58; published online 26 July 2017.