IJERPH, Vol. 15, Pages 782: The Epidemiological Significance and Temporal Stability of Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats-Based Method Applied to Mycobacterium tuberculosis in China (International Journal of Environmental Research and Public Health)

This study aimed to validate the epidemiological significance and temporal stability of Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats (MIRU-VNTR) typing in a genetically and geographically diverse set of clinical isolates from patients diagnosed with pulmonary tuberculosis in China. Between 2010 and 2013, a total of 982 Mycobacterium tuberculosis isolates were collected from four population-based investigations in China. Apart from the currently applied 24-locus MIRU-VNTR, six additional hypervariable loci were analyzed in order to validate the MIRU-VNTR combinations in terms of their epidemiological links, clustering time span, and paired geographic distance. In vitro temporal stability was analyzed for both individual MIRU-VNTR loci, and for several combinations of loci. In the present study, four MIRU-VNTR combinations, including the hypervariable loci 3820, 3232, 2163a, and 4120, were evaluated. All of these combinations obtained a Hunter-Gaston discriminatory index (HGDI) value over 0.9900 with a reduced clustering proportion (from 32.0% to 25.6%). By comparing epidemiological links, clustering time span, and paired geographic distance, we found that the performances of the four MIRU-VNTR combinations were comparable to the insertion sequence 6110 restriction fragment length polymorphism (IS6110-RFLP), and significantly better than that of 24-locus MIRU-VNTR genotyping alone. The proportion of temporally stable loci ranged from 90.5% to 92.5% within the combined MIRU-VNTR genotyping, which is higher than IS6110-RFLP (85.4%). By adding four hypervariable loci to the standard 24-locus MIRU-VNTR genotyping, we obtained a high discriminatory power, stability and epidemiological significance. This algorithm could therefore be used to improve tuberculosis transmission surveillance and outbreak investigation in China.