The shortcut of mycobacterial interspersed repetitive unit-variable number tandem repeat typing for Mycobacterium tuberculosis differentiation

Abstract

The 24-loci mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) genotyping has been used as an international standard method for Mycobacterium tuberculosis (Mtb) genotyping. However, different optimized VNTR loci sets for improving the discrimination of specific Mtb genotypes have been proposed. In this regard, we investigated the efficacy of accumulation of the percentage differences (APDs) compared with the least absolute shrinkage and selection operator (LASSO) regression strategy to identify a customized genotype-specific VNTR loci set which provides a resolution comparable to 24-loci MIRU-VNTR in divergent Mtb populations. We utilized Spoligotyping and 24-loci MIRU-VNTR typing for genotyping 306 Mtb isolates. The APD and LASSO regression approaches were used to identify a customized VNTR set in our studied isolates. Besides, the Hunter-Gaston discriminatory index (HGDI), sensitivity, and specificity of each selected loci set were calculated based on both strategies. The selected loci based on LASSO regression compared with APD-based loci showed a better discriminatory power for identifying all studied genotypes except for T genotype, which APD-based loci showed promising discriminative power. Our findings suggested the LASSO regression rather than the APD approach is more effective in the determination of possible discriminative VNTR loci set to precise discrimination of our studied Mtb population and may be beneficial to be used in finding reduced number loci sets in other Mtb genotypes or sublineages. Moreover, we proposed customized genotype-specific MIRU-VNTR loci sets based on the LASSO regression and APD approaches for precise Mtb strains identification. As the proposed VNTR sets offered a comparable discriminatory power to the standard 24 MIRU-VNTR loci set could be promising alternatives to the standard genotyping for using in resource-limited settings.