Automatic Identification of Individual <i>rpoB</i> Gene Mutations Responsible for Rifampin Resistance in Mycobacterium tuberculosis by Use of Melting Temperature Signatures Generated by the Xpert MTB/RIF Ultra Assay
Cao Y, Parmar H, Simmons AM, Kale D, Tong K, Lieu D, Persing D, Kwiatkowski R, et al. (10 authors)
Journal of clinical microbiology · 2019-12
Abstract
Molecular surveillance of rifampin-resistant Mycobacterium tuberculosis can help to monitor the transmission of the disease. The Xpert MTB/RIF Ultra assay detects mutations in the rifampin resistance-determining region (RRDR) of the rpoB gene by the use of melting temperature ( T m ) information from 4 rpoB probes which can fall in one of the 9 different assay-specified T m windows. The large amount of T m data generated by the assay offers the possibility of an RRDR genotyping approach more accessible than whole-genome sequencing. In this study, we developed an automated algorithm to specifically identify a wide range of mutations in the rpoB RRDR by utilizing the pattern of the T m of the 4 probes within the 9 windows generated by the Ultra assay. The algorithm builds a RRDR mutation-specific " T m signature" reference library from a set of known mutations and then identifies the RRDR genotype of an unknown sample by measuring the T m distances between the test sample and the reference T m values. Validated using a set of clinical isolates, the algorithm correctly identified RRDR genotypes of 93% samples with a wide range of rpoB single and double mutations. Our analytical approach showed a great potential for fast RRDR mutation identification and may also be used as a stand-alone method for ruling out relapse or transmission between patients. The algorithm can be further modified and optimized for higher accuracy as more Ultra data become available.
MeSH terms
- Humans
- Mycobacterium tuberculosis
- Tuberculosis
- Rifampin
- DNA-Directed RNA Polymerases
- Bacterial Proteins
- Microbial Sensitivity Tests
- Sensitivity and Specificity
- Drug Resistance, Bacterial
- Mutation
- Algorithms