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Automatic Identification of Individual rpoB Gene Mutations Responsible for Rifampin Resistance in Mycobacterium tuberculosis by Use of Melting Temperature Signatures Generated by the Xpert MTB/RIF Ultra Assay
Molecular surveillance of rifampin-resistant 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 gene by the use of melting temperature ( ) information from 4 probes which can fall in one of...
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Published in: | Journal of clinical microbiology 2019-12, Vol.58 (1) |
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Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Molecular surveillance of rifampin-resistant
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
gene by the use of melting temperature (
) information from 4
probes which can fall in one of the 9 different assay-specified
windows. The large amount of
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
RRDR by utilizing the pattern of the
of the 4 probes within the 9 windows generated by the Ultra assay. The algorithm builds a RRDR mutation-specific "
signature" reference library from a set of known mutations and then identifies the RRDR genotype of an unknown sample by measuring the
distances between the test sample and the reference
values. Validated using a set of clinical isolates, the algorithm correctly identified RRDR genotypes of 93% samples with a wide range of
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. |
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ISSN: | 0095-1137 1098-660X |
DOI: | 10.1128/JCM.00907-19 |