Low-Level Rifampin Resistance and rpoB Mutations in Mycobacterium tuberculosis: an Analysis of Whole-Genome Sequencing and Drug Susceptibility Test Data in New York.
Joseph SheaTanya A HalseDonna KohlerschmidtPascal LapierreHerns A ModestilCheryl H KearnsFelicia F DworkinJennifer L RakemanVincent EscuyerKimberlee A MusserPublished in: Journal of clinical microbiology (2021)
Rapid and reliable detection of rifampin (RIF) resistance is critical for the diagnosis and treatment of drug-resistant and multidrug-resistant (MDR) tuberculosis. Discordant RIF phenotype/genotype susceptibility results remain a challenge due to the presence of rpoB mutations that do not confer high levels of RIF resistance, as have been exhibited in strains with mutations such as Ser450Leu. These strains, termed low-level RIF resistant, exhibit elevated RIF MICs compared to fully susceptible strains but remain phenotypically susceptible by mycobacterial growth indicator tube (MGIT) testing and have been associated with poor patient outcomes. Here, we assess RIF resistance prediction by whole-genome sequencing (WGS) among a set of 1,779 prospectively tested strains by both prevalence of rpoB gene mutation and phenotype as part of routine clinical testing during a 2.5-year period. During this time, 139 strains were found to have nonsynonymous rpoB mutations, 53 of which were associated with RIF resistance, including both low-level and high-level resistance. Resistance to RIF (1.0 μg/ml in MGIT) was identified in 43 (81.1%) isolates. The remaining 10 (18.9%) strains were susceptible by MGIT but were confirmed to be low-level RIF resistant by MIC testing. Full rpoB gene sequencing overcame the limitations of critical concentration phenotyping, probe-based genotyping, and partial gene sequencing methods. Universal clinical WGS with concurrent phenotypic testing provided a more complete understanding of the prevalence and type of rpoB mutations and their association with RIF resistance in New York.
Keyphrases
- pulmonary tuberculosis
- mycobacterium tuberculosis
- multidrug resistant
- drug resistant
- escherichia coli
- emergency department
- acinetobacter baumannii
- machine learning
- single cell
- gene expression
- copy number
- gram negative
- deep learning
- loop mediated isothermal amplification
- locally advanced
- klebsiella pneumoniae
- big data
- artificial intelligence