Detection of a historic reservoir of bedaquiline/clofazimine resistance-associated variants in Mycobacterium tuberculosis.

Background: Drug resistance in tuberculosis (TB) poses a major ongoing challenge to public health. The recent inclusion of bedaquiline into TB drug regimens has improved treatment outcomes, but this advance is threatened by the emergence of strains of Mycobacterium tuberculosis (Mtb) resistant to bedaquiline. Clinical bedaquiline resistance is most frequently conferred by off-target resistance-associated variants (RAVs) in the mmpR5 gene (Rv0678), the regulator of an efflux pump, which can also confer cross-resistance to clofazimine, another TB drug.

Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: a systematic review and individual isolate meta-analysis.

Background: Clinical bedaquiline resistance predominantly involves mutations in mmpR5 (Rv0678). However, mmpR5 resistance-associated variants (RAVs) have a variable relationship with phenotypic Mycobacterium tuberculosis resistance. We did a systematic review to assess the maximal sensitivity of sequencing bedaquiline resistance-associated genes and evaluate the association between RAVs and phenotypic resistance, using traditional and machine-based learning techniques.

Baseline and treatment-emergent bedaquiline resistance in drug-resistant tuberculosis: a systematic review and meta-analysis.

Bedaquiline resistance is a major threat to drug-resistant tuberculosis control strategies. This analysis found a pooled prevalence of baseline bedaquiline resistance of 2.4% and a pooled prevalence of treatment-emergent bedaquiline resistance of 2.

Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: an individual isolate metaanalysis.

Background: Clinical bedaquiline resistance predominantly involves mutations in (). However, resistance-associated variants (RAVs) have a variable relationship with phenotypic resistance. We performed a systematic review to (1) assess the maximal sensitivity of sequencing bedaquiline resistance-associated genes and (2) evaluate the association between RAVs and phenotypic resistance, using traditional and machine-based learning techniques.