Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis

Abstract

Background: Bedaquiline is a core drug for the treatment of multidrug-resistant tuberculosis; however, the understanding of resistance mechanisms is poor, which is hampering rapid molecular diagnostics. Some bedaquiline-resistant mutants are also cross-resistant to clofazimine. To decipher bedaquiline and clofazimine resistance determinants, we combined experimental evolution, protein modelling, genome sequencing, and phenotypic data.

Methods: For this in-vitro and in-silico data analysis, we used a novel in-vitro evolutionary model using subinhibitory drug concentrations to select bedaquiline-resistant and clofazimine-resistant mutants. We determined bedaquiline and clofazimine minimum inhibitory concentrations and did Illumina and PacBio sequencing to characterise selected mutants and establish a mutation catalogue. This catalogue also includes phenotypic and genotypic data of a global collection of more than 14 000 clinical Mycobacterium tuberculosis complex isolates, and publicly available data. We investigated variants implicated in bedaquiline resistance by protein modelling and dynamic simulations.

Findings: We discerned 265 genomic variants implicated in bedaquiline resistance, with 250 (94%) variants affecting the transcriptional repressor (Rv0678) of the MmpS5-MmpL5 efflux system. We identified 40 new variants in vitro, and a new bedaquiline resistance mechanism caused by a large-scale genomic rearrangement. Additionally, we identified in vitro 15 (7%) of 208 mutations found in clinical bedaquiline-resistant isolates. From our in-vitro work, we detected 14 (16%) of 88 mutations so far identified as being associated with clofazimine resistance and also seen in clinically resistant strains, and catalogued 35 new mutations. Structural modelling of Rv0678 showed four major mechanisms of bedaquiline resistance: impaired DNA binding, reduction in protein stability, disruption of protein dimerisation, and alteration in affinity for its fatty acid ligand.

Interpretation: Our findings advance the understanding of drug resistance mechanisms in M tuberculosis complex strains. We have established an extended mutation catalogue, comprising variants implicated in resistance and susceptibility to bedaquiline and clofazimine. Our data emphasise that genotypic testing can delineate clinical isolates with borderline phenotypes, which is essential for the design of effective treatments.

Funding: Leibniz ScienceCampus Evolutionary Medicine of the Lung, Deutsche Forschungsgemeinschaft, Research Training Group 2501 TransEvo, Rhodes Trust, Stanford University Medical Scientist Training Program, National Institute for Health and Care Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Bill & Melinda Gates Foundation, Wellcome Trust, and Marie Skłodowska-Curie Actions.

Figure 1

Large-scale gene rearrangement in the Rv0678 gene De-novo assembly with the PacBio SMRT Link software indicated a 2·7 Mb inversion (grey box) affecting Rv0678 (ie, splitting Rv0678 into halves; blue box), at positions 779 073 (coding sequence; amino acid 28 in Rv0678; marked by a blue arrow) and 3 552 584 (intergenic region; marked by an orange arrow), flanked by transposase related genes (orange and yellow boxes).

Figure 2

Affected codon positions in Rv0678 leading to bedaquiline resistance in this study and in clinical strains (A) The codon positions in Rv0678 that conferred bedaquiline resistance (and borderline resistance) were compared between three datasets: in-vitro selected mutants, which were single clones isolated from in-vitro evolution experiments (appendix 3 [p 1]); in-vitro population sequencing, corresponding to variants detected from deep sequencing of the entire mutant population in in-vitro evolution experiments (appendix 3 [p 2]); and variants in clinical strains from the CRyPTIC project and literature-reported strains (appendix 2). The variant at base-pair position –10 is the variant in the promoter region. In-vitro population sequencing was not quantifiable by number of isolates, hence the negative bar orientation for the affected codon positions. (B) The Venn diagram shows the number of overlapping codon positions where a mutation was detected between the three datasets. CRyPTIC=Comprehensive Resistance Prediction for Tuberculosis: an International Consortium.

Figure 3

Structural mechanisms of bedaquiline resistance (A) Rv0678 dimer with resistant (orange) and susceptible (blue) mutations shown. The pullout highlights the resistant mutations in the ligand binding pocket. (B) Rv0678 dimer modelled onto DNA, with conserved multiple antibiotic resistance regulator (MarR) family DNA binding elements. (C) Resistant mutations occur across the hydrophobic dimerisation domain.

Figure 4

Networks of the most persistent pockets found in the wild-type and mutated Rv0678 The pocket crosstalk analysis on simulated systems identifies allosteric signalling, by measuring the exchange of atoms between adjacent pockets. Each pocket (ie, network node) is represented as a green sphere, and pockets are connected via network edges (black lines). The width of each network edge is proportional to the communication frequency. Therefore, the larger the network edge, the more frequently atoms are exchanged between the two pockets indicating a strong dynamic connection. All systems shared a large common pocket located at the interface of the two monomers (appendix 1 [p 17]). Comparative analysis showed that the resistance-conferring mutations, Leu40Phe and Ala101Glu, displayed different network edges during the 100 ns simulations with respect to the wild type, whereas Leu40Val recapitulates the main interaction network as found in the wild type.