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Diverse drug-resistant subpopulations of Mycobacterium tuberculosis are sustained in continuous culture.

ABSTRACT: Drug resistance to tuberculosis (TB) has become more widespread over the past decade. As such, understanding the emergence and fitness of antibiotic-resistant subpopulations is crucial for the development of new interventions. Here we use a simple mathematical model to explain the differences in the response to isoniazid (INH) of Mycobacterium tuberculosis cells cultured under two growth rates in a chemostat. We obtain posterior distributions of model parameters consistent with data using a Markov chain Monte Carlo (MCMC) method. We explore the dynamics of diverse INH-resistant subpopulations consistent with these data in a multi-population model. We find that the simple model captures the qualitative behaviour of the cultures under both dilution rates and also present testable predictions about how diversity is maintained in such cultures.

SUBMITTER: Ayabina D

PROVIDER: S-EPMC5134024 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Diverse drug-resistant subpopulations of Mycobacterium tuberculosis are sustained in continuous culture.

Ayabina Diepreye D Hendon-Dunn Charlotte C Bacon Joanna J Colijn Caroline C

Journal of the Royal Society, Interface 20161101 124

Drug resistance to tuberculosis (TB) has become more widespread over the past decade. As such, understanding the emergence and fitness of antibiotic-resistant subpopulations is crucial for the development of new interventions. Here we use a simple mathematical model to explain the differences in the response to isoniazid (INH) of Mycobacterium tuberculosis cells cultured under two growth rates in a chemostat. We obtain posterior distributions of model parameters consistent with data using a Mark ...[more]

PMID: 27807274

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Project description:Tuberculosis disease (TB), caused by Mycobacterium tuberculosis, is a major global public health problem, resulting in more than 1 million deaths each year. Drug resistance (DR), including multi-drug (MDR-TB), is making TB control difficult and accounts for 16% of new and 48% of previously treated cases. To further complicate treatment decision-making, many clinical studies have reported patients harbouring multiple distinct strains of M. tuberculosis across the main lineages (L1 to L4). The extent to which drug-resistant strains can be deconvoluted within mixed strain infection samples is understudied. Here, we analysed M. tuberculosis isolates with whole genome sequencing data (n = 50,723), which covered the main lineages (L1 9.1%, L2 27.6%, L3 11.8%, L4 48.3%), with genotypic resistance to isoniazid (HR-TB; n = 9546 (29.2%)), rifampicin (RR-TB; n = 7974 (24.4%)), and at least MDR-TB (n = 5385 (16.5%)). TB-Profiler software revealed 531 (1.0%) isolates with potential mixed sub-lineage infections, including some with DR mutations (RR-TB 21/531; HR-TB 59/531; at least MDR-TB 173/531). To assist with the deconvolution of such mixtures, we adopted and evaluated a statistical Gaussian Mixture model (GMM) approach. By simulating 240 artificial mixtures of different ratios from empirical data across L1 to L4, a GMM approach was able to accurately estimate the DR profile of each lineage, with a low error rate for the estimated mixing proportions (mean squared error 0.012) and high accuracy for the DR predictions (93.5%). Application of the GMM model to the clinical mixtures (n = 531), found that 33.3% (188/531) of samples consisted of DR and sensitive lineages, 20.2% (114/531) consisted of lineages with only DR mutations, and 40.6% (229/531) consisted of lineages with genotypic pan-susceptibility. Overall, our work demonstrates the utility of combined whole genome sequencing data and GMM statistical analysis approaches for providing insights into mono and mixed M. tuberculosis infections, thereby potentially assisting diagnosis, treatment decision-making, drug resistance and transmission mapping for infection control.

Project description:Initial responses to tuberculosis treatment are poor predictors of final therapeutic outcomes in drug-susceptible disease, suggesting that treatment success depends on features that are hidden within a small minority of the overall infecting Mycobacterium tuberculosis population. We developed a multitranswell robotic system to perform numerous parallel cultures of genetically barcoded M. tuberculosis exposed to steady-state concentrations of rifampicin to uncover these difficult-to-eliminate minority populations. We found that tolerance emerged repeatedly from at least two subpopulations of barcoded cells, namely, one that could not grow on solid agar media and a second that could form colonies, but whose kill curves diverged from the general bacterial population within 4 and 16 days of drug exposure, respectively. These tolerant subpopulations reproducibly passed through a phase characterized by multiple unfixed resistance mutations followed by emergent drug resistance in some cultures. Barcodes associated with drug resistance identified an especially privileged subpopulation that was rarely eliminated despite 20 days of drug treatment even in cultures that did not contain any drug-resistant mutants. The association of this evolutionary scenario with a defined subset of barcodes across multiple independent cultures suggested a transiently heritable phenotype, and indeed, glpK phase variation mutants were associated with up to 16% of the resistant cultures. Drug tolerance and resistance were eliminated in a ΔruvA mutant, consistent with the importance of bacterial stress responses. This work provides a window into the origin and dynamics of bacterial drug-tolerant subpopulations whose elimination may be critical for developing rapid and resistance-free cures. IMPORTANCE Tuberculosis is unusual among bacterial diseases in that treatments which can rapidly resolve symptoms do not predictably lead to a durable cure unless treatment is continued for months after all clinical and microbiological signs of disease have been eradicated. Using a novel steady-state antibiotic exposure system combined with chromosomal barcoding, we identified small hidden Mycobacterium tuberculosis subpopulations that repeatedly enter a state of drug tolerance with a predisposition to develop fixed drug resistance after first developing a cloud of unfixed resistance mutations. The existence of these difficult-to-eradicate subpopulations may explain the need for extended treatment regimen for tuberculosis. Their identification provides opportunities to test genetic and therapeutic approaches that may result in shorter and more effective TB treatments.

Dataset Information

Diverse drug-resistant subpopulations of Mycobacterium tuberculosis are sustained in continuous culture.

Publications

Diverse drug-resistant subpopulations of Mycobacterium tuberculosis are sustained in continuous culture.

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