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Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis

Nature Genetics volume 45, pages 784–790 (2013)Cite this article

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Abstract

A key question in tuberculosis control is why some strains of M. tuberculosis are preferentially associated with resistance to multiple drugs. We demonstrate that M. tuberculosis strains from lineage 2 (East Asian lineage and Beijing sublineage) acquire drug resistances in vitro more rapidly than M. tuberculosis strains from lineage 4 (Euro-American lineage) and that this higher rate can be attributed to a higher mutation rate. Moreover, the in vitro mutation rate correlates well with the bacterial mutation rate in humans as determined by whole-genome sequencing of clinical isolates. Finally, using a stochastic mathematical model, we demonstrate that the observed differences in mutation rate predict a substantially higher probability that patients infected with a drug-susceptible lineage 2 strain will harbor multidrug-resistant bacteria at the time of diagnosis. These data suggest that interventions to prevent the emergence of drug-resistant tuberculosis should target bacterial as well as treatment-related risk factors.

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Figure 1: Lineage 2 strains more rapidly acquire rifampicin resistance.
Figure 2: Altering drug concentration does not alter the observation that lineage 2 strains more rapidly acquire rifampicin resistance.
Figure 3: The cumulative distribution of drug-resistant mutants from lineage 2 and lineage 4 indicates that mutations do not occur after exposure to antibiotic.
Figure 4: Small differences in target size and differences in basal mutation rate are responsible for the observed differences in the rate of acquisition of drug resistance.
Figure 5: A representative lineage 2 strain acquires isoniazid and ethambutol resistance at a higher rate than a strain from lineage 4.
Figure 6: Bayesian MCMC analysis shows a mutation rate in humans similar to that estimated in strains from the macaque model and in vitro.
Figure 7: A stochastic simulation mathematical model predicts the emergence of multidrug-resistant tuberculosis before the onset of treatment.

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Acknowledgements

This work was supported by a New Innovator's Award (DP2 0D001378) from the Director's Office of the US National Institutes of Health (S.M.F.), by a subcontract from the National Institute of Allergy and Infectious Diseases (U19 AI076217 to S.M.F. and M.B.M.), by the US National Institutes of Health Models of Infectious Disease Agent Study program, through cooperative agreement 1 U54 GM088558 (M.L.), by a Howard Hughes Medical Institute Physician Scientist Early Career Award (S.M.F.), by a Merit Fellowship from the Harvard University Graduate School of Arts and Sciences (C.B.F.) and by a Doris Duke Charitable Foundation Clinical Scientist Development Award (S.M.F.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases or the US National Institutes of Health.

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Authors and Affiliations

  1. Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA

    Christopher B Ford, Rupal R Shah, Marc Lipsitch & Sarah M Fortune

  2. Division of Pulmonary and Critical Care Medicine, Curry International Tuberculosis Center, University of California, San Francisco, San Francisco, California, USA

    Midori Kato Maeda

  3. Swiss Tropical & Public Health Institute, Basel, Switzerland

    Sebastien Gagneux

  4. University of Basel, Basel, Switzerland

    Sebastien Gagneux

  5. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA

    Megan B Murray & Ted Cohen

  6. Division of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Ted Cohen

  7. Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, Massachusetts, USA

    Ted Cohen & Marc Lipsitch

  8. Communicable Disease Prevention and Control Services, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada

    James C Johnston & Jennifer Gardy

  9. Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, Canada

    Jennifer Gardy

  10. Ragon Institute of MGH, MIT and Harvard, Boston, Massachusetts, USA

    Sarah M Fortune

  11. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Sarah M Fortune

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  1. Christopher B Ford
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Contributions

C.B.F. designed the study, performed experimental and molecular studies, developed the mathematical model and conducted analyses, prepared the figures and drafted the manuscript. R.R.S. performed experimental studies. M.K.M. and S.G. isolated clinical strains. M.B.M. and T.C. advised development of the mathematical model. J.C.J. and J.G. contributed to analyses. M.L. advised design of the study and data analysis, including development of the mathematical model. S.M.F. designed the study, supervised experimental and molecular studies, and drafted the manuscript. All authors edited the manuscript.

Corresponding author

Correspondence to Sarah M Fortune.

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Ford, C., Shah, R., Maeda, M. et al. Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. Nat Genet 45, 784–790 (2013). https://doi.org/10.1038/ng.2656

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