Abstract

Multidrug-resistant tuberculosis (MDR-TB), caused by drug-resistant strains of Mycobacterium tuberculosis, is an increasingly serious problem worldwide. Here we examined a data set of whole-genome sequences from 5,310 M. tuberculosis isolates from five continents. Despite the great diversity of these isolates with respect to geographical point of isolation, genetic background and drug resistance, the patterns for the emergence of drug resistance were conserved globally. We have identified harbinger mutations that often precede multidrug resistance. In particular, the katG mutation encoding p.Ser315Thr, which confers resistance to isoniazid, overwhelmingly arose before mutations that conferred rifampicin resistance across all of the lineages, geographical regions and time periods. Therefore, molecular diagnostics that include markers for rifampicin resistance alone will be insufficient to identify pre-MDR strains. Incorporating knowledge of polymorphisms that occur before the emergence of multidrug resistance, particularly katG p.Ser315Thr, into molecular diagnostics should enable targeted treatment of patients with pre-MDR-TB to prevent further development of MDR-TB.

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References

  1. 1.

    et al. The dilemma of MDR-TB in the global era. Int. J. Tuberc. Lung Dis. 2, 869–876 (1998).

  2. 2.

    World Health Organization. Global Tuberculosis Report 2015 (World Health Organization, Geneva, Switzerland, 2015).

  3. 3.

    Antituberculosis medication side effects constitute major factor for poor adherence to tuberculosis treatment. Bull. World Health Organ. 86, B–D (2008).

  4. 4.

    et al. Long-term outcomes of patients with extensively drug-resistant tuberculosis in South Africa: a cohort study. Lancet 383, 1230–1239 (2014).

  5. 5.

    et al. Rapid molecular detection of tuberculosis and rifampin resistance. N. Engl. J. Med. 363, 1005–1015 (2010).

  6. 6.

    et al. Evolution of extensively drug-resistant tuberculosis over four decades revealed by whole-genome sequencing of Mycobacterium tuberculosis from KwaZulu-Natal, South Africa. PLoS Med. 12, e1001880 (2015).

  7. 7.

    et al. Four decades of transmission of a multidrug-resistant Mycobacterium tuberculosis outbreak strain. Nat. Commun. 6, 7119 (2015).

  8. 8.

    , & Bayesian estimation of mixture models with prespecified elements to compare drug resistance in treatment-naive and experienced tuberculosis cases. PLoS Comput. Biol. 9, e1002973 (2013).

  9. 9.

    , , , & Treatment of isoniazid-resistant tuberculosis with first-line drugs: a systematic review and meta-analysis. Lancet Infect. Dis. (2016).

  10. 10.

    et al. Whole-genome sequencing reveals local transmission patterns of Mycobacterium bovis in sympatric cattle and badger populations. PLoS Pathog. 8, e1003008 (2012).

  11. 11.

    et al. Significance of the identification in the Horn of Africa of an exceptionally deep branching Mycobacterium tuberculosis clade. PLoS One 7, e52841 (2012).

  12. 12.

    et al. Inferring patient-to-patient transmission of Mycobacterium tuberculosis from whole-genome sequencing data. BMC Infect. Dis. 13, 110 (2013).

  13. 13.

    et al. Evolution and transmission of drug-resistant tuberculosis in a Russian population. Nat. Genet. 46, 279–286 (2014).

  14. 14.

    et al. Elucidating emergence and transmission of multidrug-resistant tuberculosis in treatment-experienced patients by whole-genome sequencing. PLoS One 8, e83012 (2013).

  15. 15.

    et al. Out-of-Africa migration and neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat. Genet. 45, 1176–1182 (2013).

  16. 16.

    et al. Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N. Engl. J. Med. 364, 730–739 (2011).

  17. 17.

    et al. Recurrence due to relapse or re-infection with Mycobacterium tuberculosis: a whole-genome sequencing approach in a large, population-based cohort with a high HIV infection prevalence and active follow-up. J. Infect. Dis. 211, 1154–1163 (2015).

  18. 18.

    et al. Evolutionary history and global spread of the Mycobacterium tuberculosis Beijing lineage. Nat. Genet. 47, 242–249 (2015).

  19. 19.

    et al. Unraveling Mycobacterium tuberculosis genomic diversity and evolution in Lisbon, Portugal, a highly drug-resistant setting. BMC Genomics 15, 991 (2014).

  20. 20.

    et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect. Dis. 13, 137–146 (2013).

  21. 21.

    et al. Genome sequencing of 161 Mycobacterium tuberculosis isolates from China identifies genes and intergenic regions associated with drug resistance. Nat. Genet. 45, 1255–1260 (2013).

  22. 22.

    et al. Whole-genome sequencing of Mycobacterium africanum strains from Mali provides insights into the mechanisms of geographic restriction. PLoS Negl. Trop. Dis. 10, e0004332 (2016).

  23. 23.

    et al. Whole-genome sequencing of Mycobacterium tuberculosis provides insight into the evolution and genetic composition of drug-resistant tuberculosis in Belarus. J. Clin. Microbiol. (2016).

  24. 24.

    et al. Variable host–pathogen compatibility in Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA 103, 2869–2873 (2006).

  25. 25.

    et al. Mycobacterium tuberculosis lineage 7 strains are associated with prolonged patient delay in seeking treatment for pulmonary tuberculosis in Amhara region, Ethiopia. J. Clin. Microbiol. 53, 1301–1309 (2015).

  26. 26.

    & The epidemiology of Mycobacterium bovis infections in animals and man: a review. Tuber. Lung Dis. 76 (Suppl. 1), 1–46 (1995).

  27. 27.

    et al. Novel d-cycloserine resistance mechanism in Mycobacterium tuberculosis revealed by whole-genome analysis. Nat. Genet. 48, 544–551 (2016).

  28. 28.

    , , , & Global tuberculosis drug development pipeline: the need and the reality. Lancet 375, 2100–2109 (2010).

  29. 29.

    , , & Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).

  30. 30.

    et al. Clinical characteristics and treatment outcomes of patients with isoniazid-monoresistant tuberculosis. Clin. Infect. Dis. 48, 179–185 (2009).

  31. 31.

    et al. High prevalance of rifampin-monoresistant tuberculosis: a retrospective analysis among Iranian pulmonary tuberculosis patients. Am. J. Trop. Med. Hyg. 90, 99–105 (2014).

  32. 32.

    & Mechanistic studies of the oxidation of isoniazid by the catalase peroxidase from Mycobacterium tuberculosis. J. Am. Chem. Soc. 116, 7425–7426 (1994).

  33. 33.

    , , , & Modification of the NADH of the isoniazid target (InhA) from Mycobacterium tuberculosis. Science 279, 98–102 (1998).

  34. 34.

    et al. Enzymatic characterization of the target for isoniazid in Mycobacterium tuberculosis. Biochemistry 34, 8235–8241 (1995).

  35. 35.

    , & InhA, a target of the antituberculous drug isoniazid, is involved in a mycobacterial fatty acid elongation system, FAS-II. Microbiology 146, 289–296 (2000).

  36. 36.

    , & Genes required for mycobacterial growth defined by high-density mutagenesis. Mol. Microbiol. 48, 77–84 (2003).

  37. 37.

    , , & Resistant mutants of Mycobacterium tuberculosis selected in vitro do not reflect the in vivo mechanism of isoniazid resistance. J. Antimicrob. Chemother. 64, 515–523 (2009).

  38. 38.

    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).

  39. 39.

    et al. Pre-existing isoniazid resistance, but not the genotype of Mycobacterium tuberculosis, drives rifampicin resistance codon preference in vitro. PLoS One 7, e29108 (2012).

  40. 40.

    , & The effect of oxidative stress on the mutation rate of Mycobacterium tuberculosis with impaired catalase–peroxidase function. J. Antimicrob. Chemother. 62, 709–712 (2008).

  41. 41.

    , & Effect of katG mutations on the virulence of Mycobacterium tuberculosis and the implication for transmission in humans. Infect. Immun. 70, 4955–4960 (2002).

  42. 42.

    et al. Initial mutations direct alternative pathways of protein evolution. PLoS Genet. 7, e1001321 (2011).

  43. 43.

    , , , & Adaptive landscape by environment interactions dictate evolutionary dynamics in models of drug resistance. PLoS Comput. Biol. 12, e1004710 (2016).

  44. 44.

    et al. Concentration-dependent Mycobacterium tuberculosis killing and prevention of resistance by rifampin. Antimicrob. Agents Chemother. 51, 3781–3788 (2007).

  45. 45.

    & A new evolutionary and pharmacokinetic–pharmacodynamic scenario for rapid emergence of resistance to single and multiple antituberculosis drugs. Curr. Opin. Pharmacol. 11, 457–463 (2011).

  46. 46.

    , , , & Penetration of isoniazid, rifampicin and pyrazinamide in tuberculous pleural effusion and psoas abscess. Int. J. Tuberc. Lung Dis. 8, 1368–1372 (2004).

  47. 47.

    et al. Early bactericidal activity of high-dose rifampin in patients with pulmonary tuberculosis evidenced by positive sputum smears. Antimicrob. Agents Chemother. 51, 2994–2996 (2007).

  48. 48.

    , & U.S. Public Health Service Cooperative trial of three rifampin–isoniazid regimens in treatment of pulmonary tuberculosis. Am. Rev. Respir. Dis. 119, 879–894 (1979).

  49. 49.

    Role of individual drugs in the chemotherapy of tuberculosis. Int. J. Tuberc. Lung Dis. 4, 796–806 (2000).

  50. 50.

    , & Community-wide isoniazid preventive therapy drives drug-resistant tuberculosis: a model-based analysis. Sci. Transl. Med. 5, 180ra49 (2013).

  51. 51.

    et al. High-dose rifapentine with moxifloxacin for pulmonary tuberculosis. N. Engl. J. Med. 371, 1599–1608 (2014).

  52. 52.

    et al. Treatment outcome of patients with isoniazid-monoresistant tuberculosis. Clin. Microbiol. Infect. 21, 59–68 (2015).

  53. 53.

    , , & High isoniazid resistance rates in rifampicin-susceptible Mycobacterium tuberculosis pulmonary isolates from Pakistan. PLoS One 7, e50551 (2012).

  54. 54.

    , & Quantifying the burden and trends of isoniazid-resistant tuberculosis, 1994–2009. PLoS One 6, e22927 (2011).

  55. 55.

    et al. Tuberculosis in Australia: bacteriologically confirmed cases and drug resistance, 2010. A report of the Australian Mycobacterium Reference Laboratory Network. Commun. Dis. Intell. Q. Rep. 37, E40–E46 (2013).

  56. 56.

    et al. Multidrug- and other forms of drug-resistant tuberculosis are uncommon among treatment-naive tuberculosis patients in Tanzania. PLoS One 10, e0118601 (2015).

  57. 57.

    et al. Prevalence, risk factors and treatment outcomes of isoniazid- and rifampicin-monoresistant pulmonary tuberculosis in Lima, Peru. PLoS One 11, e0152933 (2016).

  58. 58.

    et al. Standard short-course chemotherapy for drug-resistant tuberculosis: treatment outcomes in six countries. J. Am. Med. Assoc. 283, 2537–2545 (2000).

  59. 59.

    et al. Standardized treatment of active tuberculosis in patients with previous treatment and/or with monoresistance to isoniazid: a systematic review and meta-analysis. PLoS Med. 6, e1000150 (2009).

  60. 60.

    et al. Epidemiology of isoniazid-resistance mutations and their effect on tuberculosis treatment outcomes. Antimicrob. Agents Chemother. 57, 3620–3627 (2013).

  61. 61.

    , & Do we need to detect isoniazid resistance in addition to rifampicin resistance in diagnostic tests for tuberculosis? PLoS One 9, e84197 (2014).

  62. 62.

    , , , & GenoType MTBDRplus assay for rapid detection of multidrug resistance in Mycobacterium tuberculosis: a meta-analysis. PLoS One 11, e0150321 (2016).

  63. 63.

    Department of Health, South Africa. Management of Drug-Resistant Tuberculosis: Policy Guidelines (2013).

  64. 64.

    et al. inhA, a gene encoding a target for isoniazid and ethionamide in Mycobacterium tuberculosis. Science 263, 227–230 (1994).

  65. 65.

    , , , & Genetic manipulation of Mycobacterium tuberculosis. Curr. Protoc. Microbiol. 6, 10A.2 (2007).

  66. 66.

    et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome-assembly improvement. PLoS One 9, e112963 (2014).

  67. 67.

    & Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  68. 68.

    , , & Normalizing alternate representations of large sequence variants across multiple bacterial genomes. BMC Bioinformatics 16, 116 (2015).

  69. 69.

    , & FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

  70. 70.

    et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet Infect. Dis. 15, 1193–1202 (2015).

  71. 71.

    et al. Genomic analysis identifies targets of convergent positive selection in drug-resistant Mycobacterium tuberculosis. Nat. Genet. 45, 1183–1189 (2013).

  72. 72.

    PAUP: Phylogenetic Analysis Using Parsimony, Version 3.0 (Illinois Natural History Survey, 1989).

  73. 73.

    et al. Large-scale whole-genome sequencing of M. tuberculosis provides insights into transmission in a high-prevalence area. eLife 4, e05166 (2015).

  74. 74.

    et al. Whole-genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiological study. PLoS Med. 10, e1001387 (2013).

  75. 75.

    et al. Use of whole-genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat. Genet. 43, 482–486 (2011).

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Acknowledgements

We would like to thank the Broad Institute's Genome Sequencing Platform and Assembly and Annotation teams, including S.K. Young, M.E. Priest, T.P. Shea, B.J. Walker, L. Alvarado, M.G. Fitzgerald, S. Gujja, S. Hamilton, C. Howarth, J.D. Larimer, M.D. Pearson, Q. Zeng and J. Wortman. We would like to thank J. Romano and A. Keo for help with lineage detection, and M. Zambrano, B. Ferro and J.C. Rozo for isolation and phenotypic characterization of strains. We are also grateful to members of the TBResist Consortium for contribution of their strains, phenotypic data and expertise, and their help in forging collaborations, and to V. Dartois, D. Thomas, D. Hung and D. Plachetzki for helpful conversations. We also thank three anonymous reviewers of our manuscript for their insights and helpful suggestions. This project has been funded in whole or in part with federal funds from the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Department of Health and Human Services (grant U19AI110818 to the Broad Institute (B.W.B. and A.M.E.)), contract HHSN272200900018C to the Broad Institute (B.W.B.) and contract HHSN2722000900050C to the TB Clinical Diagnostics Research Consortium, the Intramural Research Program of NIAID (C.E.B. and L.E.V.) and the Korean CDC, Korean Ministry of Health and Welfare. This work was also funded (in part) by the intramural research program of the NIAID, NIH (C.E.B.). Funding was also provided by NIH grant 5U01AI069924-07 for IeDEA (A.S.P.), the Howard Hughes Medical Institute (W.R.B.) and NIH grant R01 AI110386 for 'Host–pathogen interactions in a failing global lineage of MTBC: M. africanum' (W.R.B.). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Author information

Author notes

    • Abigail L Manson
    •  & Keira A Cohen

    These authors contributed equally to this work.

Affiliations

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

    • Abigail L Manson
    • , Keira A Cohen
    • , Thomas Abeel
    • , Christopher A Desjardins
    • , Sinéad B Chapman
    • , James Gomez
    • , Alex Salazar
    • , James E Galagan
    • , Bruce W Birren
    •  & Ashlee M Earl
  2. KwaZulu-Natal Research Institute for TB and HIV (K-RITH), Durban, South Africa.

    • Keira A Cohen
    • , Alexander S Pym
    •  & William R Bishai
  3. Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • Keira A Cohen
  4. Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands.

    • Thomas Abeel
    •  & Alex Salazar
  5. Center for Tuberculosis Research, Johns Hopkins University, Baltimore, Maryland, USA.

    • Derek T Armstrong
    • , Kathryn Winglee
    • , Susan E Dorman
    •  & William R Bishai
  6. National Institute of Allergy and Infectious Disease, National Institutes of Health, Bethesda, Maryland, USA.

    • Clifton E Barry III
    •  & Laura E Via
  7. Medical Research Council, TB Platform, Pretoria, South Africa.

    • Jeannette Brand
    • , Lesibana Malinga
    •  & Martie Van der Walt
  8. International Tuberculosis Research Center, Changwon and Department of Microbiology, Yonsei University College of Medicine, Seoul, South Korea.

    • Sang-Nae Cho
    •  & Jong Seok Lee
  9. Office of Cyber Infrastructure and Computational Biology, National Institutes of Health, Rockville, Maryland, USA.

    • Andrei Gabrielian
    •  & Alex Rosenthal
  10. Clinical Hospital of Pneumology Leon Daniello, Cluj Napoca, Romania.

    • Daniela Homorodean
    •  & Andreea M Jodals
  11. Department of Medical Microbiology, Mycobacteriology Laboratory, Makerere University, Kampala, Uganda.

    • Moses Joloba
    •  & Willy Ssengooba
  12. Public Health Agency of Sweden, Solna, Sweden.

    • Pontus Jureen
    •  & Sven Hoffner
  13. University of Sciences, Techniques and Technologies of Bamako (USTTB), Bamako, Mali.

    • Mamoudou Maiga
  14. Novasano Health and Science, New York, New York, USA.

    • Dale Nordenberg
  15. Microbiology and Morphology Laboratory, Phthisiopneumology Institute, Chisinau, Moldova.

    • Ecaterina Noroc
    • , Elena Romancenco
    •  & Valeriu Crudu
  16. Mycobacteriology Research Centre, National Research Institute of Tuberculosis and Lung Disease (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.

    • A A Velayati
    •  & Parissa Farnia
  17. Republican Research and Practical Centre for Pulmonology and Tuberculosis, Minsk, Belarus.

    • Aksana Zalutskaya
    •  & Alena Skrahina
  18. Department of Global Health and Social Medicine, Harvard Medical School, Division of Global Health Equity, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Gail H Cassell
  19. Section of Infectious Diseases, Boston Medical Center, Boston, Massachusetts, USA.

    • Jerrold Ellner
  20. Department of Biomedical Engineering and Microbiology, Boston University, Boston, Massachusetts, USA.

    • James E Galagan
  21. National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.

    • Po-Ren Hsueh
  22. National Institute for Research in Tuberculosis, Chennai, India.

    • Sujatha Narayanan
    •  & Soumya Swaminathan
  23. Rutgers–New Jersey Medical School, Newark, New Jersey, USA.

    • David Alland
  24. Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

    • Ted Cohen
  25. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.

    • Ted Cohen

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  1. TBResist Global Genome Consortium

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Contributions

A.L.M., K.A.C., T.A., C.A.D., B.W.B. and A.M.E. conceived the project; A.L.M., K.A.C., T.A., C.A.D. and A. Salazar analyzed the data; A.L.M., K.A.C. and A.M.E. interpreted results; A.L.M. and K.A.C. wrote the manuscript; and D.T.A., C.E.B., J.B., S.B.C., S.-N.C., A.G., J.G., A.M.J., M.J., P.J., J.S.L., L.M., M.M., D.N., E.N., E.R., A. Skrahina, W.S., A.A.V., K.W., A.Z., L.E.V., G.H.C., S.E.D., J.E., P.F., J.E.G., A.R., V.C., D.H., P.-R.H., S.N., A.S.P., S.S., M.V.d.W., D.A., W.R.B., T.C. and S.H. were involved in sample acquisition and handling, including oversight of these activities. All authors critically read and revised the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ashlee M Earl.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–11, Supplementary Tables 1, 3, 4, 6 and 8–13, and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 2

    List of 5,310 strains included in our final data set of sequenced M. tuberculosis clinical isolates.

  2. 2.

    Supplementary Table 5

    List of all 392 mutations in our data set and their frequencies, as well as their frequencies of occurring first.

  3. 3.

    Supplementary Table 7

    The number of arisals and the number of strains with each mutation, for each of the 11 geographical regions with >30 strains.

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DOI

https://doi.org/10.1038/ng.3767

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