Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Clinically prevalent mutations in Mycobacterium tuberculosis alter propionate metabolism and mediate multidrug tolerance

Abstract

The global epidemic of drug-resistant tuberculosis is a catastrophic example of how antimicrobial resistance is undermining the public health gains made possible by combination drug therapy. Recent evidence points to unappreciated bacterial factors that accelerate the emergence of drug resistance. In a genome-wide association study of Mycobacterium tuberculosis isolates from China, we find mutations in the gene encoding the transcription factor prpR enriched in drug-resistant strains. prpR mutations confer conditional drug tolerance to three of the most effective classes of antibiotics by altering propionyl-CoA metabolism. prpR-mediated drug tolerance is carbon-source dependent, and while readily detectable during infection of human macrophages, is not captured by standard susceptibility testing. These data define a previously unrecognized and clinically prevalent class of M. tuberculosis variants that undermine antibiotic efficacy and drive drug resistance.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Genetic associations with isoniazid resistance.
Fig. 2: Distribution and functional characterization of prpR mutants.
Fig. 3: prpR mutants display conditional multidrug tolerance.
Fig. 4: Infection of human macrophages induces multidrug tolerance in prpR mutants.
Fig. 5: Metabolic rescue of propionate sensitivity suppresses drug tolerance in prpR mutants.

References

  1. 1.

    Global Tuberculosis Report 2017 (WHO, 2017); http://www.who.int/tb/publications/global_report/gtbr2017_main_text.pdf

  2. 2.

    Gegia, M., Winters, N., Benedetti, A., van Soolingen, D. & Menzies, D. Treatment of isoniazid-resistant tuberculosis with first-line drugs: a systematic review and meta-analysis. Lancet Infect. Dis. 17, 223–234 (2017).

    CAS  PubMed  Google Scholar 

  3. 3.

    Weis, S. E. et al. The effect of directly observed therapy on the rates of drug resistance and relapse in tuberculosis. N. Engl. J. Med. 330, 1179–1184 (1994).

    CAS  PubMed  Google Scholar 

  4. 4.

    Pasipanodya, J. G. & Gumbo, T. A meta-analysis of self-administered vs directly observed therapy effect on microbiologic failure, relapse, and acquired drug resistance in tuberculosis patients. Clin. Infect. Dis. 57, 21–31 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Gillespie, S. H. et al. Four-month moxifloxacin-based regimens for drug-sensitive tuberculosis. N. Engl. J. Med. 371, 1577–1587 (2014).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

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

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Merle, C. S. et al. A four-month gatifloxacin-containing regimen for treating tuberculosis. N. Engl. J. Med. 371, 1588–1598 (2014).

    PubMed  Google Scholar 

  8. 8.

    Fridman, O., Goldberg, A., Ronin, I., Shoresh, N. & Balaban, N. Q. Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations. Nature 513, 418–421 (2014).

    CAS  PubMed  Google Scholar 

  9. 9.

    Van den Bergh, B. et al. Frequency of antibiotic application drives rapid evolutionary adaptation of Escherichia coli persistence. Nat. Microbiol. 1, 16020 (2016).

    PubMed  Google Scholar 

  10. 10.

    Brauner, A., Fridman, O., Gefen, O. & Balaban, N. Q. Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat. Rev. Microbiol. 14, 320–30 (2016).

    CAS  PubMed  Google Scholar 

  11. 11.

    Lewis, K. Persister cells. Annu. Rev. Microbiol. 64, 357–372 (2010).

    CAS  PubMed  Google Scholar 

  12. 12.

    Mulcahy, L., Burns, J., Lory, S. & Lewis, K. Emergence of Pseudomonas aeruginosa strains producing high levels of persister cells in patients with cystic fibrosis. J. Bacteriol. 192, 6191–6199 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lafleur, M. D., Qi, Q. & Lewis, K. Patients with long-term oral carriage harbor high-persister mutants of Candida albicans. Antimicrob. Agents Chemother. 54, 39–44 (2010).

    CAS  PubMed  Google Scholar 

  14. 14.

    Keren, I., Minami, S., Rubin, E. & Lewis, K. Characterization and transcriptome analysis of Mycobacterium tuberculosis persisters. mBio 2, e00100–11 (2011).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Gomez, J. E. & McKinney, J. D. M. tuberculosis persistence, latency, and drug tolerance. Tuberculosis 84, 29–44 (2004).

    PubMed  Google Scholar 

  16. 16.

    Wayne, L. G. & Hayes, L. G. An in vitro model for sequential study of shiftdown of Mycobacterium tuberculosis through two stages of nonreplicating persistence. Infect. Immun. 64, 2062–2069 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Xie, Z., Siddiqi, N. & Rubin, E. J. Differential antibiotic susceptibilities of starved Mycobacterium tuberculosis isolates. Antimicrob. Agents Chemother. 49, 4778–4780 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Adams, K. N. et al. Drug tolerance in replicating mycobacteria mediated by a macrophage-induced efflux mechanism. Cell 145, 39–53 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Dhar, N. & McKinney, J. D. Mycobacterium tuberculosis persistence mutants identified by screening in isoniazid-treated mice. Proc. Natl Acad. Sci. USA 107, 12275–12280 (2010).

    CAS  PubMed  Google Scholar 

  20. 20.

    Levin, B. R. & Rozen, D. E. Non-inherited antibiotic resistance. Nat. Rev. Microbiol. 4, 556–562 (2006).

    CAS  PubMed  Google Scholar 

  21. 21.

    Levin-Reisman, I. et al. Antibiotic tolerance facilitates the evolution of resistance. Science 355, 826–830 (2017).

    CAS  PubMed  Google Scholar 

  22. 22.

    Zhao, Y. et al. National survey of drug-resistant tuberculosis in China. N. Engl. J. Med. 366, 2161–2170 (2012).

    CAS  PubMed  Google Scholar 

  23. 23.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Luo, T. et al. Southern East Asian origin and coexpansion of Mycobacterium tuberculosis Beijing family with Han Chinese. Proc. Natl Acad. Sci. USA 112, 8136–41 (2015).

    CAS  PubMed  Google Scholar 

  25. 25.

    Casali, N. et al. Microevolution of extensively drug-resistant tuberculosis in Russia. Genome Res. 22, 735–745 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Cohen, K. A. et al. Evolution of extensively drug-resistant tuberculosis over four decades: whole genome sequencing and dating analysis of Mycobacterium tuberculosis isolates from KwaZulu-Natal. PLoS Med. 12, e1001880 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Manson, A. L. et al. Genomic analysis of globally diverse Mycobacterium tuberculosis strains provides insights into the emergence and spread of multidrug resistance. Nat. Genet. 49, 395–402 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Zhang, H. 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).

    CAS  PubMed  Google Scholar 

  30. 30.

    Walker, T. M. et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet Infect. Dis. 15, 1193–202 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Sun, L., Zhang, L., Zhang, H. & He, Z.-G. Characterization of a bifunctional β-lactamase/ribonuclease and its interaction with a chaperone-like protein in the pathogen Mycobacterium tuberculosis H37Rv. Biochem. 76, 350–358 (2011).

    CAS  Google Scholar 

  32. 32.

    Taverniti, V., Forti, F., Ghisotti, D. & Putzer, H. Mycobacterium smegmatis RNase J is a 5ʹ–3ʹ exo-/endoribonuclease and both RNase J and RNase E are involved in ribosomal RNA maturation. Mol. Microbiol. 82, 1260–1276 (2011).

    CAS  PubMed  Google Scholar 

  33. 33.

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

    PubMed Central  Google Scholar 

  34. 34.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Holt, K. E. et al. Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam. Nat. Genet. 50, 849–856 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Liu, Q. et al. Within patient microevolution of Mycobacterium tuberculosis correlates with heterogeneous responses to treatment. Sci. Rep. 5, 17507 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Pandey, A. K. & Sassetti, C. M. Mycobacterial persistence requires the utilization of host cholesterol. Proc. Natl Acad. Sci. USA 105, 4376–4380 (2008).

    CAS  PubMed  Google Scholar 

  38. 38.

    Griffin, J. E. et al. Cholesterol catabolism by Mycobacterium tuberculosis requires transcriptional and metabolic adaptations. Chem. Biol. 19, 218–227 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Masiewicz, P., Brzostek, A., Wolański, M., Dziadek, J. & Zakrzewska-Czerwińska, J. A novel role of the PrpR as a transcription factor involved in the regulation of methylcitrate pathway in Mycobacterium tuberculosis. PLoS ONE 7, e43651 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Muñoz-Elías, E. J., Upton, A. M., Cherian, J. & McKinney, J. D. Role of the methylcitrate cycle in Mycobacterium tuberculosis metabolism, intracellular growth, and virulence. Mol. Microbiol. 60, 1109–1122 (2006).

    PubMed  Google Scholar 

  41. 41.

    KATO, N. The free and bound forms of the serum vitamin B12 in various animal species. J. Vitaminol. (Kyoto) 6, 132–138 (1960).

    CAS  Google Scholar 

  42. 42.

    Micklinghoff, J. C. et al. Role of the transcriptional regulator RamB (Rv0465c) in the control of the glyoxylate cycle in Mycobacterium tuberculosis. J. Bacteriol. 191, 7260–7269 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Eoh, H. & Rhee, K. Y. Methylcitrate cycle defines the bactericidal essentiality of isocitrate lyase for survival of Mycobacterium tuberculosis on fatty acids. Proc. Natl Acad. Sci. USA 111, 4976–4981 (2014).

    CAS  PubMed  Google Scholar 

  44. 44.

    Savvi, S. et al. Functional characterization of a vitamin B12-dependent methylmalonyl pathway in Mycobacterium tuberculosis: implications for propionate metabolism during growth on fatty acids. J. Bacteriol. 190, 3886–3895 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Somerville, W., Thibert, L., Schwartzman, K. & Behr, M. A. Extraction of Mycobacterium tuberculosis DNA: a question of containment. J. Clin. Microbiol. 43, 2996–2997 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Patel, R. & Jain, M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS ONE 7, e30619 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Comas, I. et al. Human T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved. Nat. Genet. 42, 498–503 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Fouts, D. Phage_Finder: automated identification and classification of prophage regions in complete bacterial genome sequences. Nucleic Acids Res. 34, 5839–5851 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Sandgren, A. et al. Tuberculosis drug resistance mutation database. PLoS Med. 6, e1000002 (2009).

    PubMed Central  Google Scholar 

  52. 52.

    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Tamura, K. et al. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol. 28, 2731–2739 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

    CAS  PubMed  Google Scholar 

  56. 56.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

  57. 57.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Martin, C. J. et al. Digitally barcoding Mycobacterium tuberculosis reveals in vivo infection dynamics in the macaque model of tuberculosis. mBio 8, e00312–17 (2017).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Blumenthal, A., Trujillo, C., Ehrt, S. & Schnappinger, D. Simultaneous analysis of multiple Mycobacterium tuberculosis knockdown mutants in vitro and in vivo. PLoS ONE 5, e15667 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Support was generously provided by the National Major Science and Technology Project of China (2012ZX10003002001-001; 2014ZX10003002) to Y.Z.; the National Program on Key Basic Research Project of China (2014CB744403973) and the CAMS Innovation Fund for Medical Sciences (2016-I2M-1-013) to Q.J.; the National Institute of Health (5U19AI109755-04) to S.M.F.; and NIH pre-doctoral training grant support (5T32AI007638-15 to E. J. Rubin and 5T32-AI049928-14 to D. Wirth) to N.D.H.

Author information

Affiliations

Authors

Contributions

X.Z., B.Z., X.C.O., S.F.W., H.X. and Y. Zhou were responsible for cohort study and strain set assembly. Q.J. and Y. Zhao were responsible for genome sequencing study design and supervision. J.Y., X.Z., L.L., Z.C., J.D., L.S. and Y. Zhu were responsible for genome sequencing and variant analysis. N.D.H., J.Y., Y.G. and S.M.F. were responsible for genome-wide association study. N.D.H. and S.M.F. were responsible for experimental validation. N.D.H., J.Y. and S.M.F. were responsible for manuscript preparation.

Corresponding authors

Correspondence to Yanlin Zhao or Qi Jin or Sarah M. Fortune.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–7.

41564_2018_218_MOESM2_ESM.pdf

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hicks, N.D., Yang, J., Zhang, X. et al. Clinically prevalent mutations in Mycobacterium tuberculosis alter propionate metabolism and mediate multidrug tolerance. Nat Microbiol 3, 1032–1042 (2018). https://doi.org/10.1038/s41564-018-0218-3

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing