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Genomic analysis of globally diverse Mycobacterium tuberculosis strains provides insights into the emergence and spread of multidrug resistance

Nature Genetics volume 49, pages 395–402 (2017)Cite this article

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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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Figure 1: Geographical distribution of M. tuberculosis isolates by drug-resistance (DR) pattern.
Figure 2: Across the globe, isoniazid resistance was overwhelmingly the first step toward drug resistance.
Figure 3: Sequential acquisition of drug-resistance-conferring mutations shows that isoniazid-resistance-conferring mutations, specifically katG mutation encoding p.Ser315Thr, most often come first in sequential pairs of mutations.
Figure 4: In all lineages and global regions, the katG mutation encoding p.Ser315Thr occurs first, and few examples of converse ordering are observed.
Figure 5: Non-rifampicin drug resistance often precedes the arisal of mutations that are detectable by the Xpert MTB/RIF assay.
Figure 6: katG p.Ser315Thr is a commonly occurring mutation with very little resistance to other drugs arising prior to its occurrence.

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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.

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Author notes

  1. Abigail L Manson, Keira A Cohen and Ted Cohen: These authors contributed equally to this work.

Authors and 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, Jeannette Brand, Lesibana Malinga, Martie Van der Walt, Lesibana Malinga & Martie Van der Walt

  8. Changwon and Department of Microbiology, International Tuberculosis Research Center, 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 & Daniela Homorodean

  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, 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 & 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, 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, Division of Global Health Equity, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA

    Gail H Cassell & 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. Abigail L Manson
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Consortia

TBResist Global Genome Consortium

  • Jeannette Brand
  • , Pontus Jureen
  • , Lesibana Malinga
  • , Dale Nordenberg
  • , A A Velayati
  • , Gail H Cassell
  • , Parissa Farnia
  • , Daniela Homorodean
  • , Martie Van der Walt
  •  & Sven Hoffner

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.

Corresponding author

Correspondence to Ashlee M Earl.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Lineage and geographic distribution of M. tuberculosis isolates.

(a) Geographic distribution of M. tuberculosis isolates by lineage. This plot shows the sampling frequency within our data set of 5,310 strains and is not necessarily indicative of overall global incidence. The pie charts illustrate the relative proportions of M. tuberculosis lineages isolated within each of 11 UN subregions (http://unstats.un.org/unsd/methods/m49/m49regin.htm). The map is colored by UN geographic subregion. There were no strains in our data set from geographic regions colored gray. UN geographic subregions with fewer than 30 strains were excluded from this figure. This map was modified from a blank map of UN geographical subregions (Pusch, T.; licensed under CC BY-SA 3.0 via Wikimedia Commons; https://commons.wikimedia.org/wiki/File:Geografiaj_subregionoj_la%C5%AD_Unui%C4%9Dintaj_Nacioj_malplene.svg). (b) Global prevalence for each of six WHO global subregions as a percentage of total global TB burden (black bars), side by side with the percentage of our data set from each of these regions. We used different geographic subdivisions (WHO rather than UN) for this panel to present our data side by side with WHO data. Note that our data set is under-represented in strains from Southeast Asia (which includes India) and over-represented in strains from Africa and Europe (which includes Russia). WHO global subregions are defined as follows. AFR (Africa): Algeria, Angola, Benin, Burkina Faso, Cameroon, Cape Verde, Chad, Comoros, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Madagascar, Mali, Mauritania, Mauritius, Niger, Nigeria, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Togo, Botswana, Burundi, Central African Republic, Congo, Côte d'Ivoire, Democratic Republic of the Congo, Eritrea, Ethiopia, Kenya, Lesotho, Malawi, Mozambique, Namibia, Rwanda, South Africa, Swaziland, Uganda, United Republic of Tanzania, Zambia, Zimbabwe; AMR (Americas): Canada, Cuba, United States of America, Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Brazil, Chile, Colombia, Costa Rica, Dominica, Dominican Republic, El Salvador, Grenada, Guyana, Honduras, Jamaica, Mexico, Panama, Paraguay, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela, Bolivia, Ecuador, Guatemala, Haiti, Nicaragua, Peru; EMR (Eastern Mediterranean): Bahrain, Cyprus, Iran (Islamic Republic of), Jordan, Kuwait, Lebanon, Libyan Arab Jamahiriya, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates, Afghanistan, Djibouti, Egypt, Iraq, Morocco, Pakistan, Somalia, Sudan, Yemen; EUR (Europe): Andorra, Austria, Belgium, Croatia, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Malta, Monaco, Netherlands, Norway, Portugal, San Marino, Slovenia, Spain, Sweden, Switzerland, United Kingdom, Albania, Armenia, Azerbaijan, Bosnia and Herzegovina, Bulgaria, Georgia, Kyrgyzstan, Poland, Romania, Slovakia, Tajikistan, The Former Yugoslav Republic of Macedonia, Turkey, Turkmenistan, Uzbekistan, Yugoslavia, Belarus, Estonia, Hungary, Kazakhstan, Latvia, Lithuania, Republic of Moldova, Russian Federation, Ukraine; SEAR (Southeast Asia): Indonesia, Sri Lanka, Thailand, Bangladesh, Bhutan, Democratic People's Republic of Korea, India, Maldives, Myanmar, Nepal, Timor Leste; WPR (Western Pacific): Cambodia, China, Cook Islands, Fiji, Kiribati, Lao People's Democratic Republic, Malaysia, Marshall Islands, Micronesia (Federated States of), Mongolia, Nauru, Niue, Palau, Papua New Guinea, Philippines, Republic of Korea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Viet Nam, Australia, Brunei Darussalam, Japan, New Zealand, Singapore.

Supplementary Figure 2 Phylogenetic tree of all 5,310 M. tuberculosis isolates indicating data set of origin.

Phylogenetic tree of all 5,310 M. tuberculosis isolates (Online Methods). Colors in the outer circle indicate the data set of origin for each strain. In the central radial phylogeny, lineages are labeled and color-coded as follows: pink, lineage 1; blue, lineage 2; purple, lineage 3; red, lineage 4; brown, lineage 5 (M. africanum); dark green, lineage 6 (M. africanum); orange, lineage 7; light green, M. bovis.

Supplementary Figure 3 Phylogenetic tree of all 5,310 M. tuberculosis isolates indicating geographic region of isolation.

Phylogenetic tree of all 5,310 M. tuberculosis isolates (Online Methods). Colors in the outer circle indicate the geographic region of isolation. In the central radial phylogeny, lineages are labeled and color-coded as follows: pink, lineage 1; blue, lineage 2; purple, lineage 3; red, lineage 4; brown, lineage 5 (M. africanum); dark green, lineage 6 (M. africanum); orange, lineage 7; light green, M. bovis.

Supplementary Figure 4 Phylogenetic tree of all 5,310 M. tuberculosis isolates indicating genotypic drug resistance pattern.

Phylogenetic tree of all 5,310 M. tuberculosis isolates (Online Methods). Colors in the outer circle indicate the genotypic drug resistance pattern of the corresponding strain. In the central radial phylogeny, lineages are labeled and color-coded as follows: pink, lineage 1; blue, lineage 2; purple, lineage 3; red, lineage 4; brown, lineage 5 (M. africanum); dark green, lineage 6 (M. africanum); orange, lineage 7; light green, M. bovis.

Supplementary Figure 5 Rates of genotypic resistance to individual drugs across our entire data set of 5,310 strains.

The greatest numbers of strains were resistant to isoniazid, rifampicin and streptomycin.

Supplementary Figure 6 Overview of drug resistance mutation arisals.

The number of arisals and number of strains for each of the 392 drug resistance mutations detected in our data set.

Supplementary Figure 7 Breakdown of overall genotypic drug resistance patterns by lineage.

Data are shown for each lineage with greater than 100 strains in our data set.

Supplementary Figure 8 Breakdown of genotypic resistance to individual antituberculous drugs by predicted lineage.

M. tuberculosis strains from lineages 5, 6 and 7 were excluded because of low numbers of isolates, and lineage B was excluded as all 44 strains were fully drug susceptible.

Supplementary Figure 9 Breakdown of resistance to individual antituberculous drugs by UN geographic region.

Geographic distribution of strains in 11 UN geographic regions having greater than 30 representative isolates in this data set (45 strains that derived from 6 UN geographic regions were excluded from this figure).

Supplementary Figure 10 Breakdown of monoresistance and polyresistance by region and lineage.

(a) Breakdown of monoresistance by geographic region and by lineage. (b) Breakdown of poly-drug resistance (excluding MDR, pre-XDR and MDR) by geographic region and by lineage.

Supplementary Figure 11 Percentage of nodes where resistance to other drugs arose prior or coincident with an Xpert MTB/RIF-detectable mutation.

(a) Resistance to single drugs. (b) Resistance to multiple drugs. This figure shows results for cases where all nodes between the first and last drug acquisition event leading up to the Xpert MTB/RIF-detectable mutation fit our filtering criteria (>90% bootstrap in the more ancestral node of a pair; maximal branch lengths between pairs <1 × 10–4).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11, Supplementary Tables 1, 3, 4, 6 and 8–13, and Supplementary Note (PDF 3734 kb)

Supplementary Table 2

List of 5,310 strains included in our final data set of sequenced M. tuberculosis clinical isolates. (XLSX 414 kb)

Supplementary Table 5

List of all 392 mutations in our data set and their frequencies, as well as their frequencies of occurring first. (XLSX 122 kb)

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. (XLSX 127 kb)

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Manson, A., Cohen, K., Abeel, T. 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). https://doi.org/10.1038/ng.3767

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