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Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis

An Author Correction to this article was published on 19 April 2018

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Abstract

To characterize the genetic determinants of resistance to antituberculosis drugs, we performed a genome-wide association study (GWAS) of 6,465 Mycobacterium tuberculosis clinical isolates from more than 30 countries. A GWAS approach within a mixed-regression framework was followed by a phylogenetics-based test for independent mutations. In addition to mutations in established and recently described resistance-associated genes, novel mutations were discovered for resistance to cycloserine, ethionamide and para-aminosalicylic acid. The capacity to detect mutations associated with resistance to ethionamide, pyrazinamide, capreomycin, cycloserine and para-aminosalicylic acid was enhanced by inclusion of insertions and deletions. Odds ratios for mutations within candidate genes were found to reflect levels of resistance. New epistatic relationships between candidate drug-resistance-associated genes were identified. Findings also suggest the involvement of efflux pumps (drrA and Rv2688c) in the emergence of resistance. This study will inform the design of new diagnostic tests and expedite the investigation of resistance and compensatory epistatic mechanisms.

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Fig. 1: Geographic distribution of the 6,465 Mycobacterium tuberculosis isolates analyzed in the study.
Fig. 2: Whole-genome phylogeny of the 6,465 Mycobacterium tuberculosis isolates.
Fig. 3: Log odds ratios from SNP–drug resistance associations are a potential surrogate for resistance level.

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  • 19 April 2018

    In the version of this article initially published, the URL listed for TubercuList was incorrect. The correct URL is https://mycobrowser.epfl.ch/. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

The project was supported by the KAUST faculty baseline research fund (BAS/1/1020-01-01) to A.P. The authors wish to thank members of the KAUST Bioscience Core laboratory who sequenced samples. We thank the Wellcome Trust Sanger Institute core and pathogen sequencing and informatics teams who were involved in the Malawi and Uganda studies. The work was funded in part by the Wellcome Trust (grant numbers WT096249/Z/11/B, WT088559MA, WT081814/Z/06/Z and WT098051) and the Wellcome Trust–Burroughs Wellcome Fund Infectious Diseases Initiative grant (number 063410/ABC/00/Z). F.C. was the recipient of a Bloomsbury College PhD Studentship and was supported by the Wellcome Trust (201344/Z/16/Z); J. Perdigão received a Fundação para a Ciência e a Tecnologia (Portugal) postdoctoral fellowship fund (SFRH/BPD/95406/2013). The Calouste Gulbenkian Foundation, the Institute Gulbenkian in Lisbon and the European Society of Clinical Microbiology and Infectious Diseases supported the research of C.P., J. Perdigão, I.P. and M.V. J. Phelan is funded by a BBSRC PhD studentship. T.G.C. is funded by the Medical Research Council UK (grant numbers MR/K000551/1, MR/M01360X/1, MR/N010469/1 and MC_PC_15103). N.F. is funded by the Medical Research Council UK (grant number MR/K020420/1). T.M. is supported by the Ministry of Health, Labor and Welfare of Japan (H21-Shinkou-Ippan-008 and H24-Shinkou-Ippan-010). We thank N. Mistry (Foundation for Medical Research, Mumbai) for contributing Mtb archived strains and drug sensitivity testing data. We wish to thank G. Moniz at the Laboratorio Central de Saúde Pública for supporting the collection of samples in Brazil and the South African National Health Laboratory Service for their contribution providing access to clinical Mtb isolates. The MRC eMedLab computing resource was used for bioinformatics and statistical analysis. The authors declare no conflicts of interest. The work has been performed as part of the TB Global Drug Resistance Collaboration (see URLs).

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Authors and Affiliations

Authors

Contributions

R.M., A.P. and T.G.C. conceived and directed the project. G.A.H.-C., K.M. and R.M. coordinated sample collection and undertook DNA extraction. S. Alghamdi, A.M.A., A.O.A., A.A., T.B.B., M.C., A.C., A.C.C., K.D., L.G., J.R.G., D.T.M.H., R.H., Z.H., P.V.H., M.J., E.C.J.-L., T.M., A.M., N.M., D.J.M., S. Panaiotov, I.P., C.P., J. Perdigão, J.R., P.S., N.T.S., F.A.S., C.S., E.d.O.S., E.M.S., P.V.H., M.V. and R.M.W. undertook sample collection, DNA extraction, genotyping and phenotypic drug resistance testing. G.A.H.-C., M.B.N., M.A., Z.R. and S. Ali prepared libraries for Illumina sequencing. J. Parkhill led the generation of Malawian and Ugandan sequencing data. F.C. and J. Phelan performed bioinformatic and statistical analyses under the supervision of T.G.C. S. Portelli and Y.O. performed additional confirmatory analysis under the supervision of M.L.H., N.F. and T.G.C. F.C., J. Phelan, S. Portelli, S.C., N.F., M.L.H., R.M., A.P. and T.G.C. interpreted results. F.C., J. Phelan, R.M. and T.G.C. wrote the first draft of the manuscript. All authors commented to and edited various versions of the draft manuscript. F.C., J. Phelan, R.M. and T.G.C. compiled the final manuscript. All authors approved the final manuscript.

Corresponding authors

Correspondence to Ruth McNerney, Arnab Pain or Taane G. Clark.

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Integrated supplementary information

Supplementary Figure 1

Allele frequency spectra for SNPs (left) and small indels (right)

Supplementary Figure 2

Principal-component analysis confirms lineage- and sublineage-based population structure (total variation explained across five components is 82.7%)

Supplementary Figure 3

Protein structure for Alr Alanine racemase mutational map showing the position and effect of mutations based on measure of protein stability by DUET. Unfavourable mutations are depicted in blue and favourable mutations are depicted in red, where color intensity reflects the extent of effect. The PLP cofactor is shown as a stick representation in green. a, The protomer structure of alanine racemase depicted as a cartoon with the PLP cofactor shown as sticks. b, The active site with residues that have been identified in the GWAS depicted as sticks and their hydrogen bonding propensity shown as dashed black lines

Supplementary Figure 4

Polymorphisms in regions surrounding ethA (top left), thyA (top right), pncA (bottom left) and katG (bottom right) using the complete dataset (n = 6,465) The top panel shows the density of SNPs per kilobase (green, nonsynonymous; black, all). The red crosses show the location of the small indels. The middle panel shows the location of the large deletions found in samples used in this study. The lower panel shows the location of the candidate regions and flanking genes.

Supplementary Figure 5

The analytical workflow, including procedures adopted for raw sequence data processing and the GWAS approach

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Tables 1–9

Life Sciences Reporting Summary

Supplementary Data 1

List of mutations found in candidate genes. A list of mutations found in drug resistance candidate genes. Mutations are separated by low/high frequency and bolded if they were significantly associated through GWAS or PhyC.

Supplementary Data 2

ENA accessions and DST phenotypes. A list of all in-house strains sequenced for this study with ENA accessions and DST phenotypes.

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Coll, F., Phelan, J., Hill-Cawthorne, G.A. et al. Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis. Nat Genet 50, 307–316 (2018). https://doi.org/10.1038/s41588-017-0029-0

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