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Evolution and transmission of drug-resistant tuberculosis in a Russian population

Nature Genetics volume 46, pages 279–286 (2014)Cite this article

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Abstract

The molecular mechanisms determining the transmissibility and prevalence of drug-resistant tuberculosis in a population were investigated through whole-genome sequencing of 1,000 prospectively obtained patient isolates from Russia. Two-thirds belonged to the Beijing lineage, which was dominated by two homogeneous clades. Multidrug-resistant (MDR) genotypes were found in 48% of isolates overall and in 87% of the major clades. The most common rpoB mutation was associated with fitness-compensatory mutations in rpoA or rpoC, and a new intragenic compensatory substitution was identified. The proportion of MDR cases with extensively drug-resistant (XDR) tuberculosis was 16% overall, with 65% of MDR isolates harboring eis mutations, selected by kanamycin therapy, which may drive the expansion of strains with enhanced virulence. The combination of drug resistance and compensatory mutations displayed by the major clades confers clinical resistance without compromising fitness and transmissibility, showing that, in addition to weaknesses in the tuberculosis control program, biological factors drive the persistence and spread of MDR and XDR tuberculosis in Russia and beyond.

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Figure 1: Coverage of the population of patients with tuberculosis.
Figure 2: Maximum-likelihood phylogeny of 1,035 M. tuberculosis isolates based on 32,445 variable sites.
Figure 3: Phylogenetic distribution of resistance-conferring and compensatory genotypes.
Figure 4: Prevalence of drug resistance mutations and association with lineage.
Figure 5: Distribution of rifampicin resistance and compensatory amino acid substitutions in the RNA polymerase subunits RpoA, RpoB and RpoC.
Figure 6: Transmissibility of drug resistance genotypes.

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Acknowledgements

We are grateful to members of the Public Health England National Mycobacterium Reference Laboratory and the Samara Regional Tuberculosis Laboratory for bacteriological work, particularly M. Stone, X. Gonzalo and A. Broda. We would also like to thank the Samara Tuberculosis Service, particularly I. Fedorin, as well as V. Kulichenko. We thank R. Hooper for expert statistical advice, S. Hoffner for bacteriological advice and S. Bentley for sequencing advice. This study was supported by European Union Framework Programme 7 (grant 201483; TB-EUROGEN), with sequencing funded by the Wellcome Trust (grant 098051) and EUROGEN. S.N. is a Wellcome Trust Senior Research Fellow in Basic Biomedical Science (095198/Z/10/Z) and is also supported by European Research Council Starting Grant 260477.

Author information

Authors and Affiliations

  1. Public Health England (PHE) National Mycobacterium Reference Laboratory, Clinical TB and HIV Group, Blizard Institute, Queen Mary University of London, London, UK

    Nicola Casali, Vladyslav Nikolayevskyy, Yanina Balabanova, Timothy Brown & Francis Drobniewski

  2. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    Simon R Harris, Josephine Bryant & Julian Parkhill

  3. Samara Oblast Tuberculosis Dispensary, Samara, Russian Federation,

    Olga Ignatyeva & Irina Kontsevaya

  4. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

    Jukka Corander

  5. Department of Medicine, University of Cambridge, Cambridge, UK

    Sergey Nejentsev

  6. Department of Molecular Medicine, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany

    Rolf D Horstmann

  7. Department of Infectious Diseases, Imperial College, London, UK

    Francis Drobniewski

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  1. Nicola Casali
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  2. Vladyslav Nikolayevskyy
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Contributions

N.C., V.N., Y.B. and F.D. designed the study. O.I., I.K., V.N. and Y.B. recruited patients and collected epidemiological data. O.I. and I.K. performed laboratory work. N.C., S.R.H. and J.C. conducted sequence analysis. N.C., T.B., V.N. and F.D. interpreted the data. Y.B., O.I. and F.D. performed statistical comparisons. N.C. and F.D. drafted the manuscript. T.B., V.N., Y.B., O.I., I.K., S.R.H., J.P., J.B., S.N. and R.D.H. provided critical analysis and reviewed the manuscript. All authors approved the final draft.

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Correspondence to Francis Drobniewski.

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Integrated supplementary information

Supplementary Figure 1 Distribution of Samara isolates relative to a global collection

The phylogeny in Figure 2 is illustrated with all clades that contained only Samaran or UK XDR isolates collapsed. The ancestral node of the Beijing East European sublineage is indicated with a star. Isolates from the UK, representing global diversity, are marked with white circles. The position of the reference sequence, H37Rv, is marked ‘R’.

Supplementary Figure 2 Population genetic analysis

Genetic structuring of the data was investigated using hierBAPS, which delineates the population using nested clustering. The estimated mode of the posterior distribution had 3, 7 and 11 clusters at levels 1–3 of the hierarchy, respectively. All clusters in the mode were significantly supported when compared against alternative partitions (posterior probability of any cluster at least 100-fold higher than for the alternative). Sublineages are defined in the text.

Supplementary Figure 3 Terminal branch lengths of Beijing versus Euro-American isolates

The number of SNPs between each isolate and its last common ancestor was determined from the phylogeny illustrated in Figure 2. The frequency of each distance was calculated and corrected for the number of isolates in the lineage. All branch lengths are shown in the main panel and those less than 10 SNPs in the inset.

Supplementary Figure 4 Distribution of sublineages across Samara

The geographic origin of the sequenced patient isolates is illustrated. Samara (a) is divided into regions: North (N), South (S), East (E), Southwest (SW) and West (W, West of the river Volga); cities: Samara City (Sm), Togliatti (T) and Syzran (Sz); and the region surrounding Samara City (Volzhskyy, Vl). Samara City (b) is divided into 9 districts.

Supplementary Figure 5 Geographic versus genetic distance between isolates

(a) The distance between the home addresses of patients sharing identical isolates. Where more than two isolates were identical, the distance to the geographically closest isolate is shown. (b) The number of SNPs separating isolates from patients sharing households.

Supplementary Figure 6 Distribution of rpoAC compensatory mutations in clades A and B

The phylogenies of clades A and B are depicted on the left of each panel. Colored bands represent different polymorphisms. The first column shows nsSNPs in the rifampicin resistance determining region of rpoB and the right two columns nsSNPs in rpoAC. The genotypes illustrated are provided in full in Supplementary Table 4. The ancestral node of the clade with a significant absence of rpoAC nsSNPs is marked with an arrow. The four SNPs on this branch are shown.

Supplementary Figure 7 Prevalence and co-occurrence of mutations conferring an XDR genotype

The number of isolates harboring genotypes conferring resistance to fluoroquinolones (green) or second-line injectables (blue) are depicted as empty circles. Filled circles show the number of isolates carrying more than one of these mutations. Red filled circles are XDR.

Supplementary Figure 8 Heterogeneity at the gyrA QRDR locus

Each graph represents an isolate with ambiguous base calls between codons 88 to 94 in GyrA. The proportion of reads with alternate basecalls at each site is shown. The isolate depicted in the top left graph had two alternate alleles at one site.

Supplementary Figure 9 Distribution of non-synonymous and nonsense SNPs in three highly variable genes implicated in drug resistance: PncA, GidB and EthA

The amino acid sequence of the reference is shown; amino acid substitutions are shown below the sequence and stop codons marked with an asterisk. In PncA, residues that comprise the iron binding site (D49, H51, H57, H71) and catalytic triad (C138, D8, K96) are underlined.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Tables 1–3 and 5–11 (PDF 3203 kb)

Supplementary Data Set 1

Maximum likelihood phylogeny of 1,035 M. tuberculosis isolates based on 32,445 variable sites (TXT 56 kb)

Supplementary Table 4

Polymorphisms at drug resistance loci (XLS 470 kb)

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Casali, N., Nikolayevskyy, V., Balabanova, Y. et al. Evolution and transmission of drug-resistant tuberculosis in a Russian population. Nat Genet 46, 279–286 (2014). https://doi.org/10.1038/ng.2878

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