1887

Abstract

Outbreaks of tuberculosis (TB) – such as the large isoniazid-resistant outbreak centred on London, UK, which originated in 1995 – provide excellent opportunities to model transmission of this devastating disease. Transmission chains for TB are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine-learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 21 transmission events with reasonable confidence, 9 of which have zero SNP distance, and a maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible TB transmitters.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000450
2020-11-11
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/mgen/6/11/mgen000450.html?itemId=/content/journal/mgen/10.1099/mgen.0.000450&mimeType=html&fmt=ahah

References

  1. Yang C, Luo T, Shen X, Wu J, Gan M et al. Transmission of multidrug-resistant Mycobacterium tuberculosis in Shanghai, China: a retrospective observational study using whole-genome sequencing and epidemiological investigation. Lancet Infect Dis 2017; 17:275–284 [View Article][PubMed]
    [Google Scholar]
  2. World Health Organization Standards and Benchmarks for Tuberculosis Surveillance and Vital Registration Systems: Checklist and User Guide Geneva: World Health organization; 2014
    [Google Scholar]
  3. Theron G, Jenkins HE, Cobelens F, Abubakar I, Khan AJ et al. Data for action: collection and use of local data to end tuberculosis. Lancet 2015; 386:2324–2333 [View Article][PubMed]
    [Google Scholar]
  4. Satta G, Lipman M, Smith GP, Arnold C, Kon OM et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential?. Clin Microbiol Infect 2018; 24:604–609 [View Article][PubMed]
    [Google Scholar]
  5. Campbell F, Strang C, Ferguson N, Cori A, Jombart T. When are pathogen genome sequences informative of transmission events?. PLoS Pathog 2018; 14:e1006885 [View Article][PubMed]
    [Google Scholar]
  6. Casali N, Broda A, Harris SR, Parkhill J, Brown T et al. Whole genome sequence analysis of a large isoniazid-resistant tuberculosis outbreak in London: a retrospective observational study. PLoS Med 2016; 13:e1002137-18 [View Article][PubMed]
    [Google Scholar]
  7. Colijn C, Gardy J. Phylogenetic tree shapes resolve disease transmission patterns. Evol Med Public Health 2014; 2014:96–108 [View Article][PubMed]
    [Google Scholar]
  8. Didelot X, Fraser C, Gardy J, Colijn C. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Mol Biol Evol 2017; 34:msw075 [View Article][PubMed]
    [Google Scholar]
  9. Maguire H, Brailsford S, Carless J, Yates M, Altass L et al. Large outbreak of isoniazid-monoresistant tuberculosis in London, 1995 to 2006: case-control study and recommendations. Euro Surveill 2011; 16:19830[PubMed]
    [Google Scholar]
  10. Neely F, Maguire H, Le Brun F, Davies A, Gelb D et al. High rate of transmission among contacts in large London outbreak of isoniazid mono-resistant tuberculosis. J Public Health 2010; 32:44–51 [View Article]
    [Google Scholar]
  11. Ruddy MC, Davies AP, Yates MD, Yates S, Balasegaram S. Outbreak of isoniazid resistant tuberculosis in North London. Thorax 2004; 59:279–285 [View Article]
    [Google Scholar]
  12. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol 2014; 10:e1003537 [View Article][PubMed]
    [Google Scholar]
  13. Rambaut A, Lam TT, Max Carvalho L, Pybus OG. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol 2016; 2:vew007 [View Article][PubMed]
    [Google Scholar]
  14. Didelot X. Computational methods in microbial population genomics. Population Genomics: Microorganisms New York: Springer; 2019 pp 3–29 [View Article]
    [Google Scholar]
  15. Brynildsrud OB, Pepperell CS, Suffys P, Grandjean L, Monteserin J et al. Global expansion of Mycobacterium tuberculosis lineage 4 shaped by colonial migration and local adaptation. Sci Adv 2018; 4:eaat5869 [View Article][PubMed]
    [Google Scholar]
  16. Möller S, du Plessis L, Stadler T. Impact of the tree prior on estimating clock rates during epidemic outbreaks. Proc Natl Acad Sci USA 2018; 115:4200–4205 [View Article][PubMed]
    [Google Scholar]
  17. Waddell PJ, Steel MA. General time-reversible distances with unequal rates across sites: mixing Γ and inverse Gaussian distributions with invariant sites. Mol Phylogenet Evol 1997; 8:398–414 [View Article]
    [Google Scholar]
  18. Nguyen L-T, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol 2015; 32:268–274 [View Article][PubMed]
    [Google Scholar]
  19. Rambaut A, Drummond A. Tracer: MCMC Trace Analysis Tool University of Oxford, UK; 2003
  20. Rambaut A, Drummond A. TreeAnnotator: MCMC Output Analysis Institute of Evolutionary Biology, University of Edinburgh, UK; 2002
  21. Jombart T, Kendall M, Almagro-Garcia J, Colijn C. TREESPACE: statistical exploration of landscapes of phylogenetic trees. Mol Ecol Resour 2017; 17:1385–1392 [View Article][PubMed]
    [Google Scholar]
  22. Didelot X. TransPhylo (version 1.3.2) 2017 https://github.com/xavierdidelot/TransPhylo
  23. Ayabina D, Ronning JO, Alfsnes K, Debech N, Brynildsrud OB et al. Genome-based transmission modelling separates imported tuberculosis from recent transmission within an immigrant population. Microb Genom 2018; 4:mgen.0.000219 [View Article][PubMed]
    [Google Scholar]
  24. Diel R, Rüsch-Gerdes S, Niemann S. Molecular epidemiology of tuberculosis among immigrants in Hamburg, Germany. J Clin Microbiol 2004; 42:2952–2960 [View Article][PubMed]
    [Google Scholar]
  25. Didelot X, Gardy J, Colijn C. Bayesian inference of infectious disease transmission from whole-genome sequence data. Mol Biol Evol 2014; 31:1869–1879 [View Article][PubMed]
    [Google Scholar]
  26. White PJ, Abubakar I. Improving control of tuberculosis in low-burden countries: insights from mathematical modeling. Front Microbiol 2016; 7:394 [View Article][PubMed]
    [Google Scholar]
  27. Behr MA, Edelstein PH, Ramakrishnan L. Revisiting the timetable of tuberculosis. BMJ 2018; 362:k2738 [View Article][PubMed]
    [Google Scholar]
  28. Roetzer A, Diel R, Kohl TA, Rückert C, Nübel U et al. Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiological study. PLoS Med 2013; 10:e1001387 [View Article][PubMed]
    [Google Scholar]
  29. Mathema B, Andrews JR, Cohen T, Borgdorff MW, Behr M et al. Drivers of tuberculosis transmission. J Infect Dis 2017; 216:S644–S653 [View Article][PubMed]
    [Google Scholar]
  30. Gardy JL, Loman NJ. Towards a genomics-informed, real-time, global pathogen surveillance system. Nat Rev Genet 2018; 19:9–20 [View Article][PubMed]
    [Google Scholar]
  31. Tang P, Gardy JL. Stopping outbreaks with real-time genomic epidemiology. Genome Med 2014; 6:104 [View Article][PubMed]
    [Google Scholar]
  32. Doyle RM, Burgess C, Williams R, Gorton R, Booth H et al. Direct whole-genome sequencing of sputum accurately identifies drug-resistant Mycobacterium tuberculosis faster than MGIT culture sequencing. J Clin Microbiol 2018; 56:e00666-18 [View Article][PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000450
Loading
/content/journal/mgen/10.1099/mgen.0.000450
Loading

Data & Media loading...

Supplements

Loading data from figshare Loading data from figshare
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error