1887

Abstract

Tuberculosis is a leading public health priority in eastern Malaysia. Knowledge of the genomic epidemiology of tuberculosis can help tailor public health interventions. Our aims were to determine tuberculosis genomic epidemiology and characterize resistance mutations in the ethnically diverse city of Kota Kinabalu, Sabah, located at the nexus of Malaysia, Indonesia, Philippines and Brunei. We used an archive of prospectively collected samples paired with epidemiological data. We collected sputum and demographic data from consecutive consenting outpatients with pulmonary tuberculosis at the largest tuberculosis clinic from 2012 to 2014, and selected samples from tuberculosis inpatients from the tertiary referral centre during 2012–2014 and 2016–2017. Two hundred and eight . sequences were available for analysis, representing 8 % of cases notified during the study periods. Whole-genome phylogenetic analysis demonstrated that most strains were lineage 1 (195/208, 93.8 %), with the remainder being lineages 2 (8/208, 3.8 %) or 4 (5/208, 2.4 %). Lineages or sub-lineages were not associated with patient ethnicity. The lineage 1 strains were diverse, with sub-lineage 1.2.1 being dominant (192, 98 %). Lineage 1.2.1.3 isolates were geographically most widely distributed. The greatest diversity occurred in a border town sub-district. The time to the most recent common ancestor for the three major lineage 1.2.1 clades was estimated to be the year 1966 (95 % HPD 1948–1976). An association was found between failure of culture conversion by week 8 of treatment and infection with lineage 2 (4/6, 67 %) compared with lineage 1 strains (4/83, 5 %) (<0.001), supporting evidence of greater virulence of lineage 2 strains. Eleven potential transmission clusters (SNP difference ≤12) were identified; at least five included people living in different sub-districts. Some linked cases spanned the whole 4-year study period. One cluster involved a multidrug-resistant tuberculosis strain matching a drug-susceptible strain from 3 years earlier. Drug resistance mutations were uncommon, but revealed one phenotype–genotype mismatch in a genotypically multidrug-resistant isolate, and rare nonsense mutations within the gene in two isolates. Consistent with the regionally mobile population, strains in Kota Kinabalu were diverse, although several lineage 1 strains dominated and were locally well established. Transmission clusters – uncommonly identified, likely attributable to incomplete sampling – showed clustering occurring across the community, not confined to households or sub-districts. The findings indicate that public health priorities should include active case finding and early institution of tuberculosis management in mobile populations, while there is a need to upscale effective contact investigation beyond households to include other contacts within social networks.

Funding
This study was supported by the:
  • National Health and Medical Research Council (Award 1142011)
    • Principle Award Recipient: AnnaP Ralph
  • National Health and Medical Research Council (Award 1131932)
    • Principle Award Recipient: AnnaP Ralph
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000573
2021-05-04
2024-04-20
Loading full text...

Full text loading...

/deliver/fulltext/mgen/7/5/mgen000573.html?itemId=/content/journal/mgen/10.1099/mgen.0.000573&mimeType=html&fmt=ahah

References

  1. World Health Organisation Global tuberculosis report 2016. who library Cataloguing-in-Publication data. WHO/HTM/TB/2016.13. http://apps.who.int/iris/bitstream/10665/137094/1/9789241564809_eng.pdf?ua=1
  2. Dony JF, Ahmad J, Khen Tiong Y. Epidemiology of tuberculosis and leprosy, Sabah, Malaysia. Tuberculosis 2004; 84:8–18 [View Article][PubMed]
    [Google Scholar]
  3. World Health Organisation Tuberculosis country profiles. https://www.who.int/tb/country/data/profiles/en/
  4. Rashid Ali MRS, Parameswaran U, William T, Bird E, Wilkes CS et al. A prospective study of tuberculosis drug susceptibility in Sabah, Malaysia, and an algorithm for management of isoniazid resistance. J Trop Med 2015; 2015:1–8 [View Article][PubMed]
    [Google Scholar]
  5. William T, Parameswaran U, Lee WK, Yeo TW, Anstey NM et al. Pulmonary tuberculosis in outpatients in Sabah, Malaysia: advanced disease but low incidence of HIV co-infection. BMC Infect Dis 2015; 15:32 [View Article][PubMed]
    [Google Scholar]
  6. Muhammad Redzwan SRA, Ralph AP, Sivaraman Kannan KK, William T. Individualised second line anti-tuberculous therapy for an extensively resistant pulmonary tuberculosis (XDR PTB) in East Malaysia. Med J Malaysia 2015; 70:200–204[PubMed]
    [Google Scholar]
  7. Dale JW, Nor RM, Ramayah S, Tang TH, Zainuddin ZF. Molecular epidemiology of tuberculosis in Malaysia. J Clin Microbiol 1999; 37:1265–1268 [View Article][PubMed]
    [Google Scholar]
  8. Rashid Ali MRS, Parameswaran U, William T, Bird E, Wilkes CS et al. A prospective study of mycobacterial viability in refrigerated, unpreserved sputum batched for up to 8 weeks. Int J Tuberc Lung Dis 2015; 19:620–621 [View Article][PubMed]
    [Google Scholar]
  9. Ralph AP, Rashid Ali MRS, William T, Piera K, Parameswaran U et al. Vitamin D and activated vitamin D in tuberculosis in equatorial Malaysia: a prospective clinical study. BMC Infect Dis 2017; 17:312 [View Article][PubMed]
    [Google Scholar]
  10. Bainomugisa A, Pandey S, Donnan E, Simpson G, Foster J'Belle et al. Cross-Border movement of highly drug-resistant Mycobacterium tuberculosis from Papua New Guinea to Australia through Torres Strait protected zone, 2010-2015. Emerg Infect Dis 2019; 25:406–415 [View Article][PubMed]
    [Google Scholar]
  11. Bainomugisa A, Duarte T, Lavu E, Pandey S, Coulter C et al. A complete high-quality MinION nanopore assembly of an extensively drug-resistant Mycobacterium tuberculosis Beijing lineage strain identifies novel variation in repetitive PE/PPE gene regions. Microb Genom 2018; 4: 15 06 2018 [View Article][PubMed]
    [Google Scholar]
  12. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article][PubMed]
    [Google Scholar]
  13. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009; 25:1754–1760 [View Article][PubMed]
    [Google Scholar]
  14. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The sequence Alignment/Map format and SAMtools. Bioinformatics 2009; 25:2078–2079 [View Article][PubMed]
    [Google Scholar]
  15. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010; 20:1297–1303 [View Article][PubMed]
    [Google Scholar]
  16. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 2012; 6:80–92 [View Article][PubMed]
    [Google Scholar]
  17. Merker M, Barbier M, Cox H, Rasigade J-P, Feuerriegel S et al. Compensatory evolution drives multidrug-resistant tuberculosis in Central Asia. elife 2018; 7:e38200 30 10 2018 [View Article][PubMed]
    [Google Scholar]
  18. Brown TS, Challagundla L, Baugh EH, Omar SV, Mustaev A et al. Pre-detection history of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa. Proc Natl Acad Sci U S A 2019; 116:23284–23291 [View Article][PubMed]
    [Google Scholar]
  19. Bainomugisa A, Lavu E, Hiashiri S, Majumdar S, Honjepari A et al. Multi-clonal evolution of multi-drug-resistant/extensively drug-resistant Mycobacterium tuberculosis in a high-prevalence setting of Papua New Guinea for over three decades. Microb Genom 2018; 4: [View Article]
    [Google Scholar]
  20. Cirillo DM, Miotto P, Tagliani E, ReSeq TBC. ReSeqTB Consortium Reaching consensus on drug resistance conferring mutations. Int J Mycobacteriol 2016; 5 Suppl 1:S33 [View Article][PubMed]
    [Google Scholar]
  21. Coll F, McNerney R, Guerra-Assunção JA, Glynn JR, Perdigão J et al. A robust SNP barcode for typing Mycobacterium tuberculosis complex strains. Nat Commun 2014; 5:4812 [View Article][PubMed]
    [Google Scholar]
  22. Palittapongarnpim P, Ajawatanawong P, Viratyosin W, Smittipat N, Disratthakit A et al. Evidence for host-bacterial co-evolution via genome sequence analysis of 480 Thai Mycobacterium tuberculosis lineage 1 isolates. Sci Rep 2018; 8:11597 [View Article][PubMed]
    [Google Scholar]
  23. Stamatakis A, Hoover P, Rougemont J. A rapid bootstrap algorithm for the RAxML web servers. Syst Biol 2008; 57:758–771 [View Article][PubMed]
    [Google Scholar]
  24. Drummond AJ, Suchard MA, Xie D, Rambaut A. Bayesian phylogenetics with BEAUti and the beast 1.7. Mol Biol Evol 2012; 29:1969–1973 [View Article][PubMed]
    [Google Scholar]
  25. BEAST developers Using bets to evaluate temporal signal; 2021
  26. Duchene S, Lemey P, Stadler T, Ho SYW, Duchene DA et al. Bayesian evaluation of temporal signal in measurably evolving populations. Mol Biol Evol 2020; 37:3363–3379 [View Article][PubMed]
    [Google Scholar]
  27. Walker TM, Ip CLC, Harrell RH, Evans JT, Kapatai G et al. Whole-Genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect Dis 2013; 13:137–146 [View Article][PubMed]
    [Google Scholar]
  28. Menardo F, Duchêne S, Brites D, Gagneux S. The molecular clock of Mycobacterium tuberculosis. PLoS Pathog 2019; 15:e1008067 [View Article][PubMed]
    [Google Scholar]
  29. Baele G, Li WLS, Drummond AJ, Suchard MA, Lemey P. Accurate model selection of relaxed molecular clocks in Bayesian phylogenetics. Mol Biol Evol 2013; 30:239–243 [View Article][PubMed]
    [Google Scholar]
  30. Paradis E, Schliep K. Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2019; 35:526–528 [View Article][PubMed]
    [Google Scholar]
  31. Stimson J, Gardy J, Mathema B, Crudu V, Cohen T et al. Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions. Mol Biol Evol 2019; 36:587–603 [View Article][PubMed]
    [Google Scholar]
  32. R Core Team R: a language and environment for satistical computing. R foundation for statistical computing. https://www.R-project.org/
  33. 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]
  34. Ford CB, Lin PL, Chase MR, Shah RR, Iartchouk O et al. Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat Genet 2011; 43:482–486 [View Article][PubMed]
    [Google Scholar]
  35. Lowbridge C, Fadhil SAM, Krishnan GD, Schimann E, Karuppan RM et al. How can gastro-intestinal tuberculosis diagnosis be improved? A prospective cohort study. BMC Infect Dis 2020; 20:255 [View Article][PubMed]
    [Google Scholar]
  36. Goroh MMD, van den Boogaard CHA, Ibrahim MY, Tha NO, Swe S et al. Factors affecting participation of local residents and migrants in tuberculosis contact investigation in a low-income, high-burden setting. Trop Med Infect Dis 2020accepted
    [Google Scholar]
  37. Ministry of Health Malaysia Manual Sistem Maklumat Tibi Kebangsaan (National Tuberculosis Information System [TBIS] Manual); 2002
  38. World Health Organisation Treatment of tuberculosis Guidelines Fourth edition WHO/HTM/TB/2009.420 Geneva, Switzerland: WHO; 2010.
    [Google Scholar]
  39. Visser ME, Stead MC, Walzl G, Warren R, Schomaker M et al. Baseline predictors of sputum culture conversion in pulmonary tuberculosis: importance of cavities, smoking, time to detection and W-Beijing genotype. PLoS One 2012; 7:e29588 [View Article][PubMed]
    [Google Scholar]
  40. Parwati I, van Crevel R, van Soolingen D. Possible underlying mechanisms for successful emergence of the Mycobacterium tuberculosis Beijing genotype strains. Lancet Infect Dis 2010; 10:103–111 [View Article][PubMed]
    [Google Scholar]
  41. Miotto P, Cabibbe AM, Borroni E, Degano M, Cirillo DM. Role of Disputed Mutations in the rpoB Gene in Interpretation of Automated Liquid MGIT Culture Results for Rifampin Susceptibility Testing of Mycobacterium tuberculosis. J Clin Microbiol 2018; 56: 25 04 2018 [View Article][PubMed]
    [Google Scholar]
  42. WHO Latent Tuberculosis Infection: Updated and Consolidated Guidelines for Programmatic Management Geneva: WHO Press; 2018
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000573
Loading
/content/journal/mgen/10.1099/mgen.0.000573
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF
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