1887

Abstract

Whole-genome sequencing (WGS) of (MTB) isolates can be used to get an accurate diagnosis, to guide clinical decision making, to control tuberculosis (TB) and for outbreak investigations. We evaluated the performance of long-read (LR) and/or short-read (SR) sequencing for anti-TB drug-resistance prediction using the TBProfiler and Mykrobe tools, the fraction of genome recovery, assembly accuracies and the robustness of two typing approaches based on core-genome SNP (cgSNP) typing and core-genome multi-locus sequence typing (cgMLST). Most of the discrepancies between phenotypic drug-susceptibility testing (DST) and drug-resistance prediction were observed for the first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol, mainly with LR sequence data. Resistance prediction to second-line drugs made by both TBProfiler and Mykrobe tools with SR- and LR-sequence data were in complete agreement with phenotypic DST except for one isolate. The SR assemblies were more accurate than the LR assemblies, having significantly (<0.05) fewer indels and mismatches per 100 kbp. However, the hybrid and LR assemblies had slightly higher genome fractions. For LR assemblies, Canu followed by Racon, and Medaka polishing was the most accurate approach. The cgSNP approach, based on either reads or assemblies, was more robust than the cgMLST approach, especially for LR sequence data. In conclusion, anti-TB drug-resistance prediction, particularly with only LR sequence data, remains challenging, especially for first-line drugs. In addition, SR assemblies appear more accurate than LR ones, and reproducible phylogeny can be achieved using cgSNP approaches.

Funding
This study was supported by the:
  • Stichting Beatrixoord Noord-Nederland
    • Principle Award Recipient: NotApplicable
  • H2020-MSCA-COFUND-2015 (Award 713660)
    • Principle Award Recipient: LeonardSchuele
  • H2020-MSCA-COFUND-2015 (Award 713660)
    • Principle Award Recipient: NilayPeker
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000695
2021-11-26
2024-05-06
Loading full text...

Full text loading...

/deliver/fulltext/mgen/7/11/mgen000695.html?itemId=/content/journal/mgen/10.1099/mgen.0.000695&mimeType=html&fmt=ahah

References

  1. World Health Organization Global Tuberculosis Report 2019. n.d https://www.who.int/publications/i/item/9789241565714
  2. World Health Organization The use of next-generation sequencing technologies for the detection of mutations associated with drug resistance in Mycobacterium tuberculosis complex: technical guide. n.d https://apps.who.int/iris/bitstream/handle/10665/274443/WHO-CDS-TB-2018.19-eng.pdf
  3. Chedore P, Bertucci L, Wolfe J, Sharma M, Jamieson F. Potential for erroneous results indicating resistance when using the bactec mgit 960 system for testing susceptibility of mycobacterium tuberculosis to pyrazinamide. J Clin Microbiol 2010; 48:300–301 [View Article] [PubMed]
    [Google Scholar]
  4. Sanchez-Padilla E, Merker M, Beckert P, Jochims F, Dlamini T et al. Detection of Drug-Resistant Tuberculosis by Xpert MTB/RIF in Swaziland. N Engl J Med 2015; 372:1181–1182 [View Article] [PubMed]
    [Google Scholar]
  5. Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C et al. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 1998; 393:537–544 [View Article] [PubMed]
    [Google Scholar]
  6. 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]
    [Google Scholar]
  7. Meehan CJ, Goig GA, Kohl TA, Verboven L, Dippenaar A et al. Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. Nat Rev Microbiol 2019; 17:533–545 [View Article] [PubMed]
    [Google Scholar]
  8. Votintseva AA, Bradley P, Pankhurst L, Elias C del O, Loose M et al. Same-Day diagnostic and surveillance data for tuberculosis via whole-genome sequencing of direct respiratory samples. J Clin Microbiol 2017; 55:1285–1298 [View Article] [PubMed]
    [Google Scholar]
  9. Smith C, Halse TA, Shea J, Modestil H, Fowler RC et al. Assessing Nanopore Sequencing for Clinical Diagnostics: a Comparison of Next-Generation Sequencing (NGS) Methods for Mycobacterium tuberculosis. J Clin Microbiol 2020; 59:20e00583 [View Article]
    [Google Scholar]
  10. Kendall EA, Fofana MO, Dowdy DW. Burden of transmitted multidrug resistance in epidemics of tuberculosis: a transmission modelling analysis. Lancet Respir Med 2015; 3:963–972 [View Article] [PubMed]
    [Google Scholar]
  11. Nardell E, Volchenkov G. Tuberculosis transmission control: a refocused approach. In Tuberculosis Sheffield: ERS Monograph; 2018 pp 364–380
    [Google Scholar]
  12. Phelan JE, O’Sullivan DM, Machado D, Ramos J, Oppong YEA et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Med 2019; 11:41 [View Article] [PubMed]
    [Google Scholar]
  13. Coll F, McNerney R, Preston MD, Guerra-Assunção JA, Warry A et al. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences. Genome Med 2015; 7:51 [View Article] [PubMed]
    [Google Scholar]
  14. Bradley P, Gordon NC, Walker TM, Dunn L, Heys S et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun 2015; 6:1–15 [View Article]
    [Google Scholar]
  15. Hunt M, Bradley P, Lapierre SG, Heys S, Thomsit M et al. Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe. Wellcome Open Res 2019; 4:191 [View Article] [PubMed]
    [Google Scholar]
  16. Kohl TA, Utpatel C, Schleusener V, Filippo MRD, Beckert P et al. MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates. PeerJ 2018; 6:e5895 [View Article] [PubMed]
    [Google Scholar]
  17. Kohl TA, Diel R, Harmsen D, Rothgänger J, Walter KM et al. Whole-genome-based Mycobacterium tuberculosis surveillance: a standardized, portable, and expandable approach. J Clin Microbiol 2014; 52:2479–2486 [View Article] [PubMed]
    [Google Scholar]
  18. Kohl TA, Harmsen D, Rothgänger J, Walker T, Diel R et al. Harmonized genome wide typing of tubercle bacilli using a web-based gene-by-gene nomenclature system. EBioMedicine 2018; 34:131–138 [View Article] [PubMed]
    [Google Scholar]
  19. Ridderberg W, Strino F, Ettenhuber P, Materna A. High-resolution outbreak tracing and resistance detection using whole genome sequencing in the case of a mycobacterium tuberculosis outbreak. https://digitalinsights.qiagen.com/wp-content/uploads/2020/02/PROM-11373-001_1108257_WP_BIOX_CLC_GW_TB_0917_WW.pdf
  20. Muwonge A, Malama S, Johansen TB, Kankya C, Biffa D et al. Molecular epidemiology, drug susceptibility and economic aspects of tuberculosis in mubende district, uganda. PLoS ONE 2013; 8:e64745 [View Article] [PubMed]
    [Google Scholar]
  21. Jajou R, Kohl TA, Walker T, Norman A, Cirillo DM et al. Towards standardisation: comparison of five whole genome sequencing (WGS) analysis pipelines for detection of epidemiologically linked tuberculosis cases. Euro Surveill 2019; 24:1900130 [View Article]
    [Google Scholar]
  22. Phelan J, O’Sullivan DM, Machado D, Ramos J, Whale AS et al. The variability and reproducibility of whole genome sequencing technology for detecting resistance to anti-tuberculous drugs. Genome Med 2016; 8:132 [View Article] [PubMed]
    [Google Scholar]
  23. Schleusener V, Köser CU, Beckert P, Niemann S, Feuerriegel S. Mycobacterium tuberculosis resistance prediction and lineage classification from genome sequencing: comparison of automated analysis tools. Sci Rep 2017; 7:46327 [View Article] [PubMed]
    [Google Scholar]
  24. Macedo R, Nunes A, Portugal I, Duarte S, Vieira L et al. Dissecting whole-genome sequencing-based online tools for predicting resistance in Mycobacterium tuberculosis: can we use them for clinical decision guidance?. Tuberculosis 2018; 110:44–51 [View Article] [PubMed]
    [Google Scholar]
  25. Mahé P, El Azami M, Barlas P, Tournoud M. A large scale evaluation of TBProfiler and Mykrobe for antibiotic resistance prediction in Mycobacterium tuberculosis. PeerJ 20197 7: [View Article] [PubMed]
    [Google Scholar]
  26. van Beek J, Haanperä M, Smit PW, Mentula S, Soini H. Evaluation of whole genome sequencing and software tools for drug susceptibility testing of Mycobacterium tuberculosis. Clin Microbiol Infect 2019; 25:82–86 [View Article] [PubMed]
    [Google Scholar]
  27. Ngo T. M, Teo Y. Y. Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms. BMC Bioinformatics 2019; 20:68
    [Google Scholar]
  28. Tafess K, Ng TTL, Lao HY, Leung KSS, Tam KKG et al. Targeted-sequencing workflows for comprehensive drug resistance profiling of mycobacterium tuberculosis cultures using two commercial sequencing platforms: Comparison of analytical and diagnostic performance, turnaround time, and cost. Clin Chem 2020; 66:809–820 [View Article]
    [Google Scholar]
  29. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
    [Google Scholar]
  30. De Coster W, D’Hert S, Schultz DT, Cruts M, Van Broeckhoven C. NanoPack: visualizing and processing long-read sequencing data. Bioinforma Oxf Engl 2018; 34:2666–2669 [View Article]
    [Google Scholar]
  31. Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJM et al. ABySS: A parallel assembler for short read sequence data. Genome Res 2009; 19:1117–1123 [View Article] [PubMed]
    [Google Scholar]
  32. Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A et al. Assembling genomes and mini-metagenomes from highly chimeric reads. Deng M, Jiang R, Sun F, Zhang X. eds In Research in Computational Molecular Biology Berlin Heidelberg: Springer; 2013 pp 158–170
    [Google Scholar]
  33. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008; 18:821–829 [View Article] [PubMed]
    [Google Scholar]
  34. Antipov D, Korobeynikov A, McLean JS, Pevzner PA. hybridSPAdes: an algorithm for hybrid assembly of short and long reads. Bioinformatics 2016; 32:1009–1015 [View Article] [PubMed]
    [Google Scholar]
  35. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLOS Comput Biol 2017; 13:e1005595 [View Article] [PubMed]
    [Google Scholar]
  36. Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 2017; 27:722–736 [View Article] [PubMed]
    [Google Scholar]
  37. Lin Y, Yuan J, Kolmogorov M, Shen MW, Chaisson M et al. Assembly of long error-prone reads using de Bruijn graphs. Proc Natl Acad Sci U S A 2016; 113:E8396–405 [View Article] [PubMed]
    [Google Scholar]
  38. Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol 2019; 37:540–546 [View Article] [PubMed]
    [Google Scholar]
  39. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 2013; 29:1072–1075 [View Article] [PubMed]
    [Google Scholar]
  40. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 2018; 34:3094–3100 [View Article] [PubMed]
    [Google Scholar]
  41. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 2011; 27:2987–2993 [View Article] [PubMed]
    [Google Scholar]
  42. Paradis E, Claude J, Strimmer K. APE: Analyses of phylogenetics and evolution in r language. Bioinformatics 2004; 20:289–290 [View Article] [PubMed]
    [Google Scholar]
  43. Galili T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 2015; 31:3718–3720 [View Article] [PubMed]
    [Google Scholar]
  44. Wei T, Simko V, Levy M, Xie Y, Jin Y et al. corrplot: Visualization of a correlation matrix; 2017 https://CRAN.R-project.org/package=corrplot
  45. Wilkinson S, phylogram DS. Dendrograms for evolutionary analysis; 2018 https://CRAN.R-project.org/package=phylogram
  46. MATLAB cophenet - cophenetic correlation coefficient. https://www.mathworks.com/help/stats/cophenet.html
  47. CRyPTIC Consortium and the 100,000 Genomes Project Allix-Béguec C, Arandjelovic I, Bi L, Beckert P et al. Prediction of susceptibility to first-line tuberculosis drugs by dna sequencing. N Engl J Med 2018; 379:1403–1415 [View Article] [PubMed]
    [Google Scholar]
  48. Iwamoto T, Murase Y, Yoshida S, Aono A, Kuroda M et al. Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis. PLOS ONE 2019; 14:e0212798 [View Article] [PubMed]
    [Google Scholar]
  49. Shi D, Li L, Zhao Y, Jia Q, Li H et al. Characteristics of embB mutations in multidrug-resistant Mycobacterium tuberculosis isolates in Henan, China. J Antimicrob Chemother 2011; 66:2240–2247 [View Article] [PubMed]
    [Google Scholar]
  50. Sun Q, Xiao T, Liu H, Zhao X, Liu Z et al. Mutations within embCAB are associated with variable level of ethambutol resistance in mycobacterium tuberculosis isolates from china. Antimicrob Agents Chemother 2017; 62:e01279-17 [View Article] [PubMed]
    [Google Scholar]
  51. Goldstein S, Beka L, Graf J, Klassen JL. Evaluation of strategies for the assembly of diverse bacterial genomes using MinION long-read sequencing. BMC Genomics 2019; 20:23 [View Article] [PubMed]
    [Google Scholar]
  52. 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 Genomics 2018; 4:e000188
    [Google Scholar]
  53. Chen Z, Erickson DL, Meng J. Benchmarking hybrid assembly approaches for genomic analyses of bacterial pathogens using Illumina and Oxford Nanopore sequencing. BMC Genomics 2020; 21:631 [View Article]
    [Google Scholar]
  54. Heydari M, Miclotte G, Demeester P, Van de Peer Y, Fostier J. Evaluation of the impact of Illumina error correction tools on de novo genome assembly. BMC Bioinformatics 2017; 18:374 [View Article] [PubMed]
    [Google Scholar]
  55. Cohen KA, Manson AL, Desjardins CA, Abeel T, Earl AM. Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges. Genome Med 2019; 11:45 [View Article] [PubMed]
    [Google Scholar]
  56. Loman NJ, Quick J, Simpson JT. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat Methods 2015; 12:733–735 [View Article] [PubMed]
    [Google Scholar]
  57. Amarasinghe SL, Su S, Dong X, Zappia L, Ritchie ME et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol 2020; 21:30 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000695
Loading
/content/journal/mgen/10.1099/mgen.0.000695
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