Antibiotics pp 331-356 | Cite as

Epidemiological Surveillance and Typing Methods to Track Antibiotic Resistant Strains Using High Throughput Sequencing

  • Miguel Paulo Machado
  • Bruno Ribeiro-Gonçalves
  • Mickael Silva
  • Mário Ramirez
  • João André Carriço
Part of the Methods in Molecular Biology book series (MIMB, volume 1520)


High-Throughput Sequencing (HTS) technologies transformed the microbial typing and molecular epidemiology field by providing the cost-effective ability for researchers to probe draft genomes, not only for epidemiological markers but also for antibiotic resistance and virulence determinants. In this chapter, we provide protocols for the analysis of HTS data for the determination of multilocus sequence typing (MLST) information and for determining presence or absence of antibiotic resistance genes.

Key words

High-throughput sequencing Microbial typing Gene finding Online databases Antibiotic resistance 


  1. 1.
    Courvalin P (2016) Why is antibiotic resistance a deadly emerging disease? Clin Microbiol InfectGoogle Scholar
  2. 2.
    Struelens MJ (1996) Consensus guidelines for appropriate use and evaluation of microbial epidemiologic typing systems. Clin Microbiol Infect 2:2–11CrossRefPubMedGoogle Scholar
  3. 3.
    Baggesen DL, Sorensen G, Nielsen EM et al (2010) Phage typing of Salmonella Typhimurium-is it still a useful tool for surveillance and outbreak investigation. Euro Surveill 15(4):19471PubMedGoogle Scholar
  4. 4.
    Maiden MC, Bygraves JA, Feil EJ et al (1998) Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. Proc Natl Acad Sci U S A 95:3140–3145CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Jolley KA, Maiden MCJ (2010) BIGSdb: scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics 11:595CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Feil EJ, Li B, Aanensen D et al (2004) eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data. J Bacteriol 186:1518–1530CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Francisco AP, Bugalho M, Ramirez M et al (2009) Global optimal eBURST analysis of multilocus typing data using a graphic matroid approach. BMC Bioinformatics 10:152CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Francisco AP, Vaz C, Monteiro PT et al (2012) PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods. BMC Bioinformatics 13:87CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Corander J, Waldmann P, Marttinen P et al (2004) BAPS 2: enhanced possibilities for the analysis of genetic population structure. Bioinformatics 20:2363–2369CrossRefPubMedGoogle Scholar
  10. 10.
    Thomas JC, Robinson DA (2014) Multilocus sequence typing of Staphylococcus epidermidis. Methods Mol Biol 1106:61–69CrossRefPubMedGoogle Scholar
  11. 11.
    Koreen L, Ramaswamy SV, Graviss EA et al (2004) spa typing method for discriminating among Staphylococcus aureus isolates: implications for use of a single marker to detect genetic micro- and macrovariation. J Clin Microbiol 42:792–799CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Beall B, Facklam R, Thompson T (1996) Sequencing emm-specific PCR products for routine and accurate typing of group A streptococci. J Clin Microbiol 34:953PubMedPubMedCentralGoogle Scholar
  13. 13.
    Lindstedt B (2005) Multiple‐locus variable number tandem repeats analysis for genetic fingerprinting of pathogenic bacteria. Electrophoresis 26(13):2567–2582CrossRefPubMedGoogle Scholar
  14. 14.
    Sabat AJ, Budimir A, Nashev D et al (2013) Overview of molecular typing methods for outbreak detection and epidemiological surveillance. Euro Surveill 18:17–30Google Scholar
  15. 15.
    Carrico JA, Sabat AJ, Friedrich AW et al (2013) Bioinformatics in bacterial molecular epidemiology and public health: databases, tools and the next-generation sequencing revolution. Euro Surveill 18:20382PubMedGoogle Scholar
  16. 16.
    Loman NJ, Constantinidou C, Chan JZM et al (2012) High-throughput bacterial genome sequencing: an embarrassment of choice, a world of opportunity. Nat Rev Microbiol 10:599–606CrossRefPubMedGoogle Scholar
  17. 17.
    Loman NJ, Misra RV, Dallman TJ et al (2012) Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol 30:434–439CrossRefPubMedGoogle Scholar
  18. 18.
    Jünemann S, Sedlazeck FJ, Prior K et al (2013) Updating benchtop sequencing performance comparison. Nat Biotechnol 31:294–296CrossRefPubMedGoogle Scholar
  19. 19.
    Jünemann S, Prior K, Albersmeier A et al (2014) GABenchToB: a genome assembly benchmark tuned on bacteria and benchtop sequencers. PLoS One 9:e107014CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Martin JA, Wang Z (2011) Next-generation transcriptome assembly. Nat Rev Genet 12:671–682CrossRefPubMedGoogle Scholar
  21. 21.
    Seemann T (2014) Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068–2069CrossRefPubMedGoogle Scholar
  22. 22.
    Maiden MCJ, van Rensburg MJJ, Bray JE et al (2013) MLST revisited: the gene-by-gene approach to bacterial genomics. Nat Rev Microbiol 11:728–736CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hatem A, Bozdağ D, Toland AE et al (2013) Benchmarking short sequence mapping tools. BMC Bioinformatics 14:184CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chewapreecha C, Marttinen P, Croucher NJ et al (2014) Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes. PLoS Genet 10:e1004547CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Harris SR, Feil EJ, Holden MTG et al (2010) Evolution of MRSA during hospital transmission and intercontinental spread. Science 327:469–474CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Gardy JL, Johnston JC, Sui SJH et al (2011) Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N Engl J Med 364:730–739CrossRefPubMedGoogle Scholar
  27. 27.
    Medini D, Donati C, Tettelin H et al (2005) The microbial pan-genome. Curr Opin Genet Dev 15:589–594CrossRefPubMedGoogle Scholar
  28. 28.
    McArthur AG, Waglechner N, Nizam F et al (2013) The comprehensive antibiotic resistance database. Antimicrob Agents Chemother 57:3348–3357CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Liu B, Pop M (2009) ARDB—antibiotic resistance genes database. Nucleic Acids Res 37:D443–D447CrossRefPubMedGoogle Scholar
  30. 30.
    Zankari E, Hasman H, Cosentino S et al (2012) Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 67:2640–2644CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Thai QK, Bös F, Pleiss J (2009) The Lactamase Engineering Database: a critical survey of TEM sequences in public databases. BMC Genomics 10:390CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Croucher NJ, Finkelstein JA, Pelton SI et al (2013) Population genomics of post-vaccine changes in pneumococcal epidemiology. Nat Genet 45:656–663CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Leopold SR, Goering RV, Witten A et al (2014) Bacterial whole genome sequencing revisited: portable, scalable and standardized analysis for typing and detection of virulence and antibiotic resistance genes. J Clin Microbiol 52(7):2365–2370CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Bankevich A, Nurk S, Antipov D et al (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Larsen MV, Cosentino S, Rasmussen S et al (2012) Multilocus sequence typing of total-genome-sequenced bacteria. J Clin Microbiol 50:1355–1361CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Camacho C, Coulouris G, Avagyan V et al (2009) BLAST plus: architecture and applications. BMC Bioinformatics 10:421CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    CLSI. Performance standards for antimicrobial susceptibility testing; Eighteenth Informational Supplement. CLSI document M100-S18. Wayne, PA: Clinical and Laboratory Standards Institute; 2008Google Scholar
  38. 38.
    Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Quinlan AR (2014) BEDTools: the Swiss‐army tool for genome feature analysis. Wiley, Hoboken, NJ, USAGoogle Scholar
  41. 41.
    Van der Auwera GA, Carneiro MO, Hartl C et al (2013) From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics 11:11.10.1–11.10.33Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Miguel Paulo Machado
    • 1
  • Bruno Ribeiro-Gonçalves
    • 1
  • Mickael Silva
    • 1
  • Mário Ramirez
    • 1
  • João André Carriço
    • 1
  1. 1.Instituto de Microbiologia, Instituto de Medicina Molecular, Faculdade de MedicinaUniversidade de Lisboa, Alameda Da UniversidadeLisbonPortugal

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