A Transcriptomic Approach to Identify Novel Drug Efflux Pumps in Bacteria

  • Liping Li
  • Sasha G. Tetu
  • Ian T. Paulsen
  • Karl A. Hassan
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1700)

Abstract

The core genomes of most bacterial species include a large number of genes encoding putative efflux pumps. The functional roles of most of these pumps are unknown, however, they are often under tight regulatory control and expressed in response to their substrates. Therefore, one way to identify pumps that function in antimicrobial resistance is to examine the transcriptional responses of efflux pump genes to antimicrobial shock. By conducting complete transcriptomic experiments following antimicrobial shock treatments, it may be possible to identify novel drug efflux pumps encoded in bacterial genomes. In this chapter we describe a complete workflow for conducting transcriptomic analyses by RNA sequencing, to determine transcriptional changes in bacteria responding to antimicrobials.

Key words

Multidrug efflux Drug resistance Transcriptomics RNA-Seq Gene expression 

Notes

Acknowledgements

This work was supported by a Macquarie University International Research Excellence Scholarship to L.L., an Australian Research Council Discovery Early Career Research Fellowship to S.G.T. (DE150100009), an Australian National Health and Medical Research Council Project Grant to I.T.P. and K.A.H. (1060895), and a Macquarie University Research Development Grant to K.A.H. (9201401563).

References

  1. 1.
    Kroeger JK, Hassan K, Vörös A et al (2015) Bacillus cereus efflux protein BC3310 – a multidrug transporter of the unknown major facilitator family, UMF-2. Front Microbiol 6:1063CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Piddock LJ (2006) Multidrug-resistance efflux pumps – not just for resistance. Nat Rev Microbiol 4:629–636CrossRefPubMedGoogle Scholar
  3. 3.
    McMurry LM, Petrucci RE Jr, Levy SB (1980) Active efflux of tetracycline encoded by four genetically different tetracycline resistance determinants in Escherichia coli. Proc Natl Acad Sci U S A 77:3974–3977CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Tennent JM, Lyon BR, Gillespie MT et al (1985) Cloning and expression of Staphylococcus aureus plasmid-mediated quaternary ammonium resistance in Escherichia coli. Antimicrob Agents Chemother 27:79–83CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Ubukata K, Itoh-Yamashita N, Konno M (1989) Cloning and expression of the norA gene for fluoroquinolone resistance in Staphylococcus aureus. Antimicrob Agents Chemother 33:1535–1539CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Edgar R, Bibi E (1997) MdfA, an Escherichia coli multidrug resistance protein with an extraordinarily broad spectrum of drug recognition. J Bacteriol 179:2274–2280CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Morita Y, Kodama K, Shiota S et al (1998) NorM, a putative multidrug efflux protein, of Vibrio parahaemolyticus and its homolog in Escherichia coli. Antimicrob Agents Chemother 42:1778–1782PubMedPubMedCentralGoogle Scholar
  8. 8.
    Su XZ, Chen J, Mizushima T et al (2005) AbeM, an H+-coupled Acinetobacter baumannii multidrug efflux pump belonging to the MATE family of transporters. Antimicrob Agents Chemother 49:4362–4364CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Yamada Y, Hideka K, Shiota S et al (2006) Gene cloning and characterization of SdrM, a chromosomally-encoded multidrug efflux pump, from Staphylococcus aureus. Biol Pharm Bull 29:554–556CrossRefPubMedGoogle Scholar
  10. 10.
    Hassan KA, Brzoska AJ, Wilson NL et al (2011) Roles of DHA2 family transporters in drug resistance and iron homeostasis in Acinetobacter spp. J Mol Microbiol Biotechnol 20:116–124CrossRefPubMedGoogle Scholar
  11. 11.
    Hassan KA, Li Q, Henderson PJF et al (2015) Homologs of the Acinetobacter baumannii AceI transporter represent a new family of bacterial multidrug efflux systems. MBio 6:e01982–e01914CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Hassan KA, Jackson SM, Penesyan A et al (2013) Transcriptomic and biochemical analyses identify a family of chlorhexidine efflux proteins. Proc Natl Acad Sci U S A 110:20254–20259CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Hassan KA, Elbourne LD, Li L et al (2015) An ace up their sleeve: a transcriptomic approach exposes the AceI efflux protein of Acinetobacter baumannii and reveals the drug efflux potential hidden in many microbial pathogens. Front Microbiol 6:333CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Magoc T, Wood D, Salzberg SL (2013) EDGE-pro: estimated degree of gene expression in prokaryotic genomes. Evol Bioinforma 9:127–136CrossRefGoogle Scholar
  15. 15.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    He S, Wurtzel O, Singh K et al (2010) Validation of two ribosomal RNA removal methods for microbial metatranscriptomics. Nat Methods 7:807–812CrossRefPubMedGoogle Scholar
  17. 17.
    McClure R, Balasubramanian D, Sun Y et al (2013) Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res 41:e140CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Warren AS, Aurrecoechea C, Brunk B et al (2015) RNA-Rocket: an RNA-Seq analysis resource for infectious disease research. Bioinformatics:1–3Google Scholar
  19. 19.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Reinert K, Langmead B, Weese D et al (2015) Alignment of next-generation sequencing reads. Annu Rev Genomics Hum Genet 16:133–151CrossRefPubMedGoogle Scholar
  21. 21.
    Langmead B, Salzberg SL (2012) Fast and gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Levin JZ, Yassour M, Adiconis X et al (2010) Comprehensive comparative analysis of stran-specific RNA sequencing methods. Nat Methods 7:709–715CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Conway T, Creecy JP, Maddox SM et al (2014) Unprecedented high-resolution view of bacterial operon architechture recealed by RNA sequencing. MBio 5:e01442–e01414CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Haas B, Chin M, Nusbaum C et al (2012) How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? BMC Genomics 13:734CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Adiconis X, Borges-Rivera D, Satija R et al (2013) Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat Methods 10:623–629CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  • Liping Li
    • 1
  • Sasha G. Tetu
    • 1
  • Ian T. Paulsen
    • 1
  • Karl A. Hassan
    • 2
  1. 1.Department of Chemistry and Biomolecular SciencesMacquarie UniversitySydneyAustralia
  2. 2.Department of Chemistry and Biomolecular SciencesMacquarie UniversityNorth RydeAustralia

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