Real-Time PCR Methods to Study Expression of Genes Related to Hypermutability

  • Denise M. O’Sullivan
Part of the Methods in Molecular Biology book series (MIMB, volume 642)


Pathogenic bacteria can have sub-populations of hypermutable bacteria. This sub-population has a higher spontaneous mutation rate than the majority of the population which can be attributed to defects in proofreading and repair mechanisms. This leads to the evolution of drug-resistant strains of bacteria through genetic change. It is important to study the expression of genes involved in, for example, mismatch repair and the SOS system by real-time PCR to determine hypermutability and therefore provide an indicator of the mutagenic ability of certain strains of pathogenic bacteria.

Key words

Hypermutability DNA repair Mutation 


  1. 1.
    Miller JH (1996) Spontaneous mutators in bacteria: insights into pathways of mutagenesis and repair. Annu Rev Microbiol 50:625–643CrossRefPubMedGoogle Scholar
  2. 2.
    Oliver A, Baquero F, Blazquez J (2002) The mismatch repair system (mutS, mutL and uvrD genes) in Pseudomonas aeruginosa: molecular characterization of naturally occurring mutants. Mol Microbiol 43:1641–1650CrossRefPubMedGoogle Scholar
  3. 3.
    Horst J-P, Wu T-H, Marinus MG (1999) Escherichia coli mutator genes. Trends Microbiol 7:29–36CrossRefPubMedGoogle Scholar
  4. 4.
    Herman GE, Modrich P (1981) Escherichia coli K-12 clones that overproduce dam methylase are hypermutable. J Bacteriol 145:644–646PubMedGoogle Scholar
  5. 5.
    Oliver A, Canton R, Campo P, Baquero F, Blazquez J (2000) High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288:1251–1254CrossRefPubMedGoogle Scholar
  6. 6.
    Wilson M, DeRisi J, Kristensen H-H, Imboden P, Rane S, Brown PO, Schoolnik GK (1999) Exploring drug-induced alterations in gene expression in Mycobacterium tuberculosis by microarray hybridization. Proc Natl Acad Sci USA 96:12833–12838CrossRefPubMedGoogle Scholar
  7. 7.
    Doumith M, Cazalet C, Simoes N, Frangeul L, Jacquet C, Kunst F, Martin P, Cossart P, Glaser P, Buchrieser C (2004) New aspects regarding evolution and virulence of Listeria monocytogenes revealed by comparative genomics and DNA arrays. Infect Immun 72:1072–1083CrossRefPubMedGoogle Scholar
  8. 8.
    Saunders NA, Underwood A, Kearns AM, Hallas G (2004) A virulence-associated gene microarray: a tool for investigation of the evolution and pathogenic potential of Staphylococcus aureus. Microbiology 150:3763–3771CrossRefPubMedGoogle Scholar
  9. 9.
    Marton MJ, DeRisi JL, Bennett HA, Iyer VR, Meyer MR, Roberts CJ, Stoughton R, Burchard J, Slade D, Dai H, Bassett DE, Hartwell LH, Brown PO, Friend SH (1998) Drug target validation and identification of secondary drug target effects using DNA microarrays. Nat Med 4:1293–1301CrossRefPubMedGoogle Scholar
  10. 10.
    Adam M, Murali B, Glenn N, Potter SS (2008) Epigenetic inheritance based evolution of antibiotic resistance in bacteria. BMC Evol Biol 8:52CrossRefPubMedGoogle Scholar
  11. 11.
    Guard-Bouldin J, Morales CA, Frye JG, Gast RK, Musgrove M (2007) Detection of Salmonella enterica subpopulations by phenotype microarray antibiotic resistance patterns. Appl Environ Microbiol 73:7753–7756CrossRefPubMedGoogle Scholar
  12. 12.
    Dumas JL, van Delden C, Perron K, Köhler T (2006) Analysis of antibiotic resistance gene expression in Pseudomonas aeruginosa by quantitative real-time-PCR. FEMS Microbiol Lett 254:217–225CrossRefPubMedGoogle Scholar
  13. 13.
    Mangan JA, Monahan IM, Butcher PD (2002) Gene expression during host-pathogen interactions: approaches to bacterial mRNA extraction and labelling for microarray analysis. In: Wren B, Dorrell N (eds) Functional microbial genomics: methods in microbiology. Academic, London, pp 137–151CrossRefGoogle Scholar
  14. 14.
    Brooks PC, Movahedzadeh F, Davis EO (2001) Identification of some DNA damage-inducible genes of Mycobacterium tuberculosis: apparent lack of correlation with LexA binding. J Bacteriol 183:4459–4467CrossRefPubMedGoogle Scholar
  15. 15.
    Boshoff HI, Reed MB, Barry CE III, Mizrahi V (2003) DnaE2 polymerase contributes to in vivo survival and the emergence of drug resistance in Mycobacterium tuberculosis. Cell 113:183–193CrossRefPubMedGoogle Scholar
  16. 16.
    Hampshire T, Soneji S, Bacon J, James BW, Hinds J, Laing K, Stabler RA, Marsh PD, Butcher PD (2004) Stationary phase gene expression of Mycobacterium tuberculosis following a progressive nutrient depletion: a model for persistent organisms? Tuberculosis 84:228–238CrossRefPubMedGoogle Scholar
  17. 17.
    Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 25:402–408CrossRefPubMedGoogle Scholar
  18. 18.
    Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29:e45CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Denise M. O’Sullivan
    • 1
  1. 1.Department of Infectious and Tropical DiseaseLondon School of Hygiene and Tropical MedicineLondonUK

Personalised recommendations