Quantitative Analysis of the Rates for Repeat-Mediated Genome Instability in a Yeast Experimental System

  • Elina A. Radchenko
  • Ryan J. McGinty
  • Anna Y. Aksenova
  • Alexander J. Neil
  • Sergei M. MirkinEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1672)


Instability of repetitive DNA sequences causes numerous hereditary disorders in humans, the majority of which are associated with trinucleotide repeat expansions. Here, we describe a unique system to study instability of triplet repeats in a yeast experimental setting. Using fluctuation assay and the novel program FluCalc we are able to accurately estimate the rates of large-scale expansions, as well as repeat-mediated mutagenesis and gross chromosomal rearrangements for different repeat sequences.

Key words

Trinucleotide repeats Repeat expansions Repeat-induced mutagenesis Fluctuation assay Expansion rate Mutation rate MSS-MLE FluCalc 



We thank Alexander A. Shishkin and Kartik A. Shah for their invaluable contributions in developing cassettes to study repeat instability, and for developing experimental protocols for the selection and PCR procedures, Timofei S. Bondarev for developing FluCalc program, and Durwood Marshall for statistical consulting. This study was funded by NIH grants GM105473 and GM60987 to S.M.M and RFBR grant #15-04-08658 and research project in the Centre for Molecular and Cell Technologies (Research Park, Saint-Petersburg State University) for A.Y.A.


  1. 1.
    Luria SE, Delbruck M (1943) Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28(6):491–511PubMedPubMedCentralGoogle Scholar
  2. 2.
    Drake JW (1991) A constant rate of spontaneous mutation in DNA-based microbes. Proc Natl Acad Sci U S A 88:7160–7164. doi: 10.1073/pnas.88.16.7160 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Foster PL (2006) Methods for determining spontaneous mutation rates. Methods Enzymol 409:195–213. doi: 10.1016/S0076-6879(05)09012-9 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Zheng Q (2015) A new practical guide to the Luria–Delbrück protocol. Mutat Res 781:7–13. doi: 10.1016/j.mrfmmm.2015.08.005 CrossRefPubMedGoogle Scholar
  5. 5.
    Zheng Q (2016) A second look at the final number of cells in a fluctuation experiment. J Theor Biol 401:54–63. doi: 10.1016/j.jtbi.2016.04.027 CrossRefPubMedGoogle Scholar
  6. 6.
    Mirkin SM (2007) Expandable DNA repeats and human disease. Nature 447(7147):932–940. doi: 10.1038/nature05977 CrossRefPubMedGoogle Scholar
  7. 7.
    Kim JC, Mirkin SM (2013) The balancing act of DNA repeat expansions. Curr Opin Genet Dev 23(3):280–288. doi: 10.1016/j.gde.2013.04.009 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Shishkin AA, Voineagu I, Matera R et al (2009) Large-scale expansions of Friedreich’s ataxia GAA repeats in yeast. Mol Cell 35(1):82–92. doi: 10.1016/j.molcel.2009.06.017 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Shah KA, Shishkin AA, Voineagu I et al (2012) Role of DNA polymerases in repeat-mediated genome instability. Cell Rep 2(5):1088–1095. doi: 10.1016/j.celrep.2012.10.006 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Shah KA, McGinty RJ, Egorova VI et al (2014) Coupling transcriptional state to large-scale repeat expansions in yeast. Cell Rep 9(5):1594–1602. doi: 10.1016/j.celrep.2014.10.048 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Cherng N, Shishkin AA, Schlager LI et al (2011) Expansions, contractions, and fragility of the spinocerebellar ataxia type 10 pentanucleotide repeat in yeast. Proc Natl Acad Sci U S A 108(7):2843–2848. doi: 10.1073/pnas.1009409108 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Aksenova AY, Greenwell PW, Dominska M et al (2013) Genome rearrangements caused by interstitial telomeric sequences in yeast. Proc Natl Acad Sci U S A 110(49):19866–19871. doi: 10.1073/pnas.1319313110 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Schmidt KH, Pennaneach V, Putnam CD et al (2006) Analysis of gross-chromosomal rearrangements in Saccharomyces cerevisiae. Methods Enzymol 409:462–476. doi: 10.1016/S0076-6879(05)09027-0 CrossRefPubMedGoogle Scholar
  14. 14.
    Aksenova AY, Han G, Shishkin AA et al (2015) Expansion of interstitial telomeric sequences in yeast. Cell Rep 13(8):1545–1551. doi: 10.1016/j.celrep.2015.10.023 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Zheng Q (2002) Statistical and algorithmic methods for fluctuation analysis with SALVADOR as an implementation. Math Biosci 176(2):237–252. doi: 10.1016/S0025-5564(02)00087-1 CrossRefPubMedGoogle Scholar
  16. 16.
    Zheng Q (2016) rSalvador 1.5: an R tool for the Luria–Delbrück fluctuation assay. Accessed 7 July 2016Google Scholar
  17. 17.
    Hall BM, Ma CX, Liang P et al (2009) Fluctuation AnaLysis CalculatOR: a web tool for the determination of mutation rate using Luria-Delbruck fluctuation analysis. Bioinformatics 25(12):1564–1565. doi: 10.1093/bioinformatics/btp253 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Gillet-Markowska A, Louvel G, Fischer G (2015) bz-rates: a web tool to estimate mutation rates from fluctuation analysis. G3 Bethesda 5(11):2323–2327. doi: 10.1534/g3.115.019836 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Sarkar S, Ma WT, Sandri GH (1992) On fluctuation analysis: a new, simple and efficient method for computing the expected number of mutants. Genetica 85(2):173–179. doi: 10.1007/BF00120324 CrossRefPubMedGoogle Scholar
  20. 20.
    Rosche WA, Foster PL (2000) Determining mutation rates in bacterial populations. Methods 20(1):4–17. doi: 10.1006/meth.1999.0901 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2018

Authors and Affiliations

  • Elina A. Radchenko
    • 1
  • Ryan J. McGinty
    • 1
  • Anna Y. Aksenova
    • 2
  • Alexander J. Neil
    • 1
  • Sergei M. Mirkin
    • 1
    Email author
  1. 1.Department of BiologyTufts UniversityMedfordUSA
  2. 2.Laboratory of Amyloid BiologySaint-Petersburg State UniversitySaint-PetersburgRussia

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