SpringerLink
Log in

Estimation of Microbial Mutation Rates in Tuberculosis Research

  • Chapter
  • First Online:
Tuberculosis

Part of the book series: Integrated Science ((IS,volume 11))

  • 689 Accesses

Summary

Antibiotic resistance is a dominant theme in tuberculosis research. Quantitative studies on microbial mutation rates play a key role in drug resistance research. Despite recent rapid advances in whole-genome sequencing, the classic Luria-Delbrük fluctuation test continues to be the choice of method for measuring microbial mutation rates. To help researchers new to this field, the author of this chapter provides detailed descriptions and practical guidelines pertaining to the proper use of this classic protocol. The discussion focuses on practical issues that are still bewildering to many tuberculosis researchers. Future developments in the field are discussed from a personal perspective.

Graphical Abstract

Anybody can do it – after he has been shown how.

Christopher Columbus, as retold by James Baldwin

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 160.49
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 213.99
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
EUR 213.99
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Luria SE, Delbrück M (1943) Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28:491–511

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Luria SE (1984) A slot machine, a broken test tube: an autobiography. Harper & Row, New York

    Google Scholar 

  3. Zheng Q (2017) Toward a unique definition of the mutation rate. Bull Math Biol 79:683–692

    Article  PubMed  Google Scholar 

  4. David HL (1970) Probability distribution of drug-resistant mutants in unselected populations of Mycobacterium tuberculosis. Appl Microbiol 20:810–814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Werngren J, Hoffner SE (2003) Drug-susceptible Mycobacterium tuberculosis Bei**g genotype does not develop mutation-conferred resistance to rifampin at an elevated rate. J Clin Microbiol 41:1520–1524

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ford CB, Shah RR, Meeda MK et al (2013) Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. Nat Genet 45:784–790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zheng Q, Werngren J (2018) An unbiased attitude is vital to exploring the Bei**g genotype of Mycobacterium tuberculosis. Tuberculosis 111:193–197

    Article  PubMed  Google Scholar 

  8. Zheng Q (2003) Mathematical issues arising from the directed mutation controversy. Genetics 164:373–379

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chakraborty S, Rhee KY (2015) Tuberculosis drug development: history and evolution of the mechanism-based paradigm. Cold Spring Harb Perspect Med 5:a021147

    Article  PubMed  PubMed Central  Google Scholar 

  10. Pyle M (1947) Relative numbers of resistant tubercle bacilli in sputa of patients before and during treatment with streptomycin. Proc Staff Meet Mayo Clin 22:465–473

    CAS  PubMed  Google Scholar 

  11. Vennesland K, Ebert RH, Bloch RG (1947) The demonstration of naturally-occurring streptomycin-resistant variants in the human strain of tubercle bacillus H-37RV. Science 106:476–477

    Article  CAS  PubMed  Google Scholar 

  12. Yegian D, Vanderlinde RJ (1948) A quantitative analysis of the resistance of Mycobacteria to streptomycin. J Bacteriol 56:177–186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kohanski MA, DePristo MA, Collins JJ (2010) Sublethal antibiotic treatment leads to multidrug resistance via radical-induced mutagenesis. Mol Cell 37:311–320

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Henderson-Begg SK, Sheppard CL, George RC, Livermore DM, Hall LMC (2010) Mutant frequency in antibiotic-resistant and -susceptible isolates of Streptococcus pneumoniae. Int J Antimicrob Agents 35:342–346

    Article  CAS  PubMed  Google Scholar 

  15. Kayigire XA, Friedrich SO, van der Merwe L, Diacon AH (2017) Acquisition of rifampin resistance in pulmonary tuberculosis. Antimicrob Agents Chemother 61:e02220-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Delbrück M (1945) Spontaneous mutations of bacteria. Ann Mo Bot Garden 32:223–233

    Article  Google Scholar 

  17. de Steenwinkel JEM, ten Kate MT, de Knegt GJ, Kremer K, Aarnoutse RE, Boeree MJ, Verbrugh HA, van Soolingen D, Bakker-Woudenberg IAJM (2012) Drug susceptibility of Mycobacterium tuberculosis Bei**g genotype and association with MDR TB. Emerg Infect Dis 18:660–663

    Article  PubMed  PubMed Central  Google Scholar 

  18. den Hertog AL, Menting S, van Soolingen D, Anthony RM (2014) Letter to the editor: Mycobacterium tuberculosis Bei**g genotype resistance to transient rifampin exposure. Emerg Infect Dis 20:1932–1933

    Article  Google Scholar 

  19. Werngren J (2013) Letter to the editor: Mycobacterium tuberculosis Bei**g type mutation frequency. Emerg Infect Dis 19:522

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zheng Q (2018) A cautionary note on the mutation frequency in microbial research. Mutat Res 809:51–55

    Article  CAS  PubMed  Google Scholar 

  21. Parkinson JS (2016) Look, Max—no math required! J Bacteriol 17:2281–2282

    Article  Google Scholar 

  22. Foster PL (2006) Methods for determining spontaneous mutation rates. Methods Enzymol 409:195–213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Foster PL (2007) Measuring spontaneous mutation rates. In: Reddy C, Beveridge T, Breznak J, Marzluf G, Schmidt T, Snyder L (eds) Methods for general and molecular microbiology, 3rd edn. ASM Press, Washington, DC, pp 676–683

    Google Scholar 

  24. Rosche WA, Foster PL (2000) Determining mutation rates in bacterial populations. Methods 20:4–17

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Armitage P (1952) The statistical theory of bacterial population subject to mutation. J R Stat Soc Ser B 14:1–44

    Google Scholar 

  26. Armitage P (1953) Statistical concepts in the theory of bacterial mutation. J Hygiene 51:162–184

    Article  CAS  Google Scholar 

  27. Drake JW (1970) The molecular basis of mutation. Holden-Day, San Francisco

    Google Scholar 

  28. Hayes W (1968) The genetics of bacteria and their viruses: studies in basic genetics and molecular biology, 2nd edn. Wiley, New York

    Google Scholar 

  29. Kondo S (1972) A theoretical study on spontaneous mutation rate. Mutat Res 14:365–374

    Article  CAS  PubMed  Google Scholar 

  30. Zheng Q (2005) Update on estimation of mutation rates using data from fluctuation experiments. Genetics 171:861–864

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zheng Q (2015) A new practical guide to the Luria-Delbrück protocol. Mutat Res 781:7–13

    Article  CAS  PubMed  Google Scholar 

  32. Lea EA, Coulson CA (1949) The distribution of the numbers of mutants in bacterial populations. J Genet 49:264–285

    Article  CAS  PubMed  Google Scholar 

  33. Gillespie SH, Voelker LL, Ambler JE et al (2003) Fluoroquinolone resistance in Streptococcus pneumoniae: evidence that gyrA mutations arise at a lower rate and that mutation in gyrA or parC predisposes to further mutation. Microb Drug Resist 9:17–24

    Article  CAS  PubMed  Google Scholar 

  34. Gillespie SH, Basu A, Dickens AL et al (2005) Effect of subinhibitory concentration of ciprofloxacin on Mycobacterium fortuitum mutation rates. J Antimicrob Chemother 56:344–348

    Article  CAS  PubMed  Google Scholar 

  35. Pope CF, Gillespie SH, Moore JE, McHugh TD (2010) Approaches to measure the fitness of Burkholderia cepacia complex isolates. J Med Microbiol 59:679–689

    Article  CAS  PubMed  Google Scholar 

  36. Sarkar A (1991) Haldane’s solution of the Luria-Delbrück distribution. Genetics 127:257–261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Polleys EJ, Freudenreich CH (2020) Genetic assays to study repeat fragility in Saccharomyces cerevisiae. In: Guy-Frank Richard (ed) Trinucleotide repeats: methods and protocols, methods in molecular biology, vol 2056. Springer, pp 83–101 (Chapter 5)

    Google Scholar 

  38. Stephenson FH (2016) Calculations for molecular biology and biotechnology: a guide to mathematics in the laboratory, 3rd edn. Academic, San Diego, CA

    Google Scholar 

  39. Stewart FM, Gordon DM, Levin BR (1990) Fluctuation analysis: the probability distribution of the number of mutants under different conditions. Genetics 124:175–185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jones ME, Thomas SM, Rogers A (1994) Luria-Delbrück fluctuation experiments: design and analysis. Genetics 136:1209–1216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Stewart FM (1991) Fluctuation analysis: the effect of plating efficiency. Genetica 84:51–55

    Article  CAS  PubMed  Google Scholar 

  42. Zheng Q (2008) A note on plating efficiency in fluctuation experiments. Math Biosci 216:150–153

    Article  PubMed  Google Scholar 

  43. Ma WT, Vh Sandri G, Sarkar S (1992) Analysis of the Luria and Delbrück distribution using discrete convolution powers. J Appl Prob 29:255–267

    Google Scholar 

  44. Gurland J (1958) A generalized class of contagious distribution. Biometrics 14:229–249

    Article  Google Scholar 

  45. Fichtenholtz GM (1954) Differential and integral calculus, Chinese language edition. Higher Education Press, Bei**g (8th edition 2006)

    Google Scholar 

  46. Fichtenholtz GM (1954) Differential- und Integralrechnung. Band 2. VEB Deutscher Verlag der Wissenschaften, Berlin (10th edition 1964, 1990)

    Google Scholar 

  47. Stewart FM (1994) Fluctuation tests: how reliable are the estimates of mutation rates? Genetics 137:1139–1146

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Nádas A, Goncharova EI, Rossman TG (1996) Mutations and infinity: improved statistical methods for estimating spontaneous rates. Environ Mol Mutagen 28:90–99

    Article  PubMed  Google Scholar 

  49. Zheng Q (1999) Progress of a half century in the study of the Luria-Delbrück distribution. Math Biosci 162:1–32

    Article  CAS  PubMed  Google Scholar 

  50. Zheng Q (2017) rSalvador: an R package for the fluctuation experiment. G3 (Bethesda) 7:3849–3856

    Google Scholar 

  51. Demerec M (1945) Production of Staphylococcus strains resistant to various concentrations of penicillin. Proc Natl Acad Sci USA 31:16–24

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zheng Q (2005) New algorithms for Luria-Delbrück fluctuation analysis. Math Biosci 196:198–214

    Article  PubMed  Google Scholar 

  53. Pawitan Y (2001) In all likelihood: statistical modeling and inference using likelihood. Clarendon Press, Oxford

    Google Scholar 

  54. Zheng Q (2002) Statistical and algorithmic methods for fluctuation analysis with SALVADOR as an implementation. Math Biosci 176:237–252

    Article  PubMed  Google Scholar 

  55. Zheng Q (2016) Comparing mutation rates under the Luria-Delbrück protocol. Genetica 144:351–359

    Article  CAS  PubMed  Google Scholar 

  56. Lindsey JK (1996) Parametric statistical inference. Clarendon Press, Oxford

    Google Scholar 

  57. Agresti A (2007) An introduction to categorical data analysis, 2nd edn. Wiley, Hoboken, NJ

    Book  Google Scholar 

  58. Zheng Q (2021) New approaches to mutation rate fold change in Luria-Delbrück fluctuation experiments. Math Biosci 335:108572

    Article  CAS  PubMed  Google Scholar 

  59. Wierdl M, Greens CN, Datta A et al (1996) Destabilization of simple repetitive DNA sequences by transcription in yeast. Genetics 143:713–721

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Schmidt KH, Pennaneach V, Putamn CD, Kolodner RD (2006) Analysis of gross-chromosomal rearrangements in Saccharomyces cerevisiae. Methods Enzymol 409:462–476

    Article  CAS  PubMed  Google Scholar 

  61. Wu X, Strome ED, Meng Q et al (2009) A robust estimator of mutation rates. Mutat Res 661:101–109

    Article  CAS  PubMed  Google Scholar 

  62. Zheng Q (2011) A Bayesian two-level model for fluctuation assay. Genetica 139:1409–1416

    Article  PubMed  Google Scholar 

  63. Ycart B, Veziris N (2014) Unbiased estimation of mutation rates under fluctuating final counts. PLoS ONE 9(7):e101434

    Article  PubMed  PubMed Central  Google Scholar 

  64. Zheng Q (2016) A second look at the final number of cells in a fluctuation experiment. J Theor Biol 401:54–63

    Article  PubMed  Google Scholar 

  65. Hamon A, Ycart B (2012) Statistics for the Luria-Delbrück distribution. Electron J Stat 6:1251–1272

    Article  Google Scholar 

  66. Mazoyer A, Drouilhet R, Despréaux S, Ycart B (2017) flan: an R package for inference on mutation models. R J 9:334–351

    Article  Google Scholar 

  67. Hall BM, Ma C-X, Liang P, Singh KK (2009) Fluctuation AnaLysis CalculatOR: a web tool for the determination of mutation rate using Luria-Delbrück fluctuation analysis. Bioinformatics 25:1564–1565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Nyinoh IW, McFadden J (2019) Spontaneous mutations conferring antibiotic resistance to antitubercular drugs at a range of concentrations in Mycobacterium smegmatis. Drug Dev Res 80:147–154

    Article  CAS  PubMed  Google Scholar 

  69. Radchenko EA, McCinty RJ, Aksenova AY, Neil AJ, Mirkin SM (2018) Quantitative analysis of the rates of repeat-mediated genome instability in a yeast experimental system. In: Muzi-Falconi M, Brown GW (eds) Genome instability: methods and protocols, methods in molecular biology, vol 1672. Springer, pp 421–438 (Chapter 29)

    Google Scholar 

  70. Zheng Q (2021) webSalvador: a web tool for the Luria-Delbrück experiment. Microbiol Resource Announc 10:e00314-21

    Article  CAS  Google Scholar 

  71. Ramiro RS, Durão P, Bank C, Gordo I (2020) Low mutational load and high mutation rate variation in gut commensal bacterial. PLoS Biol 18(3):e000617

    Article  Google Scholar 

  72. Zheng Q (2015) Methods for comparing mutation rates using fluctuation assay data. Mutat Res 777:20–22

    Article  CAS  PubMed  Google Scholar 

  73. Gillet-Markowska A, Louvel G, Fischer G (2015) bz-rates: a web-tool to estimate mutation rates from fluctuation analysis. G3 (Bethesda) 5:2323–2327

    Google Scholar 

  74. Zheng Q (2005) SALVADOR 2.0: a tool for fluctuation analysis. https://library.wolfram.com/infocenter/MathSource/5556. Accessed 22 Feb 2022

  75. Wolfram Research, Inc. (2020) Mathematica, version 12.1. Champaign, IL

    Google Scholar 

  76. Bergval IL, Schuiteme ARJ, Klatser PR, Anthony RM (2009) Resistant mutants of Mycobacterium tuberculosis selected in vitro do not reflect the in vivo mechanism of isoniazid resistance. J Antimicrob Chemother 64:515–523

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Ford CB, Lin PL, Chase MR et al (2011) Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat Genet 43:482–486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Pauly MD, Procario MC, Lauring AS (2017) A novel twelve class fluctuation test reveals higher than expected mutation rates for influenza A viruses. eLife 6:e26437

    Google Scholar 

  79. Altmann A, Weber P, Bader D et al (2012) A beginners guide to SNP calling from high-throughput DNA-sequencing data. Hum Genet 131:1541–1554

    Article  PubMed  Google Scholar 

  80. Hwang S, Kim E, Lee I, Marcotte EM (2015) Systematic comparison of variant calling pipelines using gold standard personal exome variants. Sci Rep 5:17875

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, Texas A&M University School of Public Health, College Station, TX, 77843, USA

    Qi Zheng

Authors
  1. Qi Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Zheng .

Editor information

Editors and Affiliations

  1. Universal Scientific Education and Research Network (USERN), Stockholm, Sweden

    Nima Rezaei

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zheng, Q. (2023). Estimation of Microbial Mutation Rates in Tuberculosis Research. In: Rezaei, N. (eds) Tuberculosis. Integrated Science, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-031-15955-8_43

Download citation

Publish with us

Policies and ethics

Search

Navigation