Archives of Microbiology

, Volume 195, Issue 6, pp 413–418 | Cite as

When should a DDH experiment be mandatory in microbial taxonomy?

  • Jan P. Meier-Kolthoff
  • Markus Göker
  • Cathrin Spröer
  • Hans-Peter Klenk
Original Paper

Abstract

DNA–DNA hybridizations (DDH) play a key role in microbial species discrimination in cases when 16S rRNA gene sequence similarities are 97 % or higher. Using real-world 16S rRNA gene sequences and DDH data, we here re-investigate whether or not, and in which situations, this threshold value might be too conservative. Statistical estimates of these thresholds are calculated in general as well as more specifically for a number of phyla that are frequently subjected to DDH. Among several methods to infer 16S gene sequence similarities investigated, most of those routinely applied by taxonomists appear well suited for the task. The effects of using distinct DDH methods also seem to be insignificant. Depending on the investigated taxonomic group, a threshold between 98.2 and 99.0 % appears reasonable. In that way, up to half of the currently conducted DDH experiments could safely be omitted without a significant risk for wrongly differentiated species.

Keywords

DNA–DNA hybridization Species concept 16S rRNA gene Generalized linear model BLAST Smith–Waterman Substitution model Microbial taxonomy 

Notes

Acknowledgments

We are grateful to Prof. Erko Stackebrandt for providing data and for helpful comments.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

203_2013_888_MOESM1_ESM.xls (374 kb)
Supplementary material 1 (XLS 373 kb)
203_2013_888_MOESM2_ESM.pdf (1.2 mb)
Supplementary material 2 (PDF 1262 kb)

References

  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 19:716–723. doi: 10.1109/TAC.1974.1100705 CrossRefGoogle Scholar
  2. Altschul S, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410PubMedGoogle Scholar
  3. Auch A, Von Jan M, Klenk H-P, Göker M (2010a) Digital DNA–DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison. Stand Genomic Sci 2:117–134. doi: 10.4056/sigs.531120 PubMedCrossRefGoogle Scholar
  4. Auch A, Klenk H-P, Göker M (2010b) Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs. Stand Genomic Sci 2:142–148. doi: 10.4056/sigs.541628 PubMedCrossRefGoogle Scholar
  5. Crawley MJ (2007) The R book. Wiley, ChichesterCrossRefGoogle Scholar
  6. Cui H-L, Zhou P-J, Oren A, Liu S-J (2009) Intraspecific polymorphism of 16S rRNA genes in two halophilic archaeal genera, Haloarcula and Halomicrobium. Extremophiles 13:31–37. doi: 10.1007/s00792-008-0194-2 PubMedCrossRefGoogle Scholar
  7. De Ley J, Cattoir H, Reynaerts A (1970) The quantitative measurement of DNA hybridization from renaturation rates. Eur J Biochem 12:133–142. doi: 10.1111/j.1432-1033.1970.tb00830.x PubMedCrossRefGoogle Scholar
  8. Euzéby JP (1997) List of bacterial names with standing in nomenclature: a folder available on the internet. Int J Syst Bacteriol 47:590–592. doi: 10.1099/00207713-47-2-590 PubMedCrossRefGoogle Scholar
  9. Ezaki T, Hashimoto Y, Yabuuchi E (1989) Fluorometric deoxyribonucleic acid-deoxyribonucleic acid hybridization in microdilution wells as an alternative to membrane filter hybridization in which radioisotopes are used to determine genetic relatedness among bacterial strains. Int J Syst Bacteriol 39:224–229. doi: 10.1099/00207713-39-3-224 CrossRefGoogle Scholar
  10. Felsenstein J (2004) Inferring phylogenies. Sinauer Associates, SunderlandGoogle Scholar
  11. Goris J, Konstantinidis K, Klappenbach J et al (2007) DNA–DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol 57:81–91. doi: 10.1099/ijs.0.64483-0 PubMedCrossRefGoogle Scholar
  12. Harrell FE, Lee KL, Califf RM et al (1984) Regression modelling strategies for improved prognostic prediction. Stat Med 3:143–152. doi: 10.1002/sim.4780030207 PubMedCrossRefGoogle Scholar
  13. Hasegawa M, Kishino H, Yano T (1985) Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol 22:160–174PubMedCrossRefGoogle Scholar
  14. Henz SR, Huson DH, Auch AF et al (2005) Whole-genome prokaryotic phylogeny. Bioinformatics 21:2329–2335. doi: 10.1093/bioinformatics/bth324 PubMedCrossRefGoogle Scholar
  15. Jukes T, Cantor C (1969) Evolution of protein molecules. Academic Press, New YorkGoogle Scholar
  16. Keswani J, Whitman WB (2001) Relationship of 16S rRNA sequence similarity to DNA hybridization in prokaryotes. Int J Syst Evol Microbiol 51:667–678PubMedGoogle Scholar
  17. Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol 16:111–120PubMedCrossRefGoogle Scholar
  18. Klenk H-P, Göker M (2010) En route to a genome-based classification of Archaea and Bacteria? Syst Appl Microbiol 33:175–182. doi: 10.1016/j.syapm.2010.03.003 PubMedCrossRefGoogle Scholar
  19. Konstantinidis KT, Tiedje JM (2005) Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci USA 102:2567–2572PubMedCrossRefGoogle Scholar
  20. Konstantinidis KT, Tiedje JM (2007) Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Curr Opin Microbiol 10:504–509. doi: 10.1016/j.mib.2007.08.006 PubMedCrossRefGoogle Scholar
  21. Kostinek M, Pukall R, Rooney AP et al (2005) Lactobacillus arizonensis is a later heterotypic synonym of Lactobacillus plantarum. Int J Syst Evol Microbiol 55:2485–2489. doi: 10.1099/ijs.0.63880-0 PubMedCrossRefGoogle Scholar
  22. Lagier J-C, Karkouri K El, Rivet R et al (2013) Non contiguous-finished genome sequence and description of Senegalemassilia anaerobia gen. nov., sp. nov. Stand Genomic Sci. doi:  10.4056/sigs.3246665
  23. Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M (2013) Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics 14:60. doi: 10.1186/1471-2105-14-60 PubMedCrossRefGoogle Scholar
  24. Motulsky H, Christopoulos A (2004) Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting. Oxford University Press, OxfordGoogle Scholar
  25. Nelder JA, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc 135:370–384Google Scholar
  26. Peduzzi P, Concato J, Kemper E et al (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49:1373–1379PubMedCrossRefGoogle Scholar
  27. Rice P, Longden I, Bleasby A (2000) EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 16:276–277PubMedCrossRefGoogle Scholar
  28. Richter M, Rosselló-Móra R (2009) Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci USA 106:19126–19131. doi: 10.1073/pnas.0906412106 PubMedCrossRefGoogle Scholar
  29. Stackebrandt E, Ebers J (2006) Taxonomic parameters revisited: tarnished gold standards. Microbiol Today 33:152–155Google Scholar
  30. Stackebrandt E, Goebel BM (1994) Taxonomic note: a place for DNA–DNA reassociation and 16S rRNA sequence analysis in the present species definition in bacteriology. Int J Syst Bacteriol 44:846–849. doi: 10.1099/00207713-44-4-846 CrossRefGoogle Scholar
  31. Steyerberg EW, Eijkemans MJC, Harrell FE, Habbema JDF (2000) Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med 19:1059–1079PubMedCrossRefGoogle Scholar
  32. Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc 36:111–147. doi: 10.2307/2984809 Google Scholar
  33. Swezey JL, Nakamura LK, Abbott TP, Peterson RE (2000) Lactobacillus arizonensis sp. nov., isolated from jojoba meal. Int J Syst Evol Microbiol 50:1803–1809. doi: 10.1099/00207713-50-5-1803 PubMedGoogle Scholar
  34. Swofford DL (2003) PAUP*. Phylogenetic analysis using parsimony (*and other methods). Version 4. Sinauer Associates, SunderlandGoogle Scholar
  35. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://r-project.org/
  36. Tindall BJ, Kampfer P, Euzeby JP, Oren A (2006) Valid publication of names of prokaryotes according to the rules of nomenclature: past history and current practice. Int J Syst Evol Microbiol 56:2715–2720. doi: 10.1099/ijs.0.64780-0 PubMedCrossRefGoogle Scholar
  37. Tindall B, Rosselló-Móra R, Busse HJ et al (2010) Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol Microbiol 60:249–266. doi: 10.1099/ijs.0.016949-0 PubMedCrossRefGoogle Scholar
  38. Tourova TP, Antonov AS (1988) Identification of microorganisms by rapid DNA–DNA hybridization. In: Colwell RR, Grigorova R (eds) Methods in microbiology. Academic Press, London, pp 333–355Google Scholar
  39. Wayne LG, Brenner DJ, Colwell RR et al (1987) Report of the ad hoc committee on reconciliation of approaches to bacterial systematics. Int J Syst Bacteriol 37:463–464. doi: 10.1099/00207713-37-4-463 CrossRefGoogle Scholar
  40. Woese CR (1987) Bacterial evolution. Microbiol Rev 51:221–271PubMedGoogle Scholar
  41. Yang Z (1993) Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Mol Biol Evol 10:1396–1401PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jan P. Meier-Kolthoff
    • 1
  • Markus Göker
    • 1
  • Cathrin Spröer
    • 1
  • Hans-Peter Klenk
    • 1
  1. 1.Leibniz Institute DSMZ - German Collection of Microorganisms and Cell CulturesBraunschweigGermany

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