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Microbial Ecology

, Volume 61, Issue 3, pp 676–683 | Cite as

Microsatellite-Based Quantification Method to Estimate Biomass of Endophytic Phialocephala Species in Strain Mixtures

  • Vanessa Reininger
  • Christoph R. Grünig
  • Thomas N. Sieber
Methods

Abstract

Fungi of the Phialocephala fortinii sensu lato–Acephala applanata species complex (PAC) are ubiquitous endophytic colonizers of tree roots in which they form genotypically diverse communities. Measurement of the colonization density of each of the fungal colonizers is a prerequisite to study the ecology of these communities. Up to now, there is no method readily available for the quantification of PAC strains co-colonizing the same root. The new DNA quantification method presented here is based on the amplification of microsatellites by competitive polymerase chain reaction (PCR). The method proved to be suitable to detect and quantify at least two strains within one single sample by the addition of a known amount of mycelium of a reference strain before DNA extraction. The method exploits the correlation between the reference/target ratio of light emitted during microsatellite detection (peak ratio) and the reference/target ratio of mycelial weights to determine the biomass of the target strain. Hence, calibration curves were obtained by linear regression of the peak ratios on the weight ratios for different mixtures of reference and target strains. The slopes of the calibration curves and the coefficients of determination were close to 1, indicating that peak ratios are good predictors of weight ratios. Estimates of fungal biomass in mycelial test mixtures of known composition laid within the 95% prediction interval and deviated on average by 16% (maximally 50%) from the true biomass. On average, 3–6% of the root biomass of Norway spruce seedlings consisted of mycelial biomass of either one of two inoculated PAC strains. Biomass estimates obtained by real-time quantitative PCR were correlated with the estimates obtained by the microsatellite-based method, but variation between the two estimates from the same root was high in some samples. The microsatellite-based DNA quantification method described here is currently the best method for strainwise estimation of endophytic biomass of PAC fungi in small root samples.

Keywords

Fungal Biomass Prediction Interval Biomass Estimate Dark Septate Endophyte Mycelial Biomass 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We would like to thank the Genetic Diversity Centre (GDC) of ETH Zurich for providing the necessary laboratory facilities to perform real-time quantitative PCR and microsatellite analyses. We also thank Manuel Koller of the Seminar for Statistics (SfS), ETH Zurich, for his support. The study represents part of the research project GEDIHAP funded by the Competence Center Environment and Sustainability (CCES) of the ETH Domain.

Supplementary material

248_2010_9798_MOESM1_ESM.docx (22 kb)
Table S1 Root and fungal dry weight biomass data (including upper and lower limit of the 95% prediction interval) for the 21 pooled root segments per seedling (Picea abies) in [g]. (DOCX 22 kb)

References

  1. 1.
    Ahlich K, Sieber TN (1996) The profusion of dark septate endophytic fungi in non-ectomycorrhizal fine roots of forest trees and shrubs. New Phytol 132:259–270CrossRefGoogle Scholar
  2. 2.
    Atkins SD, Peteira B, Clark IM, Kerry BR, Hirsch PR (2009) Use of real-time quantitative PCR to investigate root and gall colonisation by co-inoculated isolates of the nematophagous fungus Pochonia chlamydosporia. Ann Appl Biol 155:143–152CrossRefGoogle Scholar
  3. 3.
    Bibby K, Viau E, Peccia J (2010) Pyrosequencing of the 16S rRNA gene to reveal bacterial pathogen diversity in biosolids. Water Res 44:4252–4260PubMedCrossRefGoogle Scholar
  4. 4.
    Cleveland WS, Devlin SJ (1988) Locally weighted regression—an approach to regression-analysis by local fitting. J Am Stat Assoc 83:596–610CrossRefGoogle Scholar
  5. 5.
    Crawley MJ (2007) The R book. Wiley, ChichesterCrossRefGoogle Scholar
  6. 6.
    Currah RS, Tsuneda A, Murakami S (1993) Morphology and ecology of Phialocephala fortinii in roots of rhododendron brachycarpum. Can J Bot 71:1639–1644CrossRefGoogle Scholar
  7. 7.
    Grünig CR, Linde CC, Sieber TN, Rogers SO (2003) Development of single-copy RFLP markers for population genetic studies of Phialocephala fortinii and closely related taxa. Mycol Res 107:1332–1341PubMedCrossRefGoogle Scholar
  8. 8.
    Grünig CR, Brunner PC, Duo A, Sieber TN (2007) Suitability of methods for species recognition in the Phialocephala fortinii-Acephala applanata species complex using DNA analysis. Fungal Genet Biol 44:773–788PubMedCrossRefGoogle Scholar
  9. 9.
    Grünig CR, Queloz V, Sieber TN, Holdenrieder O (2008) Dark septate endophytes (DSE) of the Phialocephala fortinii s.l.—Acephala applanata species complex in tree roots: classification, population biology, and ecology. Botany 86:1355–1369CrossRefGoogle Scholar
  10. 10.
    Holdenrieder O, Sieber TN (1992) Fungal associations of serially washed healthy nonmycorrhizal roots of Picea abies. Mycol Res 96:151–156CrossRefGoogle Scholar
  11. 11.
    Jumpponen A, Trappe JM (1998) Dark septate endophytes: a review of facultative biotrophic root-colonizing fungi. New Phytol 140:295–310CrossRefGoogle Scholar
  12. 12.
    Melin E (1923) Experimentelle Untersuchungen über die Konstitution und Ökologie der Mykorrhizen von Pinus sylvestris L. und Picea abies (L.) Karst. In: Falck R (ed) Mykologische Untersuchungen und Berichte 2., pp 73–334Google Scholar
  13. 13.
    Naef A, Senatore M, Defago G (2006) A microsatellite based method for quantification of fungi in decomposing plant material elucidates the role of Fusarium graminearum DON production in the saprophytic competition with Trichoderma atroviride in maize tissue microcosms. FEMS Microbiol Ecol 55:211–220PubMedCrossRefGoogle Scholar
  14. 14.
    Queloz V, Grünig CR, Sieber TN, Holdenrieder O (2005) Monitoring the spatial and temporal dynamics of a community of the tree-root endophyte Phialocephala fortinii s.l. New Phytol 168:651–660PubMedCrossRefGoogle Scholar
  15. 15.
    Queloz V, Duo A, Grünig CR (2008) Isolation and characterization of microsatellite markers for the tree-root endophytes Phialocephala subalpina and Phialocephala fortinii s.s. Mol Ecol Resour 8:1322–1325CrossRefGoogle Scholar
  16. 16.
    Queloz V, Duo A, Sieber TN, Grünig CR (2010) Microsatellite size homoplasies and null alleles do not affect species diagnosis and population genetic analysis in a fungal species complex. Mol Ecol Resour 10:348–367CrossRefGoogle Scholar
  17. 17.
    R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  18. 18.
    Read DJ, Haselwandter K (1981) Observations on the mycorrhizal status of some alpine plant-communities. New Phytol 88:341–352CrossRefGoogle Scholar
  19. 19.
    Schulz B, Boyle C (2005) The endophytic continuum. Mycol Res 109:661–686PubMedCrossRefGoogle Scholar
  20. 20.
    Sieber TN (2002) Fungal root endophytes. In: Waisel Y, Eshel A, Kafkafi U (eds) Plant roots, the hidden half. Basel, New York, pp 887–917Google Scholar
  21. 21.
    Sieber TN, Grünig CR (2006) Biodiversity of fungal root-endophyte communites and populations, in particular of the dark septate endophyte Phialocephala fortinii s.l. In: Schulz B, Boyle C, Sieber T (eds) Microbial root endophytes, vol 9. Springer, Berlin, pp 107–132CrossRefGoogle Scholar
  22. 22.
    Sieber TN (2007) Endophytic fungi in forest trees: are they mutualists? Fungal Biol Rev 21:75–89CrossRefGoogle Scholar
  23. 23.
    Stoyke G, Egger KN, Currah RS (1992) Characterization of sterile endophytic fungi from the mycorrhizae of sub-alpine plants. Can J Bot 70:2009–2016CrossRefGoogle Scholar
  24. 24.
    Summerbell RC (2005) Root endophyte and mycorrhizosphere fungi of black spruce, Picea mariana, in a boreal forest habitat: influence of site factors on fungal distributions. Stud Mycol 53:121–145CrossRefGoogle Scholar
  25. 25.
    Tedersoo L, Nilsson RH, Abarenkov K, Jairus T, Sadam A, Saar I, Bahram M, Bechem E, Chuyong G, Koljalg U (2010) 454 Pyrosequencing and Sanger sequencing of tropical mycorrhizal fungi provide similar results but reveal substantial methodological biases. New Phytol 188:291–301PubMedCrossRefGoogle Scholar
  26. 26.
    Tejesvi MV, Mahesh B, Nalini MS, Prakash HS, Kini KR, Subbiah V, Shetty HS (2006) Fungal endophyte assemblages from ethnopharmaceutically important medicinal trees. Can J Microbiol 52:427–435PubMedCrossRefGoogle Scholar
  27. 27.
    Tejesvi MV, Ruotsalainen AL, Markkola AM, Pirttila AM (2010) Root endophytes along a primary succession gradient in northern Finland. Fungal Divers 41:125–134CrossRefGoogle Scholar
  28. 28.
    Tellenbach C, Grünig CR, Sieber TN (2010) Suitability of quantitative real-time PCR to estimate biomass of fungal root endophytes. Appl Environ Microbiol 76:5764–5772PubMedCrossRefGoogle Scholar
  29. 29.
    Zaffarano PL, Duò A, Grünig CR (2010) Characterization of the mating type (MAT) locus in the Phialocephala fortinii s.l.—Acephala applanata species complex. Fungal Genet Biol 47:761–772PubMedCrossRefGoogle Scholar
  30. 30.
    Zentilin L, Giacca M (2007) Competitive PCR for precise nucleic acid quantification. Nat Protoc 2:2092–2104PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Vanessa Reininger
    • 1
  • Christoph R. Grünig
    • 1
  • Thomas N. Sieber
    • 1
  1. 1.Institute of Integrative Biology, Forest Pathology and DendrologyETH ZurichZürichSwitzerland

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