Section 3 update: The Analysis of Microbial Communities with Terminal Restriction Fragment Length Polymorphism (T-RFLP)

ABSTRACT A multiplex terminal restriction fragment length polymorphism (M-TRFLP) fingerprinting method was developed and validated for simultaneous analysis of the diversity and community structure of two or more microbial taxa (up to four taxa). The reproducibility and robustness of the method were examined using soil samples collected from different habitats. DNA was PCR amplified separately from soil samples using individual taxon-specific primers for bacteria, archaea, and fungi. The same samples were also subjected to a multiplex PCR with the primers for all three taxa. The terminal restriction fragment length polymorphism profiles generated for the two sets of PCR products were almost identical not only in terms of the presence of peaks but also in terms of the relative peak intensity. The M-TRFLP method was then used to investigate rhizosphere bacterial, fungal, and rhizobial/agrobacterial communities associated with the dwarf shrub Calluna vulgaris growing in either open moorland, a mature pine forest, or a transition zone between these two habitats containing naturally regenerating pine trees. Rhizosphere microbial communities associated with Vaccinium myrtillus collected from the native pine forest were also investigated. In this study, individual PCR products from the three taxa were also pooled before restriction digestion and fragment size analysis. The terminal restriction fragment length polymorphism profiles obtained with PCR products amplified individually and with multiplexed and pooled PCR products were found to be consistent with each other in terms of the number, position, and relative intensity of peaks. The results presented here confirm that M-TRFLP analysis is a highly reproducible and robust molecular tool for simultaneous investigation of multiple taxa, which allows more complete and higher resolution of microbial communities to be obtained more rapidly and economically.

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Evaluating the physical capture method of terminal restriction fragment length polymorphism for comparison of soil microbial communities

Soil Biology and Biochemistry ◽

10.1016/j.soilbio.2006.09.010 ◽

2007 ◽

Vol 39 (2) ◽

pp. 590-599 ◽

Cited By ~ 19

Author(s):

Christopher B. Blackwood ◽

Jeffrey S. Buyer

Keyword(s):

Restriction Fragment Length Polymorphism ◽

Microbial Communities ◽

Restriction Fragment ◽

Length Polymorphism ◽

Fragment Length ◽

Fragment Length Polymorphism ◽

Soil Microbial Communities ◽

Soil Microbial ◽

Capture Method ◽

Restriction Fragment Length

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T-BAPS: A Bayesian Statistical Tool for Comparison of Microbial Communities Using Terminal-restriction Fragment Length Polymorphism (T-RFLP) Data

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1303 ◽

2007 ◽

Vol 6 (1) ◽

Cited By ~ 4

Author(s):

Jing Tang ◽

Jinglun Tao ◽

Hidetoshi Urakawa ◽

Jukka Corander

Keyword(s):

Restriction Fragment Length Polymorphism ◽

Microbial Communities ◽

Restriction Fragment ◽

Length Polymorphism ◽

Fragment Length ◽

Bayesian Model ◽

Missing Values ◽

Latent Class ◽

Fragment Length Polymorphism ◽

Restriction Fragment Length

The investigation of microbial communities is an essential part of the study of the biosphere. Flexible molecular fingerprinting tools such as terminal-restriction fragment length polymorphism (T-RFLP) analysis are often applied in the studies to enable the characterization of the microbial population. However, such data have so far been primarily analyzed using conventional clustering methods. Here we introduce a Bayesian model-based method for the purpose of comparing microbial communities using T-RFLP data. Such datasets have in general several challenging features, e.g. sparseness, missing values and structurally zero-valued observations. These features are taken into account by developing a Bayesian latent class mixture model for the observations in our framework. To make inferences under the model we use a recent Markov chain Monte Carlo (MCMC) -based method for the Bayesian model selection. To assess the introduced method we analyze both simulated and real datasets. The simulations show that our approach compares preferably to standard statistical clustering tools, such as k-means, hierarchical clustering, and Autoclass. The developed tool is freely available as a software package T-BAPS at http://www.abo.fi/fak/mnf/mate/jc/software/t-baps.html.

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