Present address: Martin Hartmann, Department of Microbiology and Immunology, University of British Columbia, 2350 Health Sciences Mall, Life Sciences Centre, Vancouver, BC, Canada V6T 1Z3.
Reliability for detecting composition and changes of microbial communities by T-RFLP genetic profiling
Article first published online: 2 JAN 2008
FEMS Microbiology Ecology
Volume 63, Issue 2, pages 249–260, February 2008
How to Cite
Hartmann, M. and Widmer, F. (2008), Reliability for detecting composition and changes of microbial communities by T-RFLP genetic profiling. FEMS Microbiology Ecology, 63: 249–260. doi: 10.1111/j.1574-6941.2007.00427.x
Editor: Jim Prosser
- Issue published online: 2 JAN 2008
- Article first published online: 2 JAN 2008
- Received 21 June 2007; revised 25 October 2007; accepted 28 October 2007.First published online January 2008.
- genetic profiling;
- artificial community;
- community structure
Terminal restriction fragment length polymorphism (T-RFLP) analysis is commonly used for profiling microbial communities in various environments. However, it may suffer from biases during the analytic process. This study addressed the potential of T-RFLP profiles (1) to reflect real community structures and diversities, as well as (2) to reliably detect changing components of microbial community structures. For this purpose, defined artificial communities of 30 SSU rRNA gene clones, derived from nine bacterial phyla, were used. PCR amplification efficiency was one primary bias with a maximum variability factor of 3.5 among clones. PCR downstream analyses such as enzymatic restriction and capillary electrophoresis introduced a maximum bias factor of 4 to terminal restriction fragment (T-RF) signal intensities, resulting in a total maximum bias factor of 14 in the final T-RFLP profiles. In addition, the quotient between amplification efficiency and T-RF size allowed predicting T-RF abundances in the profiles with high accuracy. Although these biases impaired detection of real community structures, the relative changes in structures and diversities were reliably reflected in the T-RFLP profiles. These data support the suitability of T-RFLP profiling for monitoring effects on microbial communities.