Semi-automated genetic analyses of soil microbial communities: comparison of T-RFLP and RISA based on descriptive and discriminative statistical approaches

Abstract

Cultivation independent analyses of soil microbial community structures are frequently used to describe microbiological soil characteristics. This approach is based on direct extraction of total soil DNA followed by PCR amplification of selected marker genes and subsequent genetic fingerprint analyses. Semi-automated genetic fingerprinting techniques such as terminal restriction fragment length polymorphism (T-RFLP) and ribosomal intergenic spacer analysis (RISA) yield high-resolution patterns of highly diverse soil microbial communities and hold great potential for use in routine soil quality monitoring, when rapid high throughput screening for differences or changes is more important than phylogenetic identification of organisms affected. Our objective was to perform profound statistical analysis to evaluate the cultivation independent approach and the consistency of results from T-RFLP and RISA. As a model system, we used two different heavy metal treated soils from an open top chamber experiment. Bacterial T-RFLP and RISA profiles of 16S rDNA were converted into numeric data matrices in order to allow for detailed statistical analyses with cluster analysis, Mantel test statistics, Monte Carlo permutation tests and ANOVA. Analyses revealed that soil DNA-contents were significantly correlated with soil microbial biomass in our system. T-RFLP and RISA yielded highly consistent and correlating results and both allowed to distinguish the four treatments with equal significance. While RISA represents a fast and general fingerprinting method of moderate cost and labor intensity, T-RFLP is technically more demanding but offers the advantage of phylogenetic identification of detected soil microorganisms. Therefore, selection of either of these methods should be based on the specific research question under investigation.