4.6 Article

Numerical analysis of grassland bacterial community structure under different land management regimens by using 16S ribosomal DNA sequence data and denaturing gradient gel electrophoresis banding patterns

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APPLIED AND ENVIRONMENTAL MICROBIOLOGY
卷 67, 期 10, 页码 4554-4559

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AMER SOC MICROBIOLOGY
DOI: 10.1128/AEM.67.10.4554-4559.2001

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Bacterial diversity in unimproved and improved grassland soils was assessed by PCR amplification of bacterial 16S ribosomal DNA (rDNA) from directly extracted soil DNA, followed by sequencing of similar to 45 16S rDNA clones from each of three unimproved and three improved grassland samples (A. E. McCaig, L. A. Glover, and J. I. Prosser, Appl. Environ. Microbiol. 65:1721-1730, 1999) or by denaturing gradient gel electrophoresis (DGGE) of total amplification products. Semi-improved grassland soils were analyzed only by DGGE. No differences between communities were detected by calculation of diversity indices and similarity coefficients for clone data (possibly due to poor coverage). Differences were not observed between the diversities of individual unimproved and improved grassland DGGE profiles, although considerable spatial variation was observed among triplicate samples. Semi-improved grassland samples, however, were less diverse than the other grassland samples and had much lower within-group variation. DGGE banding profiles obtained from triplicate samples pooled prior to analysis indicated that there was less evenness in improved soils, suggesting that selection for specific bacterial groups occurred. Analysis of DGGE profiles by canonical variate analysis but not by principal-coordinate analysis, using unweighted data (considering only the presence and absence of bands) and weighted data (considering the relative intensity of each band), demonstrated that there were clear differences between grasslands, and the results were not affected by weighting of data. This study demonstrated that quantitative analysis of data obtained by community profiling methods, such as DGGE, can reveal differences between complex microbial communities.

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