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Environ Microbiol. 2004 Sep;6(9):928-37.

Suppressive subtractive hybridization as a tool for identifying genetic diversity in an environmental metagenome: the rumen as a model.

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1
Department of Animal Sciences, University of Illinois at Urbana-Champaign, 1207 W. Gregory Drive, Urbana, IL 61801, USA.

Abstract

Molecular techniques previously used for genome comparisons of closely related bacterial species could prove extremely valuable for comparisons of complex microbial communities, or metagenomes. Our study aimed to determine the breadth and value of suppressive subtractive hybridization (SSH) in a pilot-scale analysis of metagenomic DNA from communities of microorganisms in the rumen. Suppressive subtractive hybridization was performed using total genomic DNA isolated from rumen fluid samples of two hay-fed steers, arbitrarily designated as tester or driver. Ninety-six subtraction DNA fragments from the tester metagenome were amplified, cloned and the DNA sequences were determined. Verification of the isolation of DNA fragments unique to the tester metagenome was accomplished through dot blot and Southern blot hybridizations. Tester-specific SSH fragments were found in 95 of 96 randomly selected clones. DNA sequences of subtraction fragments were analysed by computer assisted DNA and amino acid comparisons. Putative translations of 26 (32.1%) subtractive hybridization fragments exhibited significant similarity to Bacterial proteins, whereas 15 (18.5%) distinctive subtracted fragments had significant similarity to proteins from Archaea. The remainder of the subtractive hybridization fragments displayed no similarity to GenBank sequences. This metagenomic approach has exposed an unexpectedly large difference in Archaeal community structure between the rumen microbial populations of two steers fed identical diets and housed together. 16S rRNA dot blot hybridizations revealed similar proportions of Bacteria and Archaea in both rumen samples and suggest that the differences uncovered by SSH are the result of varying community structural composition. Our study demonstrates a novel approach to comparative analyses of environmental microbial communities through the use of SSH.

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