Open AccessEvaluation of nested PCR–DGGE (denaturing gradient gel electrophoresis) with group‐specific 16S rRNA primers for the analysis of bacterial communities from different wastewater treatment plants
Author(s) -
Boon Nico,
Windt Wim,
Verstraete Willy,
Top Eva M.
Publication year - 2002
Publication title -
fems microbiology ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.377
H-Index - 155
eISSN - 1574-6941
pISSN - 0168-6496
DOI - 10.1111/j.1574-6941.2002.tb00911.x
Subject(s) - temperature gradient gel electrophoresis , biology , 16s ribosomal rna , activated sludge , proteobacteria , microbial population biology , gel electrophoresis , bacteria , microbiology and biotechnology , community structure , ribosomal rna , genetics , sewage treatment , ecology , gene , engineering , waste management
The diversity of bacterial groups of activated sludge samples that received wastewater from four different types of industry was investigated by a nested PCR–DGGE (denaturing gradient gel electrophoresis) approach. Specific 16S rRNA primers were chosen for large bacterial groups (Bacteria and α‐Proteobacteria in particular), which dominate activated sludge communities, as well as for actinomycetes, ammonium oxidisers and methanotrophs (types I and II). In addition primers for the new Acidobacterium kingdom were used to observe their community structure in activated sludge. After this first PCR amplification, a second PCR with bacterial primers yielded 16S rRNA gene fragments that were subsequently separated by DGGE, thus generating ‘group‐specific DGGE patterns’. The community structure and diversity of the bacterial groups from the different samples was further analysed using different techniques, such as statistical analysis and Shannon diversity index evaluation of the band patterns. By combining the seven DGGE gels, cluster analysis, multidimensional scaling and principal component analysis clearly clustered two of the four activated sludge types separately. It was shown that the combination of molecular and statistical methods can be very useful to differentiate microbial communities.