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Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA
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We describe a new molecular approach to analyzing the genetic diversity of complex microbial populations. This technique is based on the separation of polymerase chain reaction-amplified fragments of genes coding for 16S rRNA, all the same length, by denaturing gradient gel electrophoresis (DGGE). DGGE analysis of different microbial communities demonstrated the presence of up to 10 distinguishable bands in the separation pattern, which were most likely derived from as many different species constituting these populations, and thereby generated a DGGE profile of the populations. We showed that it is possible to identify constituents which represent only 1% of the total population. With an oligonucleotide probe specific for the V3 region of 16S rRNA of sulfate-reducing bacteria, particular DNA fragments from some of the microbial populations could be identified by hybridization analysis. Analysis of the genomic DNA from a bacterial biofilm grown under aerobic conditions suggests that sulfate-reducing bacteria, despite their anaerobicity, were present in this environment. The results we obtained demonstrate that this technique will contribute to our understanding of the genetic diversity of uncharacterized microbial populations.
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Metadata
- Authors
- Muyzer, G., de Waal, E. C., & Uitterlinden, A. G.
- Venue
- Applied and Environmental Microbiology
- Publisher
- American Society for Microbiology
- Published
- 1993-03
- Volume
- 59
- Issue
- 3
- Pages
- 695-700
- ISSN
- 0099-2240 (ISSN-L)
- 1098-5336
- OpenAlex
- W2111858111
- Cited by
- 11,429
- References
- 1
- FWCI
- 38.85
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.