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DGGE to explore gut microflora

Knowledge on the gut microflora and i ts interact ions wi th feed is becoming more integrated in animal nutri t ion. Veri ty Ann Sat t ler, Viviana Klose and Tobias Steiner from Biomin explain how the DGGE technique can be a useful tool in this matter.

To study the effects of feed additives, such as probiotics and prebiotics, on the microbial populations in the gut, several methods can be applied.
Among microbiologists it is well known that with traditional culture dependent methods only a small fraction of bacteria can be isolated and characterised due to limitations in selective enrichment. Microbial detection is improved by the use of nucleic acid based methodologies that mostly target the 16S ribosomal RNA gene sequence, a sequence marking the signature for bacterial groups (Woese, 1987). Denaturing gradient gel electrophoresis (DGGE) of ribosomal DNA fragments is a promising fingerprinting technique, applied to provide a pattern of genetic diversity of intestinal microorganisms.
By using DGGE, many samples can be processed simultaneously, making it a powerful tool to monitor the development of bacterial community composition and to measure possible changes in populations based upon dietary factors, intestinal compartments or age.
DGGE fingerprinting
The 16S RNA gene sequence contains regions which are conserved among all bacteria, but also regions which are variable and can be highly species-specific. Comparing the 16S rDNA sequence similarities therefore serves as identification and is consequently used to analyse bacterial communities. In a polymerase chain reaction (PCR) the 16S rDNA sequences from the bacterial community of a sample of gut content are amplified and subsequently can be separated by DGGE. 16S 'species' can be distinguished by this electrophoretic method, showing a pattern of bands, which represents the composition of species in the original sample.
Therefore, this molecular methodology is also known as genetic fingerprinting. As with every method molecular techniques are also afflicted with biases and errors. The way of sampling, e.g. the choice of the sample region in the gut (upper, lower ileum or colon) or the handling of the sample under aerobic versus anaerobic conditions, can already have great influence on the results. Furthermore, the extraction of nucleic acids from cells in the sample may be biased due to inefficient cell lysis and removal of contaminants,which may inhibit PCR amplification in subsequent analysing steps. In general, DGGE will display the fragments from predominant species which constitute at least 1% of the total community.
Sensitivity can be improved by the use of group- or species specific primers, which have already been used for the amplification of the 16S ribosomal DNA from Lactobacillus or Bifidobacteria. (Satokari et al., 2001; Heilig et al., 2002). It should be made clear that species identification cannot be achieved with DGGE. Therefore, the DNA fragments from one band of the gel should be excised for subsequent cloning and sequencing of the PCR fragment. Besides all limitations, DGGE is a very reliable, rapid and reproducible technique to study a complex microflora.
Interpretation of DGGE fingerprints
To obtain an objective interpretation of complex DGGE fingerprints, specialised computer software programmes are used. Statistical analysis, cluster analysis or diversity analysis can be achieved, depending on the question of interest. As part of a current study, a feeding trial was designed to measure changes in intestinal bacterial populations in response to probiotic and prebiotic administration. Additionally, DGGE banding patterns of different gut compartments (ileum and colon) were analyzed in order to see diversity changes independent from feed quality. Therefore, eight pigs from the control feeding group were sacrificed and content of ileum and colon was obtained to be further prepared for DGGE analysis.
The much higher bacterial
diversity in samples collected from the colon in contrast to the ones collected from the ileum could be revealed by means of the GelcompareII software (Applied Maths). The complexity for a single sample can be expressed by diversity indices (e.g. Shannon's). With this diversity value, changes in bacterial community composition based on differences in diet or age may be measured.
In another study (Konstantinov et. al. 2004) the bacterial community of ileum and colon of weaning piglets were analysed in response to addition of four different fermentable carbohydrates (inulin, lactulose, wheat starch, and sugar beet pulp). Using DGGE, based on amplified 16S rRNA genes, a higher number of bands in the colon than in the ileum, as well as a significantly higher diversity in the colonic microflora of pigs fed the fermentable-carbohydrate-enriched diet was observed. However, diversity and community structure may also be influenced by the host genotype, which would mean that the environment has less impact or is just less visible. Nevertheless, this genetic linkage also has to be considered in future investigations.
Source: Feed Mix Mix magazine. Volume 16. No.1

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