Intestinal microflora with DGGE
Exploring the intestinal microflora with DGGE
Published: October 6, 2008
By: Michaela Mohnl, Product Manager - BIOMIN GmbH
One of the most complex microbial ecosystems, which exists in our world is found within us human and all animals, namely in the gastrointestinal tract (GIT). The important role of gastrointestinal microflora in health and disease of animals and humans is increasingly recognized, which makes it interesting for scientists but also animal nutritionists and veterinarians to explore and understand the interplay between microbes and host in order to develop strategies how to protect animals from enteric diseases and to improve efficiency in economic livestock farming. We are far away from a vast understanding about the intestinal composition of bacteria due to limitations of analysis methods like culture dependent techniques. These limitations can be overcome by the use of molecular methods like Denaturing Gradient Gel Electrophoresis (DGGE). The composition of the intestinal microflora is largely dependent on type of nutrition and environment. Natural feed additives like pro- and prebiotics do not only influence performance standards of farm animals but have also a positive impact on the composition of the microbial population in the gastrointestinal tract thus protecting young animals against colonization by harmful bacteria. DGGE is a powerful tool to investigate the effect of feed additives such as probiotics and prebiotics on the GI microbiota and thus obtain a better understanding how to manage the microbial communities in the gut of our animals by nutritional means for better health and improved performance.
Denaturing Gradient Gel Electrophoresis (DGGE)
Denaturing gradient gel electrophoresis (DGGE) is a nucleic acid based (DNA or RNA) technique which can be used to profile and identify dominant members of the microbial community based on their genetic fingerprint. Using this approach, differences in the genetic sequence of the 16S rRNA gene (28S for fungi) allow DNA from various microorganisms to physically separate and generate a profile of the dominant members which become visible as bands in a gel image (see Figure 1).
In a DGGE gel each band represents a different bacterial gut inhabitant and each lane displays the genetic fingerprint of the total bacterial community within the gastrointestinal compartment. The banding
patterns and relative intensities of the bands provide a measure of difference among the communities. DGGE does not provide quantifiable data, however, identification of a particular genus suggests it is a major member of the microbial community (>1% of the population). Because the gel pattern consists of DNA from the most abundant community members, a shift in the gel pattern from one event to another is indicative of a change in the major members of the community. Bands seen in one gel, but not the other suggest that certain organisms were present as >1% of the community in one event, but not in the other. Whether DGGE is for the entire bacterial population or DGGE uses special primers for particular bacterial groups, a shift in band patterns points to a shift in the microbial community structure. The relative intensity of DGGE bands can be used as a comparative measure of quantification. It should be made clear that species identification cannot be achieved with DGGE. However bands from microorganisms which constitute at least 1-2% of the total community can be excised and sequence analysis can be used to determine the identity of that microorganism. By using DGGE, many samples can be processed simultaneously, making DGGE a powerful tool to monitor the development of bacterial community composition of the host animal over time and to measure possible changes in populations based upon dietary factors, intestinal compartments or age (Muyzer et al., 1993). As with every method also molecular techniques are 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 analyzing steps. Sensitivity can be improved by the use of group- or species specific primers. Besides all limitations, DGGE is a very reliable, rapid and reproducible technique to study a complex microflora.
Figure 1: DGGE gel: analysis of intestinal content of ileum, colon and caecum of piglets using bifido specific primers: reference marker (lane 1, 7, 14); ileum samples (lane 2, 3, 4, 5), colon samples (lane 6, 8, 9, 10); caecum samples (lane 11, 12, 13)
Interpretation of DGGE fingerprints
To obtain an objective interpretation of complex DGGE fingerprints, specialized computer software programs are used. Statistical analysis, cluster analysis or diversity analysis can be achieved, depending on the question of interest. The complexity for a single sample can be expressed by diversity indices (e.g. Shannon’s). Diversity indices can be used to assess the diversity of any population in which each member belongs to a unique species. With this diversity value, changes in bacterial community composition based on differences in diet or age may be measured.
Microbial diversity and richness in the gut
Biological diversity can be quantified in many different ways. The two main factors taken into account when measuring diversity are richness and evenness. Richness is a measure of the number of different kinds of organisms present in a particular area. For example, species richness is the total number of different species present in a community. Evenness is a measure of the relative abundance of the different species making up the richness of an area. As species richness and evenness increase, so diversity increases. It is often assumed that diversity stabilizes ecosystems. Species diversity impacts a number of processes in ecological communities, including productivity, stability, and susceptibility to invasive species (Hooper et al., 2005).
Studies suggest that higher species richness in gut microbiota is associated with decreased ability of pathogens to colonize the gut (Dillon et al., 2005). Bacterial species may facilitate each other’s growth, perhaps due to more effective resource use when more species are present. This outcome would leave less niche space for invaders, including pathogens.
Investigating the effect of probiotics and prebiotics on the intestinal microflora of piglets
A feeding trial was designed to develop and evaluate DGGE to detect possible changes in the intestinal bacterial populations of weaning piglets in response to probiotic, prebiotic and synbiotic (combination of probiotic and prebiotic) administration (Sattler et al., 2008).
The experimental groups, each comprised of 12 individual weaning piglets were:
(i) control group fed with untreated feed;
(ii) test group fed with multi-strain probiotic;
(iii) test group fed with multi-strain probiotic and prebiotics; (iv) test group fed with prebiotics
Comparisons between DGGE fingerprints of universal bacterial PCR fragments of ileum, caecum and colon samples (n=8) within and between dietary groups gave first important insights. Two parameters, number of DGGE bands to measure species richness and Shannon index to describe the diversity of the microbial community were assessed to compare species diversity between different gut compartments and between piglets (n=32) from four different feeding trial groups. Based on both parameters, basically, a low eubacterial diversity in ileum samples was found increasing in the caecum and revealing highest diversity and species richness in the colon. When comparing bacterial diversity and richness between piglets fed different diets, results revealed significant differences (P < 0.05) between the pro- and prebiotic feeding groups and the control group, indicating a pro- and/or prebiotic effect of the microbial and the prebiotic feed additives (see Figure 4). Concerning the bifidobacterial community, it could be shown that microbial richness and diversity within a feeding group were similar. However, comparison with the control group showed that combination of pro- and prebiotic feed significantly enhanced the bifidobacterial population in caecum samples (Wegl et al., 2008).
4 b)
Figure 4: Comparison of average number of bands (a) and Shannon diversity indices (b) detected in DGGE fingerprints of colon, caecum and ileum samples (n=8) from different dietary groups. Statistical significant differences (P < 0.05) within groups are marked with letters a, b and between groups with *.
Conclusion
This study clearly demonstrated that the DGGE is a suitable tool to display and detect differences in the bacterial community composition of the animals’ gut in response to dietary treatments. It helps to better understand the very complex bacterial dynamics in the gut.
Literature
Dillon,R.J., Vennard,C.T., Buckling, A., et al. (2005) Diversity of locust gut bacteria protects against pathogen invasion. Ecol Lett 8: 1291–98.
Hooper,D.U., Chapin,F.S., Ewel, J.J., et al. (2005) Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol Monogr 75: 3–35.
Muyzer,G., de Waal,E.C., and Uitterlinden,A.G. (1993) Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol 59: 695-700.
Sattler,V.A., Klose,V., Wegl,G., Nitsch,S., Loibner,A.P. and G. Schatzmayer (2008) Exploring the gut microflora with DGGE. 20th IPVS Congress June 22-26, 2008 Durban, South Africa, Abstracts page 539
Wegl,G., Sattler,V.A., Plitzner,C., Nitsch, S., Schatzmayer,G. and V. Klose (2008) Molecular Analysis of the Porcine Gut Microflora in Response to Feeding with Probiotics and Prebiotics. 6th INRA-RRI Symposium June 18-20, 2008 Clermont-Ferrand, France, Abstracts page 37
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DSM-Firmenich
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21 de marzo de 2018
Hello, my name is Nicolas and I was wondering how can you exploit the results with only the gel when you don't have any software?
If it's not possible, can you give me the name of the software allowing you to have these results?
Cordially,
Nicolas.
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