Identification of unique bacteria among different animal species using denaturing gradient gel electrophoresis (DGGE) and single strand conformation polymorphism (SSCP) methods.

Fecal contamination of water bodies is a widespread problem throughout the world. The United States Environmental Protection Agency (EPA) reported about 21,000 water bodies in the United States are contaminated with feces from both animal and human sources (EPA, 2001). This study aimed to find unique fingerprints among different animal species to use for source identification and calculating the Total Maximum Daily Load (TMDL) for a body of water. Two methods were used (DGGE and CE-SSCP) to separate the bacterial species contained in the fecal samples collected from different animal species including cow, pig, chicken, chicken litter, deer, goose and horse. Comparing the DGGE band patterns by location using Idrisi software revealed unique bacteria for the animal species from Arkansas and Tennessee. Comparing the DGGE band patterns using the Dice Similarity Coefficient showed very large difference between the bacterial species with regard to their band patterns (>90% similarity level). This revealed high % of difference between the animal species indicate that there is chance to established fingerprints out of this study using DGGE. In addition, similar species comparison showed some similarity but also showed uniqueness for each species. This finding might indicate the weakness of choosing the total fecal bacteria to establish the fingerprints instead of selecting one bacterium indicator instead. This study used CE-SSCP method in addition to DGGE to evaluate the discriminatory power for differentiating between the bacterial species. The CE-SSCP did not reveal the number of peaks expected and calculated from the DGGE band patterns (#of bands x2). Many factors are suggested to be responsible for such result including polymer type and concentration (viscosity), the preferential PCR amplification (DNA concentration), and co-migration (two conformers). In addition, the Electropherogram of the samples did not show well separated peaks suggesting that maybe that was due to polymer type and concentration used (3.5% PDMA) and also because of the instrument limitations better polymer type (LPA) could not be injected in higher concentrations (>3.5%). To prove the importance of using higher polymer concentration, 4.5% PDMA was used to separate the pig sample in particular and it showed improvement in terms of increasing the number of peaks suggesting that more viscous polymer is required for better separation. In order to resolve the lower number of peaks resulted from the CE-SSCP, the Electropherograms generated for each animal species were regenerated in the "PeakFit" (V. 4.12) program to reveal the hidden peaks. All of the Electropherograms were overlapped and compared and regardless the lower number of peaks, the CE-SSCP revealed unique fingerprints for the horse and for chicken litter samples that DGGE did not. This finding indicates that both CE-SSCP and DGGE methods are using different baths to differentiate between the bacterial species and/or other factors associated with DGGE process might contributed to this including denaturing gradient concentration, PCR bias and co-migration. However, the findings using Dice Similarity Coefficient indicated that the present study has potential for bacterial fingerprints to be established using DGGE. These bacterial fingerprints were suppose to help with TMDL calculations. Therefore, this study proposed two methods for allocating the bacterial loads among the different animal species which are Luminex beads and Microarrays. The principle for these two methods depends on quantifying the intensity of DNA from the bacterial fingerprints by measuring the hybridization intensity between the target...