Optimization of conditions for profiling bacterial populations in food by culture-independent methods.

In this study we used culture-independent methods to profile bacterial populations in food products. Denaturing gradient gel electrophoresis (DGGE) and fluorescence in situ hybridization (FISH) were employed in order to identify bacterial species without the need of isolation and biochemical identification. The protocols used to extract the DNA, subsequently subjected to PCR amplification for DGGE, as well as the hybridization procedure for FISH, were optimised. Moreover, an extensive study on the primers and probes to be used for the direct detection and identification of microorganisms commonly found in food, was carried out. Meat and cheese samples, fresh or processed, were subjected to DGGE and FISH analysis and the results obtained highlighted how the processing in food industry is decreasing the bacterial biodiversity. Not only processed cheese or meat but also fermented products were dominated by only one or few species. Lactobacillus sakei, Lactobacillus curvatus and Brochothrix thermosphacta were the main species found in meat products, while in cheese(s) Lactococcus lactis, Streptococcus thermophilus and Leuconostoc spp. were repeatedly detected. The results obtained by the two culture-independent methods used always correlated well.

A fuzzy distance metric for measuring the dissimilarity of planar chromatographic profiles with application to denaturing gradient gel electrophoresis data from human skin microbes: demonstration of an individual and gender-based fingerprint.

A newly devised fuzzy metric for measuring the dissimilarity between two planar chromatographic profiles is proposed in this paper. It does not require an accurately assigned sample-feature matrix and can cope with slight imprecision of the positional information. This makes it very suitable for 1-D techniques which do not have a second spectroscopic dimension to aid variable assignment. The usefulness of this metric has been demonstrated on a large data set consisting of nearly 400 samples from Denaturing Gradient Gel Electrophoresis (DGGE) analysis of microbes on human skin. The pattern revealed by this dissimilarity metric was compared with the one represented by a sample-feature matrix and highly consistent results were obtained. Several pattern recognition techniques have been applied on the dissimilarity matrix based on this dissimilarity metric. According to rank analysis, within-individual variation is significantly less than between-individual variation, suggesting a unique individual microbial fingerprint. Principal Coordinates Analysis (PCO) suggests that there is a considerable separation between genders. These results suggest that there are specific microbial colonies characteristic of individuals.