Lactobacillaceae and Cell Adhesion: Genomic and Functional Screening

The analysis of collections of lactic acid bacteria (LAB) from traditional fermented plant foods in tropical countries may enable the detection of LAB with interesting properties. Binding capacity is often the main criterion used to investigate the probiotic characteristics of bacteria. In this study, we focused on a collection of 163 Lactobacillaceace comprising 156 bacteria isolated from traditional amylaceous fermented foods and seven strains taken from a collection and used as controls. The collection had a series of analyses to assess binding potential for the selection of new probiotic candidates. The presence/absence of 14 genes involved in binding to the gastrointestinal tract was assessed. This enabled the detection of all the housekeeping genes (ef-Tu, eno, gap, groEl and srtA) in the entire collection, of some of the other genes (apf, cnb, fpbA, mapA, mub) in 86% to 100% of LAB, and of the other genes (cbsA, gtf, msa, slpA) in 0% to 8% of LAB. Most of the bacteria isolated from traditional fermented foods exhibited a genetic profile favorable for their binding to the gastrointestinal tract. We selected 30 strains with different genetic profiles to test their binding ability to non-mucus (HT29) and mucus secreting (HT29-MTX) cell lines as well as their ability to degrade mucus. Assays on both lines revealed high variability in binding properties among the LAB, depending on the cell model used. Finally, we investigated if their binding ability was linked to tighter cross-talk between bacteria and eukaryotic cells by measuring the expression of bacterial genes and of the eukaryotic MUC2 gene. Results showed that wild LAB from tropical amylaceous fermented food had a much higher binding capacity than the two LAB currently known to be probiotics. However their adhesion was not linked to any particular genetic equipment

Le Village suisse comme modèle d'urbanisme

This chapter introduces systems biology, its context, aims, concepts and strategies. It then describes approaches and methods used for collection of high-dimensional structural and functional genomics data, including epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis and bioinformatics. Finally, the most advanced mathematical and computational methods used for clustering, feature selection, prediction analysis, text mining and pathway analysis in functional genomics and systems biology are reviewed and discussed in the context of use cases