Combination of in silico and proteomic approaches to identify candidate genes responsible for the immunomodulatory properties of Propionibacterium freudenreichii
Résumé
Recent works have revealed highly strain-dependent immunomodulatory properties in Propionibacterium freudenreichii, a cheese starter bacterium (Foligné et al. 2010). The underlying mechanisms are unknown but preliminary attempts have shown that surface components are involved in these immunomodulatory effects. The most promising strains/components exert promising anti-inflammatory effects through induction of regulatory cytokines. The aim of this work is to identify key surface components of Propionibacterium freudenreichii responsible for the immunomodulatory properties. 12 strains of Propionibacterium freudenreichii were sorted according to their high or low regulatory properties, measured by induction of IL-10 cytokine in peripheral blood mononuclear cells stimulated by equal amounts of the different P. freudenreichii strains. These strains were sequenced by Illumina paired-end sequencing and de novo assembled using Velvet software. Genome sequences were automatically and manually annotated on the INRA-AGMIAL platform. Correlations are studied both (i) between quantitative traits (high or low induction of IL-10) and genotypic properties, namely presence/lack of in silico predicted proteins and (ii) between quantitative traits (high or low induction of IL-10) and in vitro data (surface exposed proteins). In both cases, we account for the phylogenetic inertia induced by the shared evolutionary history of the strains. For in vitro data, three methods (shedding using guanidium chloride, surface labeling using CyDye cyanine and shaving using trypsine combined with mass spectrometry) are used to identify P. freudenreichii surface proteins. Statistical analysis is used to highlight which of these proteins can potentially be involved in immunomodulation, comparing the proteins identified and IL-10 induction. Statistical analyses of both in vitro and in silico approaches based on genome sequencing reveal targets potentially involved in immunomodulation. Both strategies are complementary. Proteomic approach followed by statistical analysis allow the identification of surface exposed proteins associated with immunomodulation, whereas in silico approach point out the mechanisms explaining the presence/ absence of the candidates on bacterial surface. Genome sequences, either directly used (in silico approach) or used as database (in vitro approach) determine key genes involved in the studied phenotypes. Candidate genes identified in the present study will be further characterized and their role in immunomodulation confirmed by knock out or overexpression of selected genes.