Development of pipeline to create and validate metagenomic datasets enabling microbiota associations with host traits

The microbiota can be depicted as a measurable organ consisting of microbial cells, and creating a unique ecosystem together with the host eukaryotic cells. Therefore, to understand the normal physiology and pathology of animal ecosystems, it is mandatory to tackle a comprehensive analysis of the host, the microbiota, and their interactions. Since over 99.8% of the microbes cannot be cultured, metagenomics offers a path to the study of their community structures and metabolic potential. These goals are achievable thanks to the recent advances in sequencing complex assortments of small genomes, through 16S and WGS approach. In spite of the great number of latest studies, there is not a defined and standardized pipeline to measure the microbial community; rather, significant efforts are needed to optimize sample preparation and data analysis workflows for metagenomics analysis of microbiome.In the present PhD Thesis, a major task is aimed at developing rapid and efficient workflows for metagenomics analysis. These include the choice of the sample, the extraction methods more efficient according to sample features, choice of sequencing approaches and the statistical methods.A developed workflow was successfully applied to investigate to the first time the sheep gut microbiome, to perform association studies linking gut microbiota to T1D host traits in mice and to assess dietary and immunogenetic background impact over gut microbiota in a translational murine model of NAFLD.