Genome-based microbiological safety assessment of traditional italian foods

The microbiological risk assessment (MRA) is a main component of the EU strategy for food safety. In the last years the classical methodologies for MRA, based on epidemiological data and physiological characterization of isolate strains, has been complemented with genomics and, more recently, metagenomics and other omics approaches. Although genomic analyses are already embedded in the risk assessment procedures, these has been rarely applied to the study of traditional food. Moreover, the currently used approaches present limitations, e.g. in the antimicrobial resistance (AMR) genes identification (intrinsic versus acquired resistances) and virulence characterization.

UNICATT will contribute to the culture collection of ONFOODS, composed by relevant microbial strains, for which the whole genome sequencing (WGS) is available or will be elucidated during ONFOODS activities. Data (origin, phenotypic and molecular characterization, WGS, annotation) will be included in the database. UNICATT will focus on traditional Italian foods of animal origin (cheese and meat products), including protected denomination of origin (PDO) foods. Pathogens (e.g. STEC, B. cereus, Salmonella, L. monocytogenes) and potential pathogens (e.g. enterococci) will be isolated, WGS determined and analysed, focusing identification, virulence determinants, AMR genes (intrinsic/acquired), mobile genetic island and environmental adaptation factors. Phylogenomics analysis will be performed on the strains isolated from the different units and traditional foods. Innovative approach will be used in the WGS analysis overcoming the existing limitations in the identification of intrinsic or acquired AMR and significant virulence factors.

The main aim is the WGS-based risk assessment of the pathogenic bacteria from the traditional Italian foods, a fundamental step to design risk mitigation strategies to reduce the risks associated with newly identified, emerging and re-emerging risks (e.g. STEC in raw milk cheese, Salmonella in dry fermented sausages). The omic approaches and the comparison of strains included in the database also with those isolated from other sources, including the clinical samples, will provide a better knowledge of the features (virulence, AMR and environmental adaption) of strains from traditional foods. Moreover, bioinformatic analyses for safety assessment will be constructed to ensure error-free diagnostics and thorough wet-lab procedures will be carried out to validate WGS assessment.