Toward an Integrated Genome-Based Surveillance of Salmonella enterica in Germany - Uelze, Laura; Becker, Natalie; Borowiak, Maria; Busch, Ulrich; Dangel, Alexandra; Deneke, Carlus; Fischer, Jennie; Flieger, Antje; Hepner, Sabrina; Huber, Ingrid; Methner, Ulrich; Linde, Jörg; et al. (Frontiers in Microbiology, 2021)
Despite extensive monitoring programs and preventative measures, Salmonella spp. continue to cause tens of thousands human infections per year, as well as many regional and international food-borne outbreaks, that are of great importance for public health and cause significant socio-economic costs. In Germany, salmonellosis is the second most common cause of bacterial diarrhea in humans and is associated with high hospitalization rates. Whole-genome sequencing (WGS) combined with data analysis is a high throughput technology with an unprecedented discriminatory power, which is particularly well suited for targeted pathogen monitoring, rapid cluster detection and assignment of possible infection sources. However, an effective implementation of WGS methods for large-scale microbial pathogen detection and surveillance has been hampered by the lack of standardised methods, uniform quality criteria and strategies for data sharing, all of which are essential for a successful interpretation of sequencing data from different sources. To overcome these challenges, the national GenoSalmSurv project aims to establish a working model for an integrated genome-based surveillance system of Salmonella spp. in Germany, based on a decentralised data analysis. Backbone of the model is the harmonisation of laboratory procedures and sequencing protocols, the implementation of open-source bioinformatics tools for data analysis at each institution and the establishment of routine practices for cross-sectoral data sharing for a uniform result interpretation. With this model, we present a working solution for cross-sector interpretation of sequencing data from different sources (such as human, veterinarian, food, feed and environmental) and outline how a decentralised data analysis can contribute to a uniform cluster detection and facilitate outbreak investigations.