Combined Quantification and Deep Serotyping for Salmonella Risk Profiling in Broiler Flocks

Despite a reduction of Salmonella contamination on final poultry products, the level of human salmonellosis cases attributed to poultry has remained unchanged over the last few years. There needs to be improved effort to target serovars which may survive antimicrobial interventions and cause illness, as well as to focus on lessening the amount of contamination entering the processing plant. Advances in molecular enumeration approaches allow for the rapid detection and quantification of Salmonella in pre- and postharvest samples, which can be combined with deep serotyping to properly assess the risk affiliated with a poultry flock. In this study, we collected a total of 160 boot sock samples from 20 broiler farms across four different integrators with different antibiotic management programs. Overall, Salmonella was found in 85% (68/80) of the houses, with each farm having at least one Salmonella-positive house. The average Salmonella quantity across all four complexes was 3.6 log₁₀ CFU/sample. Eleven different serovars were identified through deep serotyping, including all three key performance indicators (KPIs; serovars Enteritidis, Infantis, and Typhimurium) defined by the U.S. Department of Agriculture-Food Safety and Inspection Service (USDA-FSIS). There were eight multidrug resistant isolates identified in this study, and seven which were serovar Infantis. We generated risk scores for each flock based on the presence or absence of KPIs, the relative abundance of each serovar as calculated with CRISPR-SeroSeq (serotyping by sequencing the clustered regularly interspaced palindromic repeats), and the quantity of Salmonella organisms detected. The work presented here provides a framework to develop directed processing approaches and highlights the limitations of conventional Salmonella sampling and culturing methods. IMPORTANCE Nearly one in five foodborne Salmonella illnesses are derived from chicken, making it the largest single food category to cause salmonellosis and indicating a need for effective pathogen mitigation. Although industry has successfully reduced Salmonella incidence in poultry products, there has not been a concurrent reduction in human salmonellosis linked to chicken consumption. New efforts are focused on improved control at preharvest, which requires improved Salmonella surveillance. Here, we present a high-resolution surveillance approach that combines quantity and identity of Salmonella in broiler flocks prior to processing which will further support improved Salmonella controls in poultry. We developed a framework for this approach, indicating that it is possible and important to harness deep serotyping and molecular enumeration to inform on-farm management practices and to minimize risk of cross-contamination between flocks at processing. Additionally, this framework could be adapted to Salmonella surveillance in other food animal production systems.