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◆ Poultry science2026-09-14

Machine learning identifies key microbial signatures for Salmonella enteritidis colonization and persistence in broiler ceca.

Sabin Poudel, Fanny Abigail Contreras Zelaya, Eva Guadalupe Guzman Guzman, Farazi Abinash Rahman, Muhammad Naeem, Yagya Adhikari, Shijinaraj Manjankattil, Wilmer Pacheco, Ruediger Hauck, Dianna Bourassa, Kenneth Macklin

原始摘要(英文原文)· Original abstract
Non-typhoidal Salmonella is a leading cause for bacterial foodborne illnesses in humans, and contaminated poultry products are considered a major source of transmission. Therefore, reducing Salmonella contamination in poultry products is needed to reduce human salmonellosis. This study examined the effects of organic acid (OA) supplementation and Salmonella persistence/cleared in cecal microbiome of Ross YP × Ross 708 male broilers. A total of 900 1-day-old male broilers were randomly distributed to three treatments Control (CN), acetic, lactic and propionic acid blend (ALP), and citric acid (CA); 12 replicate pens/treatment and 25 birds/pen. All birds were challenged with 106 Cfu of Salmonella Enteritidis at d 7. Birds other than in control received continuous supplementation of OA via drinking water. Cecal samples (n = 12/treatment) were collected on d 41; genomic DNA was extracted; and full-length 16S rRNA was sequenced using MinIon (Oxford Nanopore). Organic acid supplementation did not significantly affect alpha and beta diversity indexes, indicating no major changes in overall microbial diversity and community structure. Birds were categorized into Salmonella persistence or clearance groups based on detection of genus Salmonella in 16S rRNA sequence results. Salmonella detection status (persistence vs clearance) did not significantly alter diversity metrics. However, shared and unique bacterial analysis revealed that control birds harbored more unique bacterial species than organic-acid supplemented and Salmonella-clearance birds harbored higher unique bacterial species than Salmonella-persistence birds. These findings suggest that, despite stable diversity indices, compositional shifts occurred in response to organic acid supplementation as well as presence of Salmonella. Using random forest-based machine learning approach, Escherichia coli, Shigella dysenteriae, and Shigella flexneri were identified to be positively correlated to Salmonella, while Romboutsia timonensis was negatively correlated with Salmonella-presence. In conclusion, this study identifies bacterial species associated with persistence or clearance of Salmonella and identifies a promising candidate that could potentially competitive exclude Salmonella colonization and proliferation in ceca.
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Machine learning identifies key microbial signatures for Salmonella enteritidis colonization and persistence in broiler ceca. — 科研速览 Science Skim