A machine learning approach reveals key determinants of the porcine fecal metabolome in the context of Salmonella colonization and/or deoxynivalenol intoxication

Abstract

International audienceIn Europe, pork remains a constant concern, both for human health due to Salmonella circulation in production and for animal health due to the recurrent presence of mycotoxins such as deoxynivalenol in cereal-based diets. These foodborne hazards, often studied separately despite their frequent co-occurence in pigs, affect the host’s immune system and seric metabolome in distinct ways. Salmonella intestinal colonization has been shown to induce metabolic shifts in pig serum, while deoxynivalenol alters biochemical pathways in piglet serum. To complete serum metabolome aspects, we investigated the individual and combined impacts of these exogenous inputs on the fecal metabolome. Four exposure groups (10 pigs each) were set up: Sal-DON-, Sal-DON+, Sal+DON-, and Sal+DON+. From 6 weeks of age, DON+ pigs were fed daily with DON-contaminated feed (3 mg/kg of food, D-7). At 7 weeks of age, Sal+ pigs were orally inoculated one time with the monophasic variant of Salmonella Typhimurium (10^9 CFU/pig, D0). Pigs’ feces were sampled at D15. Non-targeted metabolomic profiling using liquid chromatography-tandem mass spectrometry revealed significant changes in negatively charged signals in response to these hazards, either separately or together. In a context of Salmonella colonization, the presence of DON didn’t bring visible effects on the metabolomic composition. In the context of DON exposure, Salmonella exerted an additional effect on metabolome. Metabolites characterizing each exposure specifically were extracted using a machine-learning approach based on sPLS-DA. Two metabolites associated with Sal-DON- and Sal-DON+ were retained with 35 and 8 hits assigned to them in MS, respectively. MS2 procedures enabled the identification of one of these two metabolites, a candidate biomarker, 3-ethynyl-1H-indole, specifically distinguishing Sal-DON- from the other groups. This study highlights the potential of fecal metabolome biomarkers for detecting Salmonella colonization and mycotoxin exposure in pigs, leading to innovative diagnostic strategies and better understanding of interactions in the gut