Multi-omics analysis reveals regime shifts in the gastrointestinal ecosystem in chickens following anticoccidial vaccination andEimeria tenellachallenge

Po-Yu Liu, Janie Liaw, Francesca Soutter, José Jaramillo Ortiz,Fiona M. Tomley,Dirk Werling,Ozan Gundogdu,Damer P. Blake,Dong Xia

crossref(2024)

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摘要
Coccidiosis, caused by Eimeria parasites, poses significant economic and welfare challenges in poultry farming. Beyond its direct impact on health, Eimeria infection disrupts enteric microbial populations leading to dysbiosis and increases vulnerability to secondary diseases such as necrotic enteritis, caused by Clostridiumperfringens. The impact of Eimeria infection or anticoccidial vaccination on host gastrointestinal phenotypes and enteric microbiota remains understudied. In this study, the metabolomic profiles and microbiota composition of chicken caecal tissue and contents were evaluated concurrently during a controlled experimental vaccination and challenge trial. Cobb500 broilers were vaccinated with a Saccharomycescerevisiae-vectored anticoccidial vaccine and challenged with 15,000 Eimeriatenella oocysts. Assessment of caecal pathology and quantification of parasite load revealed correlations with alterations to caecal microbiota and host metabolome linked to infection and vaccination status. Infection heightened microbiota richness with increases in potentially pathogenic species, while vaccination elevated beneficial Bifidobacterium. Using a multi-omics factor analysis (MOFA) machine learning model, data on caecal microbiota and host metabolome were integrated and distinct profiles for healthy, infected, and recovering chickens were identified. Healthy and recovering chickens exhibited higher vitamin B metabolism linked to short-chain fatty acid-producing bacteria, whereas essential amino acid and cell membrane lipid metabolisms were prominent in infected and vaccinated chickens. Notably, vaccinated chickens showed distinct metabolites related to the enrichment of sphingolipids, important components of nerve cells and cell membranes. Our integrated multi-omics model revealed latent biomarkers indicative of vaccination and infection status, offering potential tools for diagnosing infection, monitoring vaccination efficacy, and guiding the development of novel treatments or controls.
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