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Microbial metabolite deoxycholic acid shapes microbiota against Campylobacter jejuni chicken colonization.

Bilal Alrubaye | Mussie Abraha | Ayidh Almansour | Mohit Bansal | Hong Wang | Young Min Kwon | Yan Huang | Billy Hargis | Xiaolun Sun
PloS one | 2019

Despite reducing the prevalent foodborne pathogen Campylobacter jejuni in chickens decreases campylobacteriosis, few effective approaches are available. The aim of this study was to use microbial metabolic product bile acids to reduce C. jejuni chicken colonization. Broiler chicks were fed with deoxycholic acid (DCA), lithocholic acid (LCA), or ursodeoxycholic acid (UDCA). The birds were also transplanted with DCA modulated anaerobes (DCA-Anaero) or aerobes (DCA-Aero). The birds were infected with human clinical isolate C. jejuni 81-176 or chicken isolate C. jejuni AR101. Notably, C. jejuni 81-176 was readily colonized intestinal tract at d16 and reached an almost plateau at d21. Remarkably, DCA excluded C. jejuni cecal colonization below the limit of detection at 16 and 28 days of age. Neither chicken ages of infection nor LCA or UDCA altered C. jejuni AR101 chicken colonization level, while DCA reduced 91% of the bacterium in chickens at d28. Notably, DCA diet reduced phylum Firmicutes but increased Bacteroidetes compared to infected control birds. Importantly, DCA-Anaero attenuated 93% of C. jejuni colonization at d28 compared to control infected birds. In conclusion, DCA shapes microbiota composition against C. jejuni colonization in chickens, suggesting a bidirectional interaction between microbiota and microbial metabolites.

Pubmed ID: 31276498

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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