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Differences in Faecal Microbiome Taxonomy, Diversity and Functional Potential in a Bovine Cohort Experimentally Challenged with Mycobacterium avium subsp. paratuberculosis (MAP).

Chloe MatthewsAaron M WalshStephen Vincent GordonBryan K MarkeyPaul D CotterJim O' Mahony
Published in: Animals : an open access journal from MDPI (2023)
Mycobacterium avium subspecies paratuberculosis (MAP) is the causative agent of Johne's disease in ruminants, a chronic enteritis which results in emaciation and eventual loss of the animal. Recent advances in metagenomics have allowed a more in-depth study of complex microbiomes, including that of gastrointestinal tracts, and have the potential to provide insights into consequences of the exposure of an animal to MAP or other pathogens. This study aimed to investigate taxonomic diversity and compositional changes of the faecal microbiome of cattle experimentally challenged with MAP compared to an unexposed control group. Faecal swab samples were collected from a total of 55 animals [exposed group ( n = 35) and a control group ( n = 20)], across three time points (months 3, 6 and 9 post-inoculation). The composition and functional potential of the faecal microbiota differed across time and between the groups ( p < 0.05), with the primary differences, from both a taxonomic and functional perspective, occurring at 3 months post inoculation. These included significant differences in the relative abundance of the genera Methanobrevibacter and Bifidobacterium and also of 11 other species (4 at a higher relative abundance in the exposed group and 7 at a higher relative abundance in the control group). Correlations were made between microbiome data and immunopathology measurements and it was noted that changes in the microbial composition correlated with miRNA-155, miR-146b and IFN-ɣ. In summary, this study illustrates the impact of exposure to MAP on the ruminant faecal microbiome with a number of species that may have relevance in veterinary medicine for tracking exposure to MAP.
Keyphrases
  • high density
  • immune response
  • microbial community
  • artificial intelligence
  • antibiotic resistance genes
  • electronic health record
  • gram negative
  • big data
  • multidrug resistant
  • deep learning