The prevalences of Salmonella Genomic Island 1 variants in human and animal Salmonella Typhimurium DT104 are distinguishable using a Bayesian approach

Research output: Contribution to journalJournal articleResearchpeer-review

  • Alison E Mather
  • Denwood, Matt
  • Daniel T Haydon
  • Louise Matthews
  • Dominic J Mellor
  • John E Coia
  • Derek J Brown
  • Stuart W J Reid

Throughout the 1990 s, there was an epidemic of multidrug resistant Salmonella Typhimurium DT104 in both animals and humans in Scotland. The use of antimicrobials in agriculture is often cited as a major source of antimicrobial resistance in pathogenic bacteria of humans, suggesting that DT104 in animals and humans should demonstrate similar prevalences of resistance determinants. Until very recently, only the application of molecular methods would allow such a comparison and our understanding has been hindered by the fact that surveillance data are primarily phenotypic in nature. Here, using large scale surveillance datasets and a novel Bayesian approach, we infer and compare the prevalence of Salmonella Genomic Island 1 (SGI1), SGI1 variants, and resistance determinants independent of SGI1 in animal and human DT104 isolates from such phenotypic data. We demonstrate differences in the prevalences of SGI1, SGI1-B, SGI1-C, absence of SGI1, and tetracycline resistance determinants independent of SGI1 between these human and animal populations, a finding that challenges established tenets that DT104 in domestic animals and humans are from the same well-mixed microbial population.

Original languageEnglish
JournalPLOS ONE
Volume6
Issue number11
Pages (from-to)e27220
ISSN1932-6203
DOIs
Publication statusPublished - 2011
Externally publishedYes

    Research areas

  • Animals, Anti-Bacterial Agents, Bayes Theorem, Drug Resistance, Multiple, Bacterial, Gene Frequency, Genetic Variation, Genomic Islands, Humans, Markov Chains, Monte Carlo Method, Salmonella Infections, Salmonella Infections, Animal, Salmonella typhimurium, Species Specificity

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 137015139