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Data from: Genome-wide identification of host-segregating epidemiological markers for source attribution in Campylobacter jejuni

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Jan 09, 2018 version files 369.06 MB

Abstract

Campylobacter is among the most common worldwide causes of bacterial gastroenteritis. This organism is part of the commensal microbiota of numerous host species, including livestock, and these animals constitute potential sources of human infection. Molecular typing approaches, especially multi-locus sequence typing (MLST), have been used to attribute the source of human campylobacteriosis by quantifying the relative abundance of alleles, at 7 MLST loci, among isolates from animal reservoirs and human infection, implicating chicken as a major infection source. The increasing availability of bacterial genomes provides data on allelic variation at loci across the genome, providing the potential to improve the discriminatory power of data for source attribution. Here we present a source attribution approach based on the identification of novel epidemiological markers among a reference pan-genome list of 1810 genes identified through gene-by-gene comparison of 884 genomes of C. jejuni isolates from animal reservoirs, the environment and clinical cases. Fifteen loci, involved in metabolic activities, protein modification, signal transduction and stress response, or coding for hypothetical proteins, were selected as host-segregating markers and used to attribute the source of 42 French and 281 UK clinical C. jejuni isolates. Consistent with previous studies of British campylobacteriosis, analyses performed using STRUCTURE software, attributed 56.8% of British clinical cases to chicken, emphasizing the importance of this host reservoir as an infection source in the UK. However, among French clinical isolates, approximately equal proportions of isolates were attributed to chicken and ruminant reservoirs suggesting possible differences in the relative importance of animal host reservoirs and indicating a benefit for further national-scale attribution modelling to account for differences in production, behaviour and food consumption.