A Genome-Wide Association Study to Identify Diagnostic Markers for Human Pathogenic Campylobacter jejuni Strains

Abstract

Campylobacter jejuni is a leading human enteric pathogen worldwide and despite an improved understanding of its biology, ecology, and epidemiology, limited tools exist for identifying strains that are likely to cause disease. In the current study, we used subtyping data in a database representing over 24,000 isolates collected through various surveillance projects in Canada to identify 166 representative genomes from prevalent C. jejuni subtypes for whole genome sequencing. The sequence data was used in a genome-wide association study (GWAS) aimed at identifying accessory gene markers associated with clinically related C. jejuni subtypes. Prospective markers (n = 28) were then validated against a large number (n = 3,902) of clinically associated and non-clinically associated genomes from a variety of sources. A total of 25 genes, including six sets of genetically linked genes, were identified as robust putative diagnostic markers for clinically related C. jejuni subtypes. Although some of the genes identified in this study have been previously shown to play a role in important processes such as iron acquisition and vitamin B5 biosynthesis, others have unknown function or are unique to the current study and warrant further investigation. As few as four of these markers could be used in combination to detect up to 90% of clinically associated isolates in the validation dataset, and such markers could form the basis for a screening assay to rapidly identify strains that pose an increased risk to public health. The results of the current study are consistent with the notion that specific groups of C. jejuni strains of interest are defined by the presence of specific accessory genes.

Frequent Implication of Multistress-TolerantCampylobacter jejuniin Human Infections

“…Consequently, MST C. jejuni strains are more likely to be transmitted to humans than stress-sensitive strains because these strains might be enriched and form unique clones by repeating the cycle of environmental survival, transmission, and human infection. The predominant CGF subtypes in MST clades (0169.001.002, 0044.003.001, and 0083.001.002) are not only prevalent in the CGF collection of human clinical C. jejuni strains in Alberta but also in the Canadian Campylobacter CGF database, which contains CGF information for >25,000 C. jejuni strains from foods, animals, humans, and environmental samples across Canada ( 21 ). Presumably, the level of stress tolerance for clinical strains will be different from that for nonclinical strains because clinical strains have already undergone a range of stress conditions during transmission and infection.…”

“…Strains in CGF subtype 0044.003.001 in MST clade III were highly tolerant to multiple stresses, including PAA, freezing, and heat treatment ( Figure 4 ). The CGF subtype 0044.003.001 was commonly found in human clinical cases in Alberta ( Figure 5 , panel B) and Canada ( 21 ). This CGF subtype is detected more frequently in human clinical cases (59.0%) than in animals (40.5%) in the Canadian Campylobacter CGF database ( 21 ).…”

AB_SA: Accessory genes-Based Source Attribution – tracing the source of Salmonella enterica Typhimurium environmental strains

“…The importance of accessory genes in host adaptation is not limited to S. enterica Typhimurium. Recent studies focused on the pangenome of Campylobacter jejuni showed that hostsegregating genomic factors located in the accessory genomes constitute epidemiological markers for source attribution [12,40].…”

Bacterial Genetic Architecture of Ecological Interactions in Co-culture by GWAS-Taking Escherichia coli and Staphylococcus aureus as an Example

“…The first successful GWAS in humans was published in 2005 and examined 96 patients with age-related macular degeneration, a condition that leads to vision loss in older adults, and 50 age-matched controls ( Klein et al, 2005 ; Read and Massey, 2014 ). GWAS can identify genetic factors underlying important phenotypes, but have rarely been applied to bacteria ( Sheppard et al, 2013 ; Buchanan et al, 2017 ). The application of GWAS to microorganisms is a potentially powerful approach to rapidly identifying genetic factors that mediate heritable phenotypic variation ( Muller et al, 2011 ; Connelly and Akey, 2012 ; Dalman et al, 2013 ).…”