Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Source attribution of campylobacteriosis in Australia, 2017-2019.(John Wiley and Sons, Inc., 2023-12-01) McLure A; Smith JJ; Firestone SM; Kirk MD; French N; Fearnley E; Wallace R; Valcanis M; Bulach D; Moffatt CRM; Selvey LA; Jennison A; Cribb DM; Glass KCampylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe-Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%-12%) and 2% to pigs (95% CrI: 0.2%-11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%-33%) and 6% to pigs (95% CrI: 1.1%-19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017-2019 and should remain the focus of interventions to reduce burden.Item Whole-genome sequencing and ad hoc shared genome analysis of Staphylococcus aureus isolates from a New Zealand primary school(Springer Nature Limited, 2021-10-13) Scott P; Zhang J; Anderson T; Priest PC; Chambers S; Smith H; Murdoch DR; French N; Biggs PJEpidemiological studies of communicable diseases increasingly use large whole-genome sequencing (WGS) datasets to explore the transmission of pathogens. It is important to obtain an initial overview of datasets and identify closely related isolates, but this can be challenging with large numbers of isolates and imperfect sequencing. We used an ad hoc whole-genome multi locus sequence typing method to summarise data from a longitudinal study of Staphylococcus aureus in a primary school in New Zealand. Each pair of isolates was compared and the number of genes where alleles differed between isolates was tallied to produce a matrix of "allelic differences". We plotted histograms of the number of allelic differences between isolates for: all isolate pairs; pairs of isolates from different individuals; and pairs of isolates from the same individual. 340 sequenced isolates were included, and the ad hoc shared genome contained 445 genes. There were between 0 and 420 allelic differences between isolate pairs and the majority of pairs had more than 260 allelic differences. We found many genetically closely related S. aureus isolates from single individuals and a smaller number of closely-related isolates from separate individuals. Multiple S. aureus isolates from the same individual were usually very closely related or identical over the ad hoc shared genome. Siblings carried genetically similar, but not identical isolates. An ad hoc shared genome approach to WGS analysis can accommodate imperfect sequencing of the included isolates, and can provide insights into relationships between isolates in epidemiological studies with large WGS datasets containing diverse isolates.
