Journal
EPIDEMIOLOGY AND INFECTION
Volume 148, Issue -, Pages -Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0950268820001697
Keywords
Cyclospora cayetanensis; clustering; cyclosporiasis; deep sequencing; distance-statistic; genotype; genotyping; machine learning; MLST
Funding
- Centers for Disease Control and Prevention Office of Advanced Molecular Detection
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Outbreaks of cyclosporiasis, a food-borne illness caused by the coccidian parasiteCyclospora cayetanensishave increased in the USA in recent years, with approximately 2300 laboratory-confirmed cases reported in 2018. Genotyping tools are needed to inform epidemiological investigations, yet genotypingCyclosporahas proven challenging due to its sexual reproductive cycle which produces complex infections characterized by high genetic heterogeneity. We used targeted amplicon deep sequencing and a recently described ensemble-based distance statistic that accommodates heterogeneous (mixed) genotypes and specimens with partial genotyping data, to genotype and cluster 648C. cayetanensissamples submitted to CDC in 2018. The performance of the ensemble was assessed by comparing ensemble-identified genetic clusters to analogous clusters identified independently based on common food exposures. Using these epidemiologic clusters as a gold standard, the ensemble facilitated genetic clustering with 93.8% sensitivity and 99.7% specificity. Hence, we anticipate that this procedure will greatly complement epidemiologic investigations of cyclosporiasis.
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