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Cyclosporiasis is an illness characterised by watery diarrhoea caused by the food-borne parasite Cyclospora cayetanensis. The increase in annual US cyclosporiasis cases led public health agencies to develop genotyping tools that aid outbreak investigations. A team at the Centers for Disease Control and Prevention (CDC) developed a system based on deep amplicon sequencing and machine learning, for detecting genetically-related clusters of cyclosporiasis to aid epidemiologic investigations. An evaluation of this system during 2018 supported its robustness, indicating that it possessed sufficient utility to warrant further evaluation. However, the earliest version of CDC's system had some limitations from a bioinformatics standpoint. Namely, reliance on proprietary software, the inability to detect novel haplotypes and absence of a strategy to select an appropriate number of discrete genetic clusters would limit the system's future deployment potential. We recently introduced several improvements that address these limitations and the aim of this study was to reassess the system's performance to ensure that the changes introduced had no observable negative impacts. Comparison of epidemiologically-defined cyclosporiasis clusters from 2019 to analogous genetic clusters detected using CDC's improved system reaffirmed its excellent sensitivity (90%) and specificity (99%), and confirmed its high discriminatory power. This C. cayetanensis genotyping system is robust and with ongoing improvement will form the basis of a US-wide C. cayetanensis genotyping network for clinical specimens.

Cyclosporiasis is a diarrheal illness caused by the foodborne parasite Cyclospora cayetanensis. Annually reported cases have been increasing in the United States prompting development of genotyping tools to aid cluster detection. A recently developed Cyclospora genotyping system based on 8 genetic markers was applied to clinical samples collected during the cyclosporiasis peak period of 2020, facilitating assessment of its epidemiologic utility. While the system performed well and helped inform epidemiologic investigations, inclusion of additional markers to improve cluster detection was supported. Consequently, investigations have commenced to identify additional markers to enhance performance.

Cyclosporiasis is an illness characterised by watery diarrhoea caused by the food-borne parasite Cyclospora cayetanensis. The increase in annual US cyclosporiasis cases led public health agencies to develop genotyping tools that aid outbreak investigations. A team at the Centers for Disease Control and Prevention (CDC) developed a system based on deep amplicon sequencing and machine learning, for detecting genetically-related clusters of cyclosporiasis to aid epidemiologic investigations. An evaluation of this system during 2018 supported its robustness, indicating that it possessed sufficient utility to warrant further evaluation. However, the earliest version of CDC's system had some limitations from a bioinformatics standpoint. Namely, reliance on proprietary software, the inability to detect novel haplotypes and absence of a strategy to select an appropriate number of discrete genetic clusters would limit the system's future deployment potential. We recently introduced several improvements that address these limitations and the aim of this study was to reassess the system's performance to ensure that the changes introduced had no observable negative impacts. Comparison of epidemiologically-defined cyclosporiasis clusters from 2019 to analogous genetic clusters detected using CDC's improved system reaffirmed its excellent sensitivity (90%) and specificity (99%), and confirmed its high discriminatory power. This C. cayetanensis genotyping system is robust and with ongoing improvement will form the basis of a US-wide C. cayetanensis genotyping network for clinical specimens.

Outbreaks of cyclosporiasis, a food-borne illness caused by the coccidian parasite Cyclospora cayetanensis have 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 genotyping Cyclospora has 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 648 C. cayetanensis samples 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.

Cyclospora cayetanensis is an intestinal parasite responsible for the diarrheal illness, cyclosporiasis. Molecular genotyping, using targeted amplicon sequencing, provides a complementary tool for outbreak investigations, especially when epidemiological data are insufficient for linking cases and identifying clusters. The goal of this study was to identify candidate genotyping markers using a novel workflow for detection of segregating single nucleotide polymorphisms (SNPs) in C. cayetanensis genomes. Four whole C. cayetanensis genomes were compared using this workflow and four candidate markers were selected for evaluation of their genotyping utility by PCR and Sanger sequencing. These four markers covered 13 SNPs and resolved parasites from 57 stool specimens, differentiating C. cayetanensis into 19 new unique genotypes.Développement d’un flux de travail pour l’identification de marqueurs de génotypage nucléaire pour Cyclospora cayetanensis.Cyclospora cayetanensis est un parasite intestinal responsable de la cyclosporose, maladie diarrhéique. Le génotypage moléculaire, utilisant le séquençage ciblé des amplicons, fournit un outil complémentaire pour les enquêtes sur les épidémies, en particulier lorsque les données épidémiologiques sont insuffisantes pour relier les cas et identifier les grappes. Le but de cette étude était d’identifier des marqueurs candidats de génotypage à l’aide d’un nouveau flux de travail pour la détection des polymorphismes d’un seul nucléotide (SNP) différentiateurs dans les génomes de C. cayetanensis. Quatre génomes entiers de C. cayetanensis ont été comparés à l’aide de ce flux de travail et quatre marqueurs candidats ont été sélectionnés pour l’évaluation de leur utilité de génotypage par PCR et séquençage Sanger. Ces quatre marqueurs couvraient 13 SNP et ont résolu les parasites provenant de 57 spécimens de selles, différenciant C. cayetanensis en 19 nouveaux génotypes uniques.

Background Protozoan parasites such as Giardia duodenalis, Cryptosporidium spp., Cyclospora cayetanensis, Toxoplasma gondii and Entamoeba histolytica represent a great challenge to the systems producing water for human consumption because their cystic forms are persistent in the environment and resist to the disinfection methods conventionally used for their control. In this study, we investigated the presence of these protozoan pathogens in both raw and treated water samples used for the production of drinking water in Nariño Department, southwest Colombia. We collected 110 water samples (10 lof each sample) and analyzed them with real-time PCR (qPCR). qPCR-positive samples were genotyped with PCR and DNA sequencing. Results Giardia duodenalis was detected in 35/110 (31.8%) of the samples and Cryptosporidium spp. in 9/110 (8.2%) of the samples; no sample was positive for T. gondii, E. histolytica or C. cayetanensis. Giardia duodenalis was detected in samples of both raw water (Drinking Water Treatment Plants (DWTP): 47.83%;Drinking Water Rural Plants (DWRP): 18.42%) and water collected either after conventional physicochemical treatment (26.09%) or after disinfection by chlorine (50%), whereas Cryptosporidium spp. were only detected in raw waters (DWTP: 17.39%; DWRP: 13.16%). The two pathogens were detected in both types of treatment plants supplying water to urban areas and to rural zones. Analysis of gdh and tpi markers identified assemblages AI, AII and H of G. duodenalis, while analysis of the small subunit rRNA and gp60 markers of Cryptosporidium-positive samples identified C. parvum (Subtype IIcA5G3c), C. galli, C. molnari, Cryptosporidium sp. genotype II of bats and Cryptosporidium sp. genotype VIII of birds. Conclusions The results obtained demonstrate the presence of protozoan parasites in the water of the study region, and the need to improve the surveillance systems for these pathogens and identify the corresponding sources of contamination. Electronic supplementary material The online version of this article (10.1186/s13071-018-3147-3) contains supplementary material, which is available to authorized users.