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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.

For complex clinical cases where a parasitic infection is suspected, it can be difficult for clinicians to recommend an appropriate laboratory test. These tests are usually pathogen-specific and require a certain degree of suspicion for the precise etiology. A recently described assay, the universal parasite diagnostic (UPDx) can potentially provide a diagnosis of any parasite present in a specimen. Using primers that amplify DNA from all eukaryotes, UPDx differentiates several parasitic infections in blood by amplicon-based next-generation sequencing (NGS) of the 18S rDNA locus. As the state’s public health reference laboratory, the Parasitology Laboratory at the Wadsworth Center (Albany, NY) receives specimens from patients who have potentially encountered a wide variety of parasites. As such, the ability to differentiate several blood parasites using a single assay is of interest. We assessed UPDx for its ability to confirm parasitic infections for 20 specimens that were previously identified by real-time PCR (RT-PCR). This included specimens positive for Babesia microti, Trypanosoma cruzi, Leishmania tropica, various Plasmodium species, and specimens comprising mixed Plasmodium sp. infections. Results obtained using UPDx were largely concordant with the RT-PCR assays. A T. cruzi positive specimen was negative by UPDx and for two mixed Plasmodium sp. infections only one species was detected. The results obtained for other specimens were concordant. We conclude that UPDx shows promise for the detection of blood parasites in diagnostic laboratories. As NGS becomes cheaper, assays like UPDx will become increasingly amenable to use in clinical settings.

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.

The recent advances in next-generation sequencing technologies provide a new and effective way of tracking malaria drug-resistant parasites. To take advantage of this technology, an end-to-end Illumina targeted amplicon deep sequencing (TADS) and bioinformatics pipeline for molecular surveillance of drug resistance in P. falciparum , called ma laria r esistance s urveillance (MaRS), was developed. TADS relies on PCR enriching genomic regions, specifically target genes of interest, prior to deep sequencing. MaRS enables researchers to simultaneously collect data on allele frequencies of multiple full-length P. falciparum drug resistance genes ( crt , mdr1 , k13 , dhfr , dhps , and the cytochrome b gene), as well as the mitochondrial genome. Information is captured at the individual patient level for both known and potential new single nucleotide polymorphisms associated with drug resistance. The MaRS pipeline was validated using 245 imported malaria cases that were reported to the Centers for Disease Control and Prevention (CDC). The chloroquine resistance crt CV IET genotype (mutations underlined) was observed in 42% of samples, the highly pyrimethamine-resistant dhps IRN triple mutant in 92% of samples, and the sulfadoxine resistance dhps mutation S GE AA in 26% of samples. The mdr1 N F SND genotype was found in 40% of samples. With the exception of two cases imported from Cambodia, no artemisinin resistance k13 alleles were identified, and 99% of patients carried parasites susceptible to atovaquone-proguanil. Our goal is to implement MaRS at the CDC for routine surveillance of imported malaria cases in the United States and to aid in the adoption of this system at participating state public health laboratories, as well as by global partners.