Strongyloidiasis is a neglected tropical disease caused by the human infective nematodes Strongyloides stercoralis, Strongyloides fuelleborni fuelleborni and Strongyloides fuelleborni kellyi. Previous large-scale studies exploring the genetic diversity of this important genus have focused on Southeast Asia, with a small number of isolates from the USA, Switzerland, Australia and several African countries having been genotyped. Consequently, little is known about the global distribution of geographic sub-variants of these nematodes and the genetic diversity that exists within the genus Strongyloides generally. We extracted DNA from human, dog and primate feces containing Strongyloides, collected from several countries representing all inhabited continents. Using a genotyping assay adapted for deep amplicon sequencing on the Illumina MiSeq platform, we sequenced the hyper-variable I and hyper-variable IV regions of the Strongyloides 18S rRNA gene and a fragment of the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene from these specimens. We report several novel findings including unique S. stercoralis and S. fuelleborni genotypes, and the first identifications of a previously unknown S. fuelleborni infecting humans within Australia. We expand on an existing Strongyloides genotyping scheme to accommodate S. fuelleborni and these novel genotypes. In doing so, we compare our data to all 18S and cox1 sequences of S. fuelleborni and S. stercoralis available in GenBank (to our knowledge), that overlap with the sequences generated using our approach. As this analysis represents more than 1,000 sequences collected from diverse hosts and locations, representing all inhabited continents, it allows a truly global understanding of the population genetic structure of the Strongyloides species infecting humans, non-human primates, and domestic dogs.

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. 8Joel Barratt et al.