Abstract
Cryptosporidium is a globally endemic parasite genus with over 40 recognized species. While C. hominis and C. parvum are responsible for most human infections, human cases involving other species have also been reported. Furthermore, there is increasing evidence of simultaneous infections with multiple species. Therefore, we devised a new means to identify various species of Cryptosporidium in mixed infections by sequencing a 431 bp amplicon of the 18S rRNA gene encompassing two variable regions. Using the DADA2 pipeline, amplicons were first identified to a genus using the SILVA 132 reference database; then Cryptosporidium amplicons to a species using a custom database. This approach demonstrated sensitivity, successfully detecting and accurately identifying as little as 0.001 ng of C. parvum DNA in a complex stool background. Notably, we differentiated mixed infections and demonstrated the ability to identify potentially novel species of Cryptosporidium both in situ and in vitro. Using this method, we identified Cryptosporidium parvum in Egyptian rabbits with three samples showing minor mixed infections. By contrast, no mixed infections were detected in Egyptian children, who were primarily infected with C. hominis. Thus, this pipeline provides a sensitive tool for Cryptosporidium species-level identification, allowing for the detection and accurate identification of minor variants and mixed infections.IMPORTANCECryptosporidium is a eukaryotic parasite and a leading global cause of waterborne diarrhea, with over 40 recognized species infecting livestock, wildlife, and people. While we have effective tools for detecting Cryptosporidium in clinical and agricultural water samples, there is still a need for a method that can efficiently identify known species as well as infections with multiple Cryptosporidium species, which are increasingly being reported. In this study, we utilized sequencing of a specific region to develop a sensitive and accurate identification workflow for Cryptosporidium species based on high-throughput sequencing. This method can distinguish between all 40 recognized species and accurately detect mixed infections. Our approach provides a sensitive and reliable means to identify Cryptosporidium species in complex clinical and agricultural samples. This has important implications for clinical diagnostics, biosurveillance, and understanding disease transmission, ultimately benefiting clinicians and produce growers.