Amplicon sequencing detects, identifies, and quantifies minority variants in mixed-species infections of <i>Cryptosporidium</i> parasites

<i>Cryptosporidium</i> is a globally endemic parasite genus with over 40 recognized species. While <i>C. hominis</i> and <i>C. parvum</i> 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 <i>Cryptosporidium</i> 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 <i>Cryptosporidium</i> amplicons to a species using a custom database. This approach demonstrated sensitivity, successfully detecting and accurately identifying as little as 0.001 ng of <i>C. parvum</i> DNA in a complex stool background. Notably, we differentiated mixed infections and demonstrated the ability to identify potentially novel species of <i>Cryptosporidium</i> both <i>in situ</i> and <i>in vitro</i>. Using this method, we identified <i>Cryptosporidium parvum</i> 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 <i>C. hominis</i>. Thus, this pipeline provides a sensitive tool for <i>Cryptosporidium</i> species-level identification, allowing for the detection and accurate identification of minor variants and mixed infections.IMPORTANCE<i>Cryptosporidium</i> 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 <i>Cryptosporidium</i> 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 <i>Cryptosporidium</i> 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 <i>Cryptosporidium</i> 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 <i>Cryptosporidium</i> 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.