Amplicon sequencing detects, identifies, and quantifies minority variants in mixed-species infections ofparasites.

Abstract

is a globally endemic parasite genus with over 40 recognized species. Whileandare 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 ofin 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; thenamplicons to a species using a custom database. This approach demonstrated sensitivity, successfully detecting and accurately identifying as little as 0.001 ng ofDNA in a complex stool background. Notably, we differentiated mixed infections and demonstrated the ability to identify potentially novel species ofbothand. Using this method, we identifiedin Egyptian rabbits with three samples showing minor mixed infections. By contrast, no mixed infections were detected in Egyptian children, who were primarily infected with. Thus, this pipeline provides a sensitive tool forspecies-level identification, allowing for the detection and accurate identification of minor variants and mixed infections.IMPORTANCEis 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 detectingin clinical and agricultural water samples, there is still a need for a method that can efficiently identify known species as well as infections with multiplespecies, which are increasingly being reported. In this study, we utilized sequencing of a specific region to develop a sensitive and accurate identification workflow forspecies 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 identifyspecies in complex clinical and agricultural samples. This has important implications for clinical diagnostics, biosurveillance, and understanding disease transmission, ultimately benefiting clinicians and produce growers.