A viability assay for Cyclospora and its surrogates Eimeria

To understand and mitigate human health risk posed by foodborne pathogens, produce growers and regulators require tools capable of assessing not only their presence, but also their viability. Although being present is a concern, actions to mitigate infection are more urgent if the pathogens are viable. The inability to propagate Cyclospora, either in in vitro or in vivo, hinders viability assessment. Many pathogens repel infiltration of certain dyes only when living. Propidium monoazide (PMA) has proven successful as a basis to establish the viability of other pathogens. Droplet digital PCR (ddPCR) proven very useful for absolute quantification of nucleic acids, without requiring standard curves. Hence, we proposed to develop a viability assay for Cyclospora in produce, combining PMA staining with ddPCR. To accomplish this, we will first optimize conditions and establish performance using Eimeria surrogates that infect poultry. We will then evaluate performance with Cyclospora. Success would afford the industry, and regulators, a rapid, sensitive, specific, and robust assay to diagnose parasite contamination and to test the presence of viable protozoan pathogens.

Many foodborne outbreaks derive from the protozoan pathogen Cyclospora cayetanensis. Although over 20 species in the genus Cyclospora are recognized, C. cayetanensis is the only species known to infect humans (1). Neither in vitro nor in vivo methods enable propagation of Cyclospora, severely limiting development of new control strategies. Access to oocysts is very limited, undermining efforts to understand their maturation and biological processes (2). Thus, there is an urgent need for new tools to detect the presence of viable pathogens in fresh produce, and furthermore, new model to study and understand the biology of this emerging pathogen. Fortunately, a wealth of data from natural and experimental Eimeria infections and comparative genome analysis showed that Eimeria and Cyclospora share conserved gene families, life-history traits, and developmental characteristics. Thus, Eimeria can and should be exploited as surrogates for evaluating methods capable of better detection, diagnosis, and outbreak tracing. Here, we propose to exploit this resemblance to speed development of an in vitro viability assay. These efforts will develop a rapid, sensitive, specific, and robust assay to diagnose parasite contamination and to test the presence of viable protozoan pathogens, particularly Cyclospora by using Eimeria as a surrogate. The FDA recently established a molecular detection technique that uses real-time polymerase chain reaction (qPCR) to amplify Cyclospora DNA in produce and clinical samples; this method cannot distinguish dead from live parasites (3). Viability has been assessed for other protozoans by incorporating Propidium Monoazide (PMA) prior to qPCR (4-7). PMA penetrates damaged cell membranes and intercalates in the DNA, inhibiting subsequent PCR amplification. Recently, PMA has been used very successfully in conjunction with droplet digital PCR (ddPCR) to assess and absolutely quantify viable pathogens, without requiring construction of a standard curve each time (8-10). Hence, combining PMA treatment and ddPCR could enable rapid enumeration of viable Cyclospora. Producers, regulators, and public health would benefit.

1. Adapt and validate sensitive biomarkers for risk assessments. 

2. Develop quantitative viability assays for Eimeria and Cyclospora, using a droplet digital polymerase chain reaction (ddPCR) system and propidium monoazide (PMA) treatment.

This study addresses the challenge of reliably assessing Cyclospora viability to better understand and mitigate its health risks. Due to difficulties in propagating Cyclospora, reliable viability assessment tools are needed for growers and regulators. We sought to adapt methods developed for Eimeria acervulina, a close cousin, to assess Cyclospora viability. In Eimeria, dead oocysts were identified by their granular autofluorescence under ultraviolet light, a feature not seen in viable oocysts. This autofluorescence enabled sorting of live and dead oocysts using fluorescence-activated cell sorting. In vivo testing confirmed that only viable oocysts were infectious, while dead oocysts did not lead to shedding. A deep learning-based approach using high-resolution microscopic images was also employed to distinguish live and dead Eimeria oocysts, utilizing a convolutional neural network based on YOLOv7 architecture. The model achieved 99.1% precision in identifying oocyst viability, with over 95% accuracy in cross-species testing, demonstrating its applicability to other Eimeria species. Additionally, RNA sequencing of stored E. acervulina oocysts revealed age-related gene expression changes, particularly in heat shock proteins and ribosomal subunits. These findings provide insights into the biological processes underlying parasite senescence. By combining deep learning and transcriptomic data, this study presents a novel, costeffective approach to assessing oocyst viability, enhancing the accuracy of risk assessments. This methodology offers regulatory authorities and growers a reliable tool for making informed decisions based on true parasite viability rather than detecting trace amounts of DNA, which may not pose an immediate threat. The approach holds promise for improving public health safety and parasite management.