Optimized Molecular Detection of Cryptosporidium Within the Water-Soil-Plant-Food Nexus: Advancing Surveillance in Agricultural Systems

Cryptosporidium, a protozoan parasite causing severe diarrheal illness in humans and animals, poses detection challenges due to low parasite concentrations, inhibitors, and inefficient DNA extraction. This study optimized DNA extraction and detection of Cryptosporidium in environmental samples and evaluated their practical use in agriculture. After evaluating 11 DNA extraction methods from spiked phosphate-buffered saline (PBS) samples, three methods for molecular detection of Cryptosporidium in water, soil, and fresh produce were selected and further tested using real-time PCR. A total of 188 artificially contaminated samples were prepared, consisting of distilled water (n = 36), environmental water (n = 44), soil (n = 36), and fresh produce (lettuce and spinach; n = 72). Each sample was inoculated with serial dilutions of 12,500 to 5 Cryptosporidium oocysts and tested using real-time PCR and droplet digital PCR (ddPCR) to evaluate detection sensitivity. Results demonstrated that extraction performance varied by matrix, with two spin-column kits excelling for water and another for soil and produce. DNA from as few as five oocysts was occasionally detectable, with ddPCR being less prone to be affected by PCR inhibitors than real-time PCR. These methods were then applied to detect Cryptosporidium in 210 environmental samples (water, soil, produce) from South African small-scale farms. None of the samples tested positive with real-time PCR, while ddPCR detected Cryptosporidium in 13.6% of water, 23.3% of soil, and 34.7% of fresh produce samples. Surface water showed the highest contamination at 28.6%. Soil amended with both fertilizer and manure had a 45% contamination rate. Among vegetables, roots were most affected (46.7%), followed by fruiting (40%) and leafy greens (30.15%). These findings highlight the health risks of Cryptosporidium in food systems and the need for improved detection methods to enhance surveillance and inform future outbreak prevention strategies.