Development and evaluation of a molecular based protocol for detection and quantification of Cryptosporidium spp. in wastewater.

Infections caused by protozoan parasites are a major public health concern globally. These infections are commonly diagnosed during water-borne outbreaks, necessitating accurate and highly sensitive detection procedures to assure public health protection. Current molecular techniques are challenged by several factors, such as low parasite concentration, inefficient DNA extraction methods, and inhibitors in environmental samples. This study focused on the development and validation of a molecular protocol for DNA extraction, efficient protozoan (oo)cyst recovery and quantification of protozoan parasites from wastewater using droplet digital polymerase chain reaction (ddPCR). Five DNA extraction methods, including commercial kits, custom phenol-chloroform, and in-house modified methods, were evaluated. The efficiency of each method was assessed via spectrophotometric analysis and ddPCR amplification using specific primers. Lastly, the developed protocol was evaluated for the detection and quantification of Cryptosporidium parvum in wastewater from different regions in South Africa. The conventional phenol-chloroform extraction method yielded the highest DNA concentration of 223 (±0.71) ng/µl and detected the highest number of Cryptosporidium parvum (1807 (±0.30) copies/ddPCR reaction) compared to other methods evaluated in this study. Additionally, the phenol-chloroform method demonstrated high sensitivity in extracting DNA from as few as one cyst/L of Cryptosporidium parvum, corresponding to 5.93 copies/ddPCR reaction. It was also observed that analysis of both the filtered supernatant and pellets after centrifugation improves the recovery efficiency of oocysts from wastewater by 10.5%, resulting in a total recovery of 64.1%. This optimized protocol was successfully applied to measure protozoan concentration in wastewater from different regions in South Africa. The improved DNA extraction and quantification method proposed in this study would be effective in monitoring protozoan concentration in the environment, which will help in instituting mitigation measures to reduce water-borne infections.