Evidence of Cryptosporidium transmission between cattle and humans in northern New South Wales.

Cryptosporidium is an enteric parasite of public health significance that causes diarrhoeal illness through faecal oral contamination and via water. Zoonotic transmission is difficult to determine as most species of Cryptosporidium are morphologically identical and can only be differentiated by molecular means. Transmission dynamics of Cryptosporidium in rural populations were investigated through the collection of 196 faecal samples from diarrheic (scouring) calves on 20 farms and 63 faecal samples from humans on 14 of these farms. The overall prevalence of Cryptosporidium in cattle and humans by PCR and sequence analysis of the 18S rRNA was 73.5% (144/196) and 23.8% (15/63), respectively. Three species were identified in cattle; Cryptosporidium parvum, Cryptosporidium bovis and Cryptosporidium ryanae, and from humans, C. parvum and C. bovis. This is only the second report of C. bovis in humans. Subtype analysis at the gp60 locus identified C. parvum subtype IIaA18G3R1 as the most common subtype in calves. Of the seven human C. parvum isolates successfully subtyped, five were IIaA18G3R1, one was IIdA18G2 and one isolate had a mix of IIaA18G3R1 and IIdA19G2. These findings suggest that zoonotic transmission may have occurred but more studies involving extensive sampling of both calves and farm workers are needed for a better understanding of the sources of Cryptosporidium infections in humans from rural areas of Australia.

Development of an untargeted metabolomics method for the analysis of human faecal samples using Cryptosporidium-infected samples.

Faecal metabolite profiling, though in its infancy, allows for investigation of complex metabolic interactions between gastrointestinal infections or diseases and host health. In the present study, we describe a faecal metabolite extraction method for untargeted gas chromatography-mass spectrometry (GC-MS) analysis using Cryptosporidium positive and negative human faecal samples. The extraction method takes into account the varying faecal consistencies and quantities received for clinical diagnosis. Optimisation was carried out using different extraction solvents and on three different faecal quantities to determine the minimum amount of faecal sample required. The method was validated by untargeted GC-MS analysis on 8 Cryptosporidium positive and 8 Cryptosporidium negative human faecal samples, extracted using the optimised conditions. The method showed good extraction reproducibility with a relative standard deviation of 9.14%. Multivariate analysis of the GC-MS generated dataset showed distinct differences between profiles of Cryptosporidium positive and Cryptosporidium negative samples. The most notable differences included changes in amino acid, nitrogen and energy metabolism, demonstrating the association of infection with Cryptosporidium and altered permeability of the small intestine.