Cryptosporidium is a protozoan parasite infecting the intestines of its hosts, leading to acute diarrheal disease. Out of 26 recognized species, 14 are known to infect humans. Of most importance, from a human perspective are Cryptosporidium parvum and Cryptosporidium hominis, of which the former is known to have zoonotic potential. Globally, cryptosporidiosis affect people with lowered immune status particularly hard; among children under five it is the most important parasitic cause of gastroenteritis. In the region of KwaZulu-Natal, on the east coast of South Africa, Cryptosporidium is considered endemic. Drinking water is frequently collected from river systems and as Cryptosporidium spp. can be transmitted via contaminated water, this may be one source of infection. Research on the species distribution is important for outbreak investigations and prevention efforts. In water and wastewater such speciation is commonly performed using immunomagnetic separation, an antibody dependent method. There is however a suspicion that these antibodies have less affinity to some species and hence contorts the detected species distribution. An alternative approach is therefore of interest. In the present study, Cryptosporidium diversity in wastewater collected from four different wastewater treatment plants in KwaZulu-Natal, is evaluated with an optimized antibody-free workflow and a single cell platform. It was shown that the workflow is suitable for complex samples, such as wastewater. Furthermore, diversity was assessed with amplicon sequencing, revealing four different species and genotypes. Further modifications of the methods used could benefit the field of Cryptosporidium research, along with improving global health and preventing disease outbreaks.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-338855 |
| Date | January 2017 |
| Creators | de Jong, Anton |
| Publisher | Uppsala universitet, Institutionen för medicinsk biokemi och mikrobiologi, Statens veterinärmedicinska anstalt |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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