Authors

A Morris¹; M Swain²; J Pachebat²; R M Chalmers⁴; G Robinson³;  ¹ Aberystwyth University, UK;  ² Aberystwyth University - IBERS, UK;  ³ Cryptosporidium Reference Unit, Public Health Wales, UK;  ⁴ Cryptosporidium Reference Unit, Swansea, UK

Discussion

In Wales there are ~300 reported cases of cryptosporidiosis per year, outbreaks of which can have significant health and economic impacts. The cause of cryptosporidiosis is the apicomplexan Cryptosporidium, a protozoan parasite with a complex life cycle. In the UK most cases of Cryptosporidiosis are caused by C. parvum or C. hominis. While self-limiting after prolonged duration of symptoms (2-3 weeks) in immunocompetent hosts, severely immunocompromised patients suffer severe, sometimes life threatening disease. Although all ages can be affected, cryptosporidiosis is most common in young children. In the developing world Cryptosporidium is one of the main causes of childhood morbidity. A recent large-scale study has evaluated the aetiology, burden and clinical syndromes of moderate-to severe diarrhoea in >20,000 children across seven sites in sub-Saharan Africa and South Asia and identified Cryptosporidium as one of the four highest contributors to diarrheal diseases worldwide in children <5 years of age. Because of this, there is a growing need to develop novel prevention strategies in combating the spread of this parasite.

 Currently, Cryptosporidium sp. subtyping is carried out via interrogation of a highly repetitive (Variable Number Tandem Repeat) region within the gp60 surface glycoprotein gene. This has furnished epidemiological studies with higher resolution data with which to investigate the transmission of Cryptosporidium throughout a population. However, recombination events at the gp60 locus have been demonstrated, due to the sexual nature of the Cryptosporidium life cycle. This proves to be a potential confounding issue in Cryptosporidium subtyping, and therefore epidemiological investigation. Here we present a bioinformatics tool developed to allow for the in silico automation of novel VNTR biomarker identification, with the intention of developing a more robust single or multi locus VNTR subtyping paradigm. Using this tool, 370 VNTR’s were identified within coding regions in a dataset of 8 C.parvum genomes, of which 97 were seen to be polymorphic, and therefore suitably discriminatory. This data is useful for developing novel prevention strategies to be employed in the battle against cryptopsoridiosis.