Detection and Molecular Characterisation of Cryptosporidium inNeonate Livestock in Northern Ireland.
Heather P. Thompson1*, Lihua Xiao2, Maurice McCoy3, John Kenny3, Colm J. Lowery1, James S.G. Dooley1 and John E. Moore4.
School of Biological and Environmental Science, University of Ulster, Coleraine, BT52 1SA1; Division of Parasitic Diseases, Centers for Disease Control, Atlanta, Georgia 30341; Department of Agriculture and Rural Development for Northern Ireland (Veterinary Science Division), Stoney Road, Belfast, BT4 3SD3: Belfast City Hospital, Lisburn Road, Belfast BT9 7AB4.
*Corresponding Author: Heather P. Thompson, School of Biological and Environmental Science, University of Ulster, Coleraine, BT52 1SA Tel: +44 (0)28 70 323132. Fax: +44 (0)28 70 324911, E-mail: HP.Thompson@ulster.ac.uk
Enteritis is responsible for a high proportion of mortalities in neonatal farm stock in Northern Ireland. The most frequently identified associated pathogens are Cryptosporidia,viral pathogens, Salmonella spp. and E coli K99. Using staining and immunological methods, targeted examination of neonatal enteritis in bovine animals less than four months of age for Cryptosporidia has previously shown incidence levels of 35-40%. However these methods have not allowed detailed analysis of the Cryptosporidium species or genotype.
Since February 2002, faecal and biopsy samples have been analysed by molecular methods. Following DNA extraction, each sample was analysed by a small-subunit rRNA-based PCR-restriction fragment length polymorphism technique to both speciate and genotype the isolate. Of 136 positive samples, 134 (98.5%) of isolates were classified as Cryptosporidium parvum genotype 2, and two isolates (1.5%) were classified as C. baileyi. Sequencing of the 18S rRNA nested PCR product confirmed this data and allowed initial phylogenetic analysis. Further intra-genotypic phylogenetic analysis of the genotype 2 isolates was carried out using a nested PCR targeting the highly polymorphic gp60 surface glycoprotein gene. Phylogenetic trees for both targets were constructed by neighbour-joining and CLUSTALW analysis. Trees were "rooted" by comparison of the Northern Ireland isolates with the relevant sequence data from Cryptosporidium outgroups.
These molecular techniques were further used to test samples which had been categorised as "queried" or "Eimerian positive" by microscopic methods. Cryptosporidium was identified as being present in four out of 23 (17.5%) of these samples. PCR and sequencing work also allowed these isolates to be definitively classified as three C. parvum genotype 2 isolates and a C. baileyi isolate.
This work shows the potential for molecular methods to contribute to the accurate and timely diagnosis of Cryptosporidium in livestock, with the further benefit of providing epidemiological data for both comparative phylogeny studies and disease control.
