Title |
Use of a bioinformatic-assisted primer design strategy to establish a new nested PCR-based method for Cryptosporidium
|
---|---|
Published in |
Parasites & Vectors, October 2017
|
DOI | 10.1186/s13071-017-2462-4 |
Pubmed ID | |
Authors |
Anson V. Koehler, Pasi K. Korhonen, Ross S. Hall, Neil D. Young, Tao Wang, Shane R. Haydon, Robin B. Gasser |
Abstract |
The accurate tracking of Cryptosporidium in faecal, water and/or soil samples in water catchment areas is central to developing strategies to manage the potential risk of cryptosporidiosis transmission to humans. Various PCR assays are used for this purpose. Although some assays achieve specific amplification from Cryptosporidium DNA in animal faecal samples, some do not. Indeed, we have observed non-specificity of some oligonucleotide primers in the small subunit of nuclear ribosomal RNA gene (SSU), which has presented an obstacle to the identification and classification of Cryptosporidium species and genotypes (taxa) from faecal samples. Using a novel bioinformatic approach, we explored all available Cryptosporidium genome sequences for new and diagnostically-informative, multi-copy regions to specifically design oligonucleotide primers in the large subunit of nuclear ribosomal RNA gene (LSU) as a basis for an effective nested PCR-based sequencing method for the identification and/or classification of Cryptosporidium taxa. This newly established PCR, which has high analytical specificity and sensitivity, is now in routine use in our laboratory, together with other assays developed by various colleagues. Although the present bioinformatic workflow used here was for the specific design of primers in nuclear DNA of Cryptosporidium, this approach should be broadly applicable to many other microorganisms. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 48 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 6 | 13% |
Student > Bachelor | 6 | 13% |
Student > Ph. D. Student | 5 | 10% |
Researcher | 4 | 8% |
Professor > Associate Professor | 4 | 8% |
Other | 9 | 19% |
Unknown | 14 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 10 | 21% |
Immunology and Microbiology | 6 | 13% |
Agricultural and Biological Sciences | 5 | 10% |
Medicine and Dentistry | 3 | 6% |
Computer Science | 2 | 4% |
Other | 7 | 15% |
Unknown | 15 | 31% |