Abstract
Background: Enzymatic activity of Telomerase Reverse Transcriptase (TERT) is important in maintaining the telomere length and has been implicated in cancer and aging related pathology. Since cancer susceptibility as well as longevity of dogs vary between breeds, this study involved sequencing the entire TERT gene of Canis familiaris from DNA samples obtained from forty dogs, with ten dogs each of four breeds: Shih Tzu, Dachshund, Irish Wolfhound, and Newfoundland, each with different life expectancies and susceptibility to cancer.Results: We compared the sequences of all forty individuals amongst one another and with the published sequence of canine TERT, and analyzed relationships between members of the same or different breeds. Two separate phylogenetic trees were generated and analyzed from these individuals. Polymorphisms were found most frequently in intronic regions of the gene, although exonic polymorphisms also were observed. In many locations genotypes were observed that were either homozygous for the reference sequence or heterozygous, but the variant homozygous genotype was not observed.Conclusions: We propose that these homozygous variants are likely to have adverse effects in dogs. It was also found that the polymorphisms did not segregate by breed. Because the four breeds chosen come from geographically and physiologically distinct backgrounds, it can be inferred that the polymorphic diversification of TERT preceded breed derivation.
| Original language | English (US) |
|---|---|
| Article number | 20 |
| Journal | BMC Veterinary Research |
| Volume | 10 |
| DOIs | |
| State | Published - Jan 14 2014 |
Bibliographical note
Funding Information:We wish to thank the Minnesota Supercomputing Institute for computational infrastructure and support, the Animal Cancer Care and Research Program and Masonic Cancer Center for providing funding, Elaine Ostrander and Heidi Parker at the National Human Genome Research Institute of the NIH for providing samples, Curt’s Media and Mary “Andy” Scott for graphics assistance, Matthew Beckman, PhD for advise and constructive discussions, Milcah Scott for technical support, and Evan Tang for scripting and data organization. CAM was the recipient of an Augsburg College URGO program grant.
