| Literature DB >> 26396074 |
Eze J Ideozu1, Andrew M Whiteoak2, Alexandra J Tomlinson3, Andrew Robertson4,5, Richard J Delahay6, Geoff Hide7,8.
Abstract
BACKGROUND: Wildlife can be important sources and reservoirs for pathogens. Trypanosome infections are common in many mammalian species, and are pathogenic in some. Molecular detection tools were used to measure trypanosome prevalence in a well-studied population of wild European badgers (Meles meles).Entities:
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Year: 2015 PMID: 26396074 PMCID: PMC4580359 DOI: 10.1186/s13071-015-1088-7
Source DB: PubMed Journal: Parasit Vectors ISSN: 1756-3305 Impact factor: 3.876
Social group of badgers and infection with trypanosomes
| Social group at time of capture | Badger population frequency | Trypanosome infection status | Total | |||
|---|---|---|---|---|---|---|
| Frequency | Percent | Not infected | Infected | |||
| Arthurs | 2 | 2.4 | 2 | 0 | 2 | |
| Beech | 7 | 8.5 | 4 | 3 | 7 | |
| Breakheart | 1 | 1.2 | 1 | 0 | 1 | |
| Boxwood | 1 | 1.2 | 1 | 0 | 1 | |
| Cedar | 5 | 6.1 | 2 | 3 | 5 | |
| Colepark | 1 | 1.2 | 1 | 0 | 1 | |
| Colliers wood | 1 | 1.2 | 1 | 0 | 1 | |
| Honeywell | 11 | 13.4 | 8 | 3 | 11 | |
| Inchbrook | 5 | 6.1 | 4 | 1 | 5 | |
| Jacks | 4 | 4.9 | 2 | 2 | 4 | |
| Junction | 4 | 4.9 | 2 | 2 | 4 | |
| Kennel | 3 | 3.7 | 1 | 2 | 3 | |
| Larch | 6 | 7.3 | 5 | 1 | 6 | |
| Mead | 1 | 1.2 | 0 | 1 | 1 | |
| Old oak | 4 | 4.9 | 2 | 2 | 4 | |
| Parkmill | 5 | 6.1 | 2 | 3 | 5 | |
| Scotland bank | 2 | 2.4 | 0 | 2 | 2 | |
| West | 5 | 6.1 | 5 | 0 | 5 | |
| Windsor edge | 5 | 6.1 | 3 | 2 | 5 | |
| Woodfarm | 1 | 1.2 | 1 | 0 | 1 | |
| Woodrush | 1 | 1.2 | 0 | 1 | 1 | |
| Wychelm | 2 | 2.4 | 2 | 0 | 2 | |
| Yew | 2 | 2.4 | 2 | 0 | 2 | |
| Septic | 1 | 1.2 | 1 | 0 | 1 | |
| Top | 1 | 1.2 | 1 | 0 | 1 | |
| Nettle | 1 | 1.2 | 0 | 1 | 1 | |
| Total | 26 | 82 | 100 | 53 | 29 | 82 |
Fig. 1Phylogenetic tree of concatenated SSU-rRNA and LSU-rRNA sequences of badger trypanosomes and comparator species. The phylogenetic analysis was implemented using the Maximum Likelihood method based on the Kimura 2-parameter model. The tree with the highest log likelihood (−4361) is shown. Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with the superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.1323)). The analysis involved 26 nucleotide sequences, 11 of which, used the concatenated datasets (T. grosi, T. otospermophili, T. kuseli, T. rangeli, T.pestanai, T. rotatorium, T. simiae, T. congolense (riverine forest), T. congolense (kilifi), T. congolense (savannah), Trypanoplasma borreli and badger trypanosome). There were a total of 1560 positions in the final dataset. The numbers after species name on branch are the GenInfo Identifier number (GI) while annotated colours indicate different groups of kinetoplastids. Evolutionary analyses were conducted in MEGA6. Herpetosoma Schizotrypanum Megatrypanum Fish trypanosomes Amphibian trypanosomes Salivarian trypanosomes Bodonid