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      The African buffalo parasite Theileria. sp. (buffalo) can infect and immortalize cattle leukocytes and encodes divergent orthologues of  Theileria parva antigen genes

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          Abstract

          African Cape buffalo ( Syncerus caffer) is the wildlife reservoir of multiple species within the apicomplexan protozoan genus Theileria, including Theileria parva which causes East coast fever in cattle. A parasite, which has not yet been formally named, known as Theileria sp. (buffalo) has been recognized as a potentially distinct species based on rDNA sequence, since 1993. We demonstrate using reverse line blot (RLB) and sequencing of 18S rDNA genes, that in an area where buffalo and cattle co-graze and there is a heavy tick challenge, T. sp. (buffalo) can frequently be isolated in culture from cattle leukocytes. We also show that T. sp. (buffalo), which is genetically very closely related to T. parva, according to 18s rDNA sequence, has a conserved orthologue of the polymorphic immunodominant molecule (PIM) that forms the basis of the diagnostic ELISA used for T. parva serological detection. Closely related orthologues of several CD8 T cell target antigen genes are also shared with T. parva. By contrast, orthologues of the T. parva p104 and the p67 sporozoite surface antigens could not be amplified by PCR from T. sp. (buffalo), using conserved primers designed from the corresponding T. parva sequences. Collectively the data re-emphasise doubts regarding the value of rDNA sequence data alone for defining apicomplexan species in the absence of additional data. ‘Deep 454 pyrosequencing’ of DNA from two Theileria sporozoite stabilates prepared from Rhipicephalus appendiculatus ticks fed on buffalo failed to detect T. sp. (buffalo). This strongly suggests that R. appendiculatus may not be a vector for T. sp. (buffalo ). Collectively, the data provides further evidence that T. sp. (buffalo). is a distinct species from T. parva.

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          Highlights

          • Theileria sp. ( buffalo) can infect and immortalize cattle leukocytes.
          • Antigen genes of T. sp. ( buffalo) vary in level of identity to those of T. parva
          • The tick that transmits T. sp. (buffalo) to cattle is not Rhipicephalus appendiculatus
          • 18s rDNA sequence information alone is insufficient to define species of Theileria

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          Most cited references 51

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

           Robert Edgar (2004)
          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

            Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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              • Record: found
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              RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

              Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Int J Parasitol Parasites Wildl
                Int J Parasitol Parasites Wildl
                International Journal for Parasitology: Parasites and Wildlife
                Elsevier
                2213-2244
                29 August 2015
                December 2015
                29 August 2015
                : 4
                : 3
                : 333-342
                Affiliations
                [a ]International Livestock Research Institute (ILRI), PO Box 30709, Nairobi, 00100, Kenya
                [b ]The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, UK
                [c ]School of Biological Sciences, The University of Nairobi, PO Box 30197, Nairobi, 00100, Kenya
                [d ]College of Medical Veterinary and Life Sciences, University of Glasgow Glasgow, G61 1QH, UK
                Author notes
                []Corresponding author. r.bishop@ 123456cgiar.org
                [1]

                These two authors made equal contributions to this work.

                [2]

                Current address: The Pirbright Institute, Pirbright, Woking, GU24 0NF, United Kingdom.

                Article
                S2213-2244(15)30012-2
                10.1016/j.ijppaw.2015.08.006
                4589832
                © 2015 Published by Elsevier Ltd on behalf of Australian Society for Parasitology.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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