This article is part of the supplement: Second International Symposium on Candidate Genes for Animal Health. 16-18 August 2002, Montpelier - France
Research
Serial analysis of gene expression (SAGE) in bovine trypanotolerance: preliminary results
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* Corresponding author: Jean-Charles Maillard maillard@cirad.fr
1 Cirad, Département d'Élevage et de Médecine Vétérinaire Tropicale, Animal Health Program, Montpellier, France
2 Skuld Tech Cie, Montpellier, France
3 Cirdes, Unité de Recherches Biologiques, Bobo-Dioulasso, Burkina Faso
4 Cnrs, Institut de Génétique Humaine, Université Montpellier II, France
Genetics Selection Evolution 2003, 35(Suppl 1):S35-S47 doi:10.1186/1297-9686-35-S1-S35
Published: 15 June 2003Abstract
In Africa, trypanosomosis is a tsetse-transmitted disease which represents the most important constraint to livestock production. Several indigenous West African taurine (Bos taurus) breeds, such as the Longhorn (N'Dama) cattle are well known to control trypanosome infections. This genetic ability named "trypanotolerance" results from various biological mechanisms under multigenic control. The methodologies used so far have not succeeded in identifying the complete pool of genes involved in trypanotolerance. New post genomic biotechnologies such as transcriptome analyses are efficient in characterising the pool of genes involved in the expression of specific biological functions. We used the serial analysis of gene expression (SAGE) technique to construct, from Peripheral Blood Mononuclear Cells of an N'Dama cow, 2 total mRNA transcript libraries, at day 0 of a Trypanosoma congolense experimental infection and at day 10 post-infection, corresponding to the peak of parasitaemia. Bioinformatic comparisons in the bovine genomic databases allowed the identification of 187 up- and down- regulated genes, EST and unknown functional genes. Identification of the genes involved in trypanotolerance will allow to set up specific microarray sets for further metabolic and pharmacological studies and to design field marker-assisted selection by introgression programmes.