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Gene expression profiles in testis of pigs with extreme high and low levels of androstenone

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      Abstract

      Background:Boar taint is a major obstacle when using uncastrated male pigs for swine production. One of the main compounds causing this taint is androstenone, a pheromone produced in porcine testis. Here we use microarrays to study the expression of thousands of genes simultaneously in testis of high and low androstenone boars. The study allows identification of genes and pathways associated with elevated androstenone levels, which is essential for recognising potential molecular markers for breeding purposes.Results:Testicular tissue was collected from 60 boars, 30 with extreme high and 30 with extreme low levels of androstenone, from each of the two breeds Duroc and Norwegian Landrace. The samples were hybridised to porcine arrays containing 26,877 cDNA clones, detecting 563 and 160 genes that were differentially expressed (p < 0.01) in Duroc and Norwegian Landrace, respectively. Of these significantly up- and down-regulated clones, 72 were found to be common for the two breeds, suggesting the possibility of both general and breed specific mechanisms in regulation of, or response to androstenone levels in boars. Ten genes were chosen for verification of expression patterns by quantitative real competitive PCR and real-time PCR. As expected, our results point towards steroid hormone metabolism and biosynthesis as important biological processes for the androstenone levels, but other potential pathways were identified as well. Among these were oxidoreductase activity, ferric iron binding, iron ion binding and electron transport activities. Genes belonging to the cytochrome P450 and hydroxysteroid dehydrogenase families were highly up-regulated, in addition to several genes encoding different families of conjugation enzymes. Furthermore, a number of genes encoding transcription factors were found both up- and down-regulated. The high number of clones belonging to ferric iron and iron ion binding suggests an importance of these genes, and the association between these pathways and androstenone levels is not previously described.Conclusion:This study contributes to the understanding of the complex genetic system controlling and responding to androstenone levels in pig testis. The identification of new pathways and genes involved in the biosynthesis and metabolism of androstenone is an important first step towards finding molecular markers to reduce boar taint.

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        Functional analyses based on the association of Gene Ontology (GO) terms to genes in a selected gene list are useful bioinformatic tools and the GOstats package has been widely used to perform such computations. In this paper we report significant improvements and extensions such as support for conditional testing. We discuss the capabilities of GOstats, a Bioconductor package written in R, that allows users to test GO terms for over or under-representation using either a classical hypergeometric test or a conditional hypergeometric that uses the relationships among GO terms to decorrelate the results. GOstats is available as an R package from the Bioconductor project: http://bioconductor.org
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          Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a well-tested general purpose normalization method which has given good results on a wide range of arrays. The method may be refined by using quality weights for individual spots. The method is best combined with diagnostic plots of the data which display the spatial and intensity trends. When diagnostic plots show that biases still remain in the data after normalization, further normalization steps such as plate-order normalization or scale-normalization between the arrays may be undertaken. Composite normalization may be used when control spots are available which are known to be not differentially expressed. Variations on loess normalization include global loess normalization and two-dimensional normalization. Detailed commands are given to implement the normalization techniques using freely available software.
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            Author and article information

            Affiliations
            [1 ]The Norwegian Pig Breeders Association (NORSVIN), Hamar, Norway.
            [2 ]Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway.
            [3 ]Centre for Integrative Genetics (CIGENE), Norwegian University of Life Sciences, Ås, Norway.
            [4 ]Faculty of Agricultural Sciences, University of Aarhus, Tjele, Denmark.
            [5 ]MATFORSK, Osloveien 1, Ås, Norway.
            Contributors
            Journal
            BMC Genomics
            BMC Genomics
            BioMed Central
            1471-2164
            2007
            7 November 2007
            : 8
            : 405
            2204014
            1471-2164-8-405
            17988377
            10.1186/1471-2164-8-405
            Copyright © 2007 Moe et al; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Research Article

            Genetics

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