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.
Background: The recent development within high-throughput technologies for expression profiling has allowed for parallel analysis of transcriptomes and proteomes in biological systems such as comparative analysis of transcript and protein levels of tissue regulated genes. Until now, such studies of have only included microarray or short length sequence tags for transcript profiling. Furthermore, most comparisons of transcript and protein levels have been based on absolute expression values from within the same tissue and not relative expression values based on tissue ratios.
BackgroundGene expression microarrays have been intensively applied to screen for genes involved in specific biological processes of interest such as diseases or responses to environmental stimuli. For mammalian species, cataloging of the global gene expression profiles in large tissue collections under normal conditions have been focusing on human and mouse genomes but is lacking for the pig genome.Methodology/Principal FindingsHere we present the results from a large-scale porcine study establishing microarray cDNA expression profiles of approximately 20.000 genes across 23 healthy tissues. As expected, a large portion of the genes show tissue specific expression in agreement with mappings to gene descriptions, Gene Ontology terms and KEGG pathways. Two-way hierarchical clustering identified expected tissue clusters in accordance with tissue type and a number of cDNA clusters having similar gene expression patterns across tissues. For one of these cDNA clusters, we demonstrate that possible tissue associated gene function can be inferred for previously uncharacterized genes based on their shared expression patterns with functionally annotated genes. We show that gene expression in common porcine tissues is similar to the expression in homologous tissues of human.Conclusions/SignificanceThe results from this study constitute a valuable and publicly available resource of basic gene expression profiles in normal porcine tissues and will contribute to the identification and functional annotation of porcine genes.
BackgroundBoar taint is the undesirable smell and taste of pork meat derived from some entire male pigs. The main causes of boar taint are the two compounds androstenone and skatole (3-methyl-indole). The steroid androstenone is a sex pheromone produced in the testis of the boars. Skatole is produced from tryptophan by bacteria in the intestine of the pigs. In many countries pigs are castrated as piglets to avoid boar taint, however, this is undesirable for animal welfare reasons. Genetic variations affecting the level of boar taint have previously been demonstrated in many breeds. In the study presented in this paper, markers and haplotypes, which can be applied to DNA-based selection schemes in order to reduce or eliminate the boar taint problem, are identified.ResultsApproximately 30,000 SNPs segregating in 923 boars from three Danish breeds; Duroc, Landrace, and Yorkshire, were used to conduct genome wide association studies of boar taint compounds. At 46 suggestive quantitative trait loci (QTL), 25 haplotypes and three single markers with effects were identified. Furthermore, 40% of the haplotypes mapped to previously identified regions. Haplotypes were also analysed for effects of slaughter weight and meat content. The most promising haplotype was identified on Sus scrofa chromosome 1. The gain in fixed effect of having this haplotype on level of androstenone in Landrace was identified to be high (1.279 μg/g). In addition, this haplotype explained 16.8% of the phenotypic variation within the trait. The haplotype was identified around the gene CYB5A which is known to have an indirect impact on the amount of androstenone. In addition to CYB5A, the genes SRD5A2, LOC100518755, and CYP21A2 are candidate genes for other haplotypes affecting androstenone, whereas, candidate genes for the indolic compounds were identified to be SULT1A1 and CYP2E1.ConclusionsDespite the small sample size, a total of 25 haplotypes and three single markers were identified including genomic regions not previously reported. The haplotypes that were analysed showed large effects on trait level. However, little overlap of QTL between breeds was observed.
BackgroundThe aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria.ResultsSeveral conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached.ConclusionIt is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.
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