One of the primary factors contributing to boar taint is the level of androstenone in porcine adipose tissues. A majority of the studies performed to identify candidate biomarkers for the synthesis of androstenone in testis tissues follow a reductionist approach, identifying and studying the effect of biomarkers individually. Although these studies provide detailed information on individual biomarkers, a global picture of changes in metabolic pathways that lead to the difference in androstenone synthesis is still missing. The aim of this work was to identify major pathways and interactions influencing steroid hormone synthesis and androstenone biosynthesis using an integrative approach to provide a bird's eye view of the factors causing difference in steroidogenesis and androstenone biosynthesis. For this purpose, we followed an analysis procedure merging together gene expression data from boars with divergent levels of androstenone and pathway mapping and interaction network retrieved from KEGG database. The interaction networks were weighted with Pearson correlation coefficients calculated from gene expression data and significant interactions and enriched pathways were identified based on these networks. The results show that 1,023 interactions were significant for high and low androstenone animals and that a total of 92 pathways were enriched for significant interactions. Although published articles show that a number of these enriched pathways were activated as a result of downstream signaling of steroid hormones, we speculate that the significant interactions in pathways such as glutathione metabolism, sphingolipid metabolism, fatty acid metabolism and significant interactions in cAMP-PKA/PKC signaling might be the key factors determining the difference in steroidogenesis and androstenone biosynthesis between boars with divergent androstenone levels in our study. The results and assumptions presented in this study are from an in-silico analysis done at the gene expression level and further laboratory experiments at genomic, proteomic or metabolomic level are necessary to validate these findings.
One of the primary factors contributing to boar taint is the level of androstenone in porcine adipose
tissues. Majority of the studies performed to identify candidate biomarkers for the synthesis of
androstenone in testis tissues follow a reductionist approach, identifying and studying the effect of
biomarkers individually. Although these studies provide detailed information on individual biomarkers,
a global picture of changes in metabolic pathways that lead to the difference in androstenone synthesis
is still missing. The aim of this work was to identify major pathways and interactions influencing
steroid hormone synthesis and androstenone biosynthesis using an integrative approach to provide a bird’s eye view of the factors causing difference in steroidogenesis and androstenone biosynthesis. For
this purpose, we followed an analysis procedure merging together gene expression data from boarswith divergent levels of androstenone and pathway mapping and interaction network retrieved from KEGG database. The interaction networks were weighted with Pearson correlation coefficients calculated from gene expression data and significant interactions and enriched pathways were identified based on these networks. The results show that 1,023 interactions were significant for high and low androstenone animals and that a total of 92 pathways were enriched for significant interactions. Although published articles show that a number of these enriched pathways were activated as a result of downstream signaling of steroid hormones, we speculate that the significant interactions in pathways such as glutathione metabolism, sphingolipid metabolism, fatty acid metabolism and significant interactions in cAMP-PKA/PKC signaling might be the key factors determining the difference in steroidogenesis and androstenone biosynthesis between boars with divergent androstenone levels in our study. The results and assumptions presented in this study are from an in
silico analysis done at the gene expression level and further laboratory experiments at genomic, proteomic or metabolomic level are necessary to validate these findings.
