Nutritional and technological value of milk is largely influenced by milk fat quantity and fatty acid (FA) profile. However, the molecular mechanisms underlying the regulation of milk fat synthesis and secretion in the bovine mammary gland remain largely unknown. In the present study, we exploited an Association Weight Matrix (AWM) approach to build gene networks for bovine milk FA profile aiming to i) increase the knowledge about the functional relationships among set of genes affecting milk fat content and composition, ii) describe the biological functions regulating milk fat synthesis and secretion and iii) identify key transcription factors (TFs) controlling these mechanisms. The results of single- marker genome wide association studies (GWAS) across 65 FA and fat content in milk samples, as well as the genotypes for 37,568 single nucleotide polymorphisms (SNP) from a cohort of 1152 Italian Brown Swiss cows were used. An AWM-approach based on SNP co-associations allowed to predict a network of 791 genes related to milk FA profile. This network provided new insights into the crosstalk between distinct molecular pathways (e.g. mitogen-activated protein kinase (MAPK), lipid metabolism and hormone signaling) that were not detectable when analyzing individual SNP alone. In parallel, we focused on the TFs and their potential target genes within the AWM-derived network to identify the optimal subset of TFs spanning the majority of the network topology. Results highlighted BTB Domain and CNC Homolog 2 (BACH2), E2F Transcription Factor 3 (E2F3), and Lysine Demethylase 5A (KDM5A) as key regulators of milk FA metabolism. Functional analyses of the target genes by ClueGo confirmed that ontologies/pathways related to MAPK activity, cholesterol biosynthesis, hormonal signaling and reproduction were enriched (right-sided hypergeometric test, false discovery rate <0.05). In summary, our approach allowed to identify key regulators undetectable by the standard GWAS approach and provided novel insights into the physiological and cellular processes required for the synthesis and secretion of milk FA, improving the understanding of bovine mammary gland functionality. The new knowledge might help to develop selection strategies to improve milk quality for human consumption. Acknowledgements The research was funded by Trento Province (Italy), the Italian Brown Swiss Cattle Breeders Association (ANARB,Verona, Italy), and the Superbrown Consortium of Bolzano and Trento.

SNP co-association and network analyses for bovine milk fatty acid profile / Pegolo, Sara; Dadousis, Christos; Mach, Nuria; Ramayo-Caldas, Yuliaxis; Mele, Marcello; Schiavon, Stefano; Bittante, Giovanni; Cecchinato, Alessio. - 16:s1(2017), pp. 78-79. ((Intervento presentato al convegno ASPA 2017.

SNP co-association and network analyses for bovine milk fatty acid profile

Christos Dadousis;Giovanni Bittante;
2017

Abstract

Nutritional and technological value of milk is largely influenced by milk fat quantity and fatty acid (FA) profile. However, the molecular mechanisms underlying the regulation of milk fat synthesis and secretion in the bovine mammary gland remain largely unknown. In the present study, we exploited an Association Weight Matrix (AWM) approach to build gene networks for bovine milk FA profile aiming to i) increase the knowledge about the functional relationships among set of genes affecting milk fat content and composition, ii) describe the biological functions regulating milk fat synthesis and secretion and iii) identify key transcription factors (TFs) controlling these mechanisms. The results of single- marker genome wide association studies (GWAS) across 65 FA and fat content in milk samples, as well as the genotypes for 37,568 single nucleotide polymorphisms (SNP) from a cohort of 1152 Italian Brown Swiss cows were used. An AWM-approach based on SNP co-associations allowed to predict a network of 791 genes related to milk FA profile. This network provided new insights into the crosstalk between distinct molecular pathways (e.g. mitogen-activated protein kinase (MAPK), lipid metabolism and hormone signaling) that were not detectable when analyzing individual SNP alone. In parallel, we focused on the TFs and their potential target genes within the AWM-derived network to identify the optimal subset of TFs spanning the majority of the network topology. Results highlighted BTB Domain and CNC Homolog 2 (BACH2), E2F Transcription Factor 3 (E2F3), and Lysine Demethylase 5A (KDM5A) as key regulators of milk FA metabolism. Functional analyses of the target genes by ClueGo confirmed that ontologies/pathways related to MAPK activity, cholesterol biosynthesis, hormonal signaling and reproduction were enriched (right-sided hypergeometric test, false discovery rate <0.05). In summary, our approach allowed to identify key regulators undetectable by the standard GWAS approach and provided novel insights into the physiological and cellular processes required for the synthesis and secretion of milk FA, improving the understanding of bovine mammary gland functionality. The new knowledge might help to develop selection strategies to improve milk quality for human consumption. Acknowledgements The research was funded by Trento Province (Italy), the Italian Brown Swiss Cattle Breeders Association (ANARB,Verona, Italy), and the Superbrown Consortium of Bolzano and Trento.
SNP co-association and network analyses for bovine milk fatty acid profile / Pegolo, Sara; Dadousis, Christos; Mach, Nuria; Ramayo-Caldas, Yuliaxis; Mele, Marcello; Schiavon, Stefano; Bittante, Giovanni; Cecchinato, Alessio. - 16:s1(2017), pp. 78-79. ((Intervento presentato al convegno ASPA 2017.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2905110
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