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Goat mammary gland metabolism: An integrated Omics analysis to unravel seasonal weight loss tolerance

生物 组学 合成代谢 乳腺 脂质代谢 脂肪酸合成 分解代谢 生物技术 新陈代谢 脂肪酸 生物信息学 内分泌学 生物化学 遗传学 癌症 乳腺癌
作者
David M. Ribeiro,Mariana Palma,Josepa Salvadó,Lorenzo E. Hernández-Castellano,J. Capote,N. Castro,A. Argüello,Manolis Matzapetakis,Susana de Sousa Araújo,André M. Almeida
出处
期刊:Journal of Proteomics [Elsevier BV]
卷期号:289: 105009-105009
标识
DOI:10.1016/j.jprot.2023.105009
摘要

Seasonal weight loss (SWL), is a major limitation to animal production. In the Canary Islands, there are two dairy goat breeds with different levels of tolerance to SWL: Majorera (tolerant) and Palmera (susceptible). Our team has studied the response of these breeds to SWL using different Omics tools. The objective of this study was to integrate such results in a data driven approach and using dedicated tools, namely the DIABLO method. The outputs of our analysis mainly separate unrestricted from restricted goats. Metabolites behave as “hub” molecules, grouping interactions with several genes and proteins. Unrestricted goats upregulated protein synthesis, along with arginine catabolism and adipogenesis pathways, which are related with higher anabolic rates and a larger proportion of secretory tissue, in agreement with their higher milk production. Contrarily, restricted goats seemingly increased the synthesis of acetyl-CoA through serine and acetate conversion into pyruvate. This may have occurred to increase fatty acid synthesis and/or to use them as an energy source in detriment to glucose, which was more available in the diet of unrestricted goats. Lastly, restricted Palmera upregulated the expression of PEBP4 and GPD1 genes compared to all other groups, which might support their use as putative biomarkers for SWL susceptibility. Seasonal weight loss (SWL) is a major issue influencing animal production in the tropics and Mediterranean. By studying its impact on the mammary gland of tolerant and susceptible dairy goat breeds, using Omics, we aim at surveying the tissue for possible biomarkers that reflect these traits. In this study, data integration of three Omics (transcriptomics, proteomics and metabolomics) was performed using bioinformatic tools, to relate putative biomarkers and evaluate all three levels of information; in a novel approach. This information can enhance selection programs, lowering the impact of SWL on food production systems.
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