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Milk proteome from in silico data aggregation allows the identification of putative biomarkers of negative energy balance in dairy cows

Abstract : A better knowledge of the bovine milk proteome and its main drivers is a prerequisite for the modulation of bioactive proteins in milk for human nutrition, as well as for the discovery of biomarkers that are useful in husbandry and veterinary medicine. Milk composition is affected by lactation stage and reflects, in part, the energy balance of dairy cows. We aggregated the cow milk proteins reported in 20 recent proteomics publications to produce an atlas of 4654 unique proteins. A multistep assessment was applied to the milk proteome datasets according to lactation stages and milk fractions, including annotations, pathway analysis and literature mining. Fifty-nine proteins were exclusively detected in milk from early lactation. Among them, we propose six milk proteins as putative biomarkers of negative energy balance based on their implication in metabolic adaptative pathways. These proteins are PCK2, which is a gluconeogenic enzyme; ACAT1 and IVD, which are involved in ketone metabolism; SDHA and UQCRC1, which are related to mitochondrial oxidative metabolism; and LRRC59, which is linked to mammary gland cell proliferation. The cellular origin of these proteins warrants more in-depth research but may constitute part of a molecular signature for metabolic adaptations typical of early lactation.
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Mylène Delosière, José Pires, Laurence Bernard, Isabelle Cassar-Malek, Muriel Bonnet. Milk proteome from in silico data aggregation allows the identification of putative biomarkers of negative energy balance in dairy cows. Scientific Reports, Nature Publishing Group, 2019, 9, ⟨10.1038/s41598-019-46142-7⟩. ⟨hal-02627972⟩

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