Plasma proteomics reveals crosstalk between lipid metabolism and immunity in dairy cows receiving essential fatty acids and conjugated linoleic acid

Essential fatty acids (EFA) and conjugated linoleic acids (CLA) are unsaturated fatty acids with immune-modulatory effects, yet their synergistic effect is poorly understood in dairy cows. This study aimed at identifying differentially abundant proteins (DAP) and their associated pathways in dairy cows supplied with a combination of EFA and CLA during the transition from antepartum (AP) to early postpartum (PP). Sixteen Holstein cows were abomasally infused with coconut oil as a control (CTRL) or a mixture of EFA (linseed + safflower oil) and CLA (Lutalin, BASF) (EFA + CLA) from − 63 to + 63 days relative to parturition. Label-free quantitative proteomics was performed on plasma samples collected at days − 21, + 1, + 28, and + 63. During the transition time, DAP, consisting of a cluster of apolipoproteins (APO), including APOE, APOH, and APOB, along with a cluster of immune-related proteins, were related to complement and coagulation cascades, inflammatory response, and cholesterol metabolism. In response to EFA + CLA, specific APO comprising APOC3, APOA1, APOA4, and APOC4 were increased in a time-dependent manner; they were linked to triglyceride-enriched lipoprotein metabolisms and immune function. Altogether, these results provide new insights into metabolic and immune adaptation and crosstalk between them in transition dairy cows divergent in EFA + CLA status.

Prediction of the Secretome and the Surfaceome: A Strategy to Decipher the Crosstalk between Adipose Tissue and Muscle during Fetal Growth

Crosstalk between adipose and muscular tissues is hypothesized to regulate the number of muscular and adipose cells during fetal growth, with post-natal consequences on lean and fat masses. Such crosstalk largely remains, however, to be described. We hypothesized that a characterization of the proteomes of adipose and muscular tissues from bovine fetuses may enhance the understanding of the crosstalk between these tissues through the prediction of their secretomes and surfaceomes. Proteomic experiments have identified 751 and 514 proteins in fetal adipose tissue and muscle. These are mainly involved in the regulation of cell proliferation or differentiation, but also in pathways such as apoptosis, Wnt signalling, or cytokine-mediated signalling. Of the identified proteins, 51 adipokines, 11 myokines, and 37 adipomyokines were predicted, together with 26 adipose and 13 muscular cell surface proteins. Analysis of protein–protein interactions suggested 13 links between secreted and cell surface proteins that may contribute to the adipose–muscular crosstalk. Of these, an interaction between the adipokine plasminogen and the muscular cell surface alpha-enolase may regulate the fetal myogenesis. The in silico secretome and surfaceome analyzed herein exemplify a powerful strategy to enhance the elucidation of the crosstalk between cell types or tissues.

Aggregation of Omic Data and Secretome Prediction Enable the Discovery of Candidate Plasma Biomarkers for Beef Tenderness

Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation. Seventy-one proteins putatively released in the plasma were revealed. Among them 13 proteins were predicted to be secreted in plasma, 44 proteins as hypothetically secreted in plasma, and 14 additional candidate proteins were detected thanks to network analysis. Among these 71 proteins, 24 were included in tenderness quantitative trait loci. The in-silico workflow enabled the discovery of candidate plasma biomarkers for beef tenderness from reconstruction of the secretome, to be examined in the cattle plasma proteome.

Predicting the Quality of Meat: Myth or Reality?

This review is aimed at providing an overview of recent advances made in the field of meat quality prediction, particularly in Europe. The different methods used in research labs or by the production sectors for the development of equations and tools based on different types of biological (genomic or phenotypic) or physical (spectroscopy) markers are discussed. Through the various examples, it appears that although biological markers have been identified, quality parameters go through a complex determinism process. This makes the development of generic molecular tests even more difficult. However, in recent years, progress in the development of predictive tools has benefited from technological breakthroughs in genomics, proteomics, and metabolomics. Concerning spectroscopy, the most significant progress was achieved using near-infrared spectroscopy (NIRS) to predict the composition and nutritional value of meats. However, predicting the functional properties of meats using this method-mainly, the sensorial quality-is more difficult. Finally, the example of the MSA (Meat Standards Australia) phenotypic model, which predicts the eating quality of beef based on a combination of upstream and downstream data, is described. Its benefit for the beef industry has been extensively demonstrated in Australia, and its generic performance has already been proven in several countries.

Proteomic Analysis of Secreted Proteins from Cell Microenvironment

Cell microenvironment consists of various types of cells which communicate with each other by vast number of secreted proteins. An unbiased profiling of these secreted proteins on a global scale is often critical for understanding the intercellular signaling in an autocrine or paracrine manner. Mass spectrometry-based proteomics has become one of the most popular technology for characterization of the secreted proteins. In this chapter, we discuss the standard workflow for secreted proteins characterization, including harvesting secreted proteins from conditioned media, digesting the obtained proteins, liquid chromatography-mass spectrometry analysis, and downstream data analysis.