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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.

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.

The efficiency with which ruminants convert feed to desirable products is difficult to measure under normal commercial settings. We explored the use of potential biological markers from easily obtainable samples, that is, blood, hair, and feces, to characterize potential causes of divergent efficiency when considered as residual feed intake (RFI) or feed conversion efficiency (FCE). A total of 54 Charolais bulls, 20 in period 1 and 34 in period 2, were examined for individual dry matter intake (DMI) and growth. Bulls were offered a diet of 70:30 wrapped grass silage to concentrate for 99 d. At the conclusion of the test period, blood samples were collected for the determination of vitamins B 2 and B 6 , and plasma used for the determination of metabolites, natural isotopic 15 N abundance ( 15 N NIA, expressed as δ 15 N ‰) and fractionation (Δ 15 N plasma proteins-diet and Δ 13 C plasma proteins-diet ) and near-infrared spectroscopy (NIRS). Feces were analyzed by NIRS. Bulls were slaughtered at 15-17 months of age and carcass characteristics determined. Bulls were ranked according to RFI with extremes (SD ± 0.5; n = 31) classified as either efficient (Neg-RFI) or inefficient (Pos-RFI). Extreme bulls were then classified for FCE (high vs low FCE), changing the groups. Pos-RFI bulls consumed 14% more feed than Neg-RFI bulls for the same level of weight gain. Low FCE bulls tended to eat more, but had lower weight gains than high FCE bulls. No differences were detected in carcass conformation, fat scores, hot carcass weight, or dressing percentage. Yet, heart and bladder weights were heavier in Pos-RFI, and rumen weight tended to be heavier in Pos-RFI bulls. RFI did not affect bulk 15 N or 13 C fractionation. A negative correlation was observed between FCE and Δ 15 N plasma proteins-diet . Inefficient bulls (Pos-RFI) had higher δ 15 N in glycine compared to Neg-RFI bulls. Similarly, metabolomic analysis showed a tendency for concentrations of glycine and sarcosine to be elevated in Pos-RFI bulls, whereas aspartic acid and carnosine tended to be elevated, and serine tended to be lower in High FCE. Among vitamins, only flavin adenine dinucleotide concentration was higher in the blood of bulls with High FCE. These results suggest that the two feed efficiency metrics differ in the underlying mechanisms of metabolism, where RFI is driven by differences in the energetic requirements of visceral organs and the extent of AA catabolism.

For beef cattle research, a main objective is to control concomitantly the development of muscles and the qualities of beef cuts. Beef quality is a complex phenotype that is only detectable after slaughter and is highly variable. The beef industry is in need of tools to estimate beef quality of live cattle or online in abattoirs, with specific attention towards sensory attributes (tenderness, juiciness, flavour, and colour). Identification of relevant genetic and genomic markers is ongoing, especially for tenderness--a top priority quality attribute. In this paper, we describe the steps of an expression marker-based strategy to improve beef sensory quality, from the discovery of biomarkers that identify consistent beef and the biological functions governing beef tenderness to the integration of the knowledge into detection tests for desirable animals. These tools should soon be available for the management of sensory quality in the beef production chain for meeting market's demands and assuring good quality standards.

Previous proteomic analyses established a list of proteins biomarkers of beef tenderness. The present study quantified the relative abundance of 21 of these proteins by dot-blot technique in the Longissimus thoracis and Semitendinosus muscles of 71 young bulls from three breeds: Aberdeen Angus (AA), Limousin (LI), and Blond d'Aquitaine (BA). For both muscles overall tenderness was estimated by sensory analysis; shear force was measured with a Warner-Bratzler instrument, and an index combining sensory and mechanical measurements was calculated. Multiple regressions based on relative abundances of these proteins were used to propose equations of prediction of the three evaluations of tenderness. Hsp70-1B appeared to be a good biomarker of low tenderness in the three breeds and in the two muscles. Proteins such as lactate dehydrogenase-B, myosin heavy chain IIx, and small heat shock proteins (Hsp27, Hsp20, and αB-crystallin) were related to tenderness but inversely according to the muscle and breed. The results demonstrate that prediction of tenderness must take into account muscle characteristics and animal type.

Beef tenderness is a very complex and multifactorial sensorial meat quality trait, which depends partly on muscle characteristics. This tissue is very variable according to animal type (age, breed and sex) and rearing conditions. Consequently, beef tenderness exhibits a great variability. Different research programs have revealed several genes or proteins which could be good markers of beef tenderness. In order to validate the relation of these markers with beef tenderness on a large population of bovines, it is necessary to have a large-scale and trusty technique which can access different quantities of proteins related to tenderness. In this study we firstly compared Western-Blot and Dot-Blot. Secondly, we evaluated Dot-Blot technical and biological capabilities for the quantification of protein biomarkers. The results demonstrated that the Dot-Blot technique with fluorescence detection presents numerous interests. This technique allows a good reproducibility and permits the simultaneous analysis of a large number of samples. The Dot-Blot technique defined and validated in this study can be used for protein biomarkers analyses, notably to predict beef tenderness. Another major result of this study is that about 5 to 10 animals per group are required to detect large differences (>1.5) in biomarker expression between tender and tough beef, whereas much larger numbers of animals (10 to 30) are required to detect smaller differences (about 1.2 to 1.3) taking into account the biological variability of these markers.

Some proteins have been revealed as biomarkers for beef tenderness by previous studies. These markers could be used in immunological tests to predict beef tenderness, in living animals as well as in carcasses. It is well known that rearing practices modify the amounts of mRNA and proteins. Therefore, the reliability of protein tests could be affected by livestock and biological effects such as production systems, breed, muscle and animal type. This study analysed the effects of animal and muscle type on 24 proteins. The animals studied were 67 young bulls and 44 steers of the Charolais breed, and muscles were Longissimus thoracis and Semitendinosus. Protein amounts were determined by Dot blot, an immunological technique. Results showed that expressions of 20 proteins were influenced by animal and/or muscle type. These results could lead to modifications and adaptations of prediction tests according to rearing practice, bovine breed and beef cut. [ABSTRACT FROM AUTHOR]

The knowledge of the molecular organization of living organisms evolved considerably during the last years. The methodologies associated also progressed with the development of the high-throughput sequencing (SNP array, RNAseq, etc.) and of genomic tools allowing the simultaneous analysis of hundreds or thousands of genes, proteins or metabolites. In farm animals, some proteins, mRNAs or metabolites whose abundance has been associated with meat quality traits have been detected in pig, cattle, chicken. They constitute biomarkers for the assessment and prediction of qualities of interest in each species, with potential biomarkers across species. The ongoing development of rapid methods will allow their use for decision-making and management tools in slaughterhouses, to better allocate carcasses or cuts to the appropriate markets. Besides, their application on living animals will help to improve genetic selection and to adapt a breeding system to fulfill expected quality level. The ultimate goal is to propose effective molecular tools for the management of product quality in meat production chains. [ABSTRACT FROM AUTHOR]