Proteomic biomarkers of beef colour

Molecular signatures of beef tenderness: Underlying mechanisms based on integromics of protein biomarkers from multi-platform proteomics studies

Over the last two decades, proteomics have been employed to decipher the underlying factors contributing to variation in the quality of muscle foods, including beef tenderness. One such approach is the application of high-throughput protein analytical platforms in the identification of meat quality biomarkers. To broaden our understanding about the biological mechanisms underpinning meat tenderization across a large number of studies, an integromics study was performed to review the current status of protein biomarker discovery targeting beef tenderness. This meta-analysis is the first to gather and propose a comprehensive list of 124 putative protein biomarkers derived from 28 independent proteomics-based experiments, from which 33 robust candidates were identified worthy of evaluation using targeted or untargeted data-independent acquisition proteomic methods. We further provide an overview of the interconnectedness of the main biological pathways impacting tenderness determination after multistep analyses including Gene Ontology annotations, pathway and process enrichment and literature mining, and specifically discuss the major proteins and pathways most often reported in proteomics research.

Protein Array-Based Approach to Evaluate Biomarkers of Beef Tenderness and Marbling in Cows: Understanding of the Underlying Mechanisms and Prediction

This study evaluated the potential of a panel of 20 protein biomarkers, quantified by Reverse Phase Protein Array (RPPA), to explain and predict two important meat quality traits, these being beef tenderness assessed by Warner-Bratzler shear force (WBSF) and the intramuscular fat (IMF) content (also termed marbling), in a large database of 188 Protected Designation of Origin (PDO) Maine-Anjou cows. Thus, the main objective was to move forward in the progression of biomarker-discovery for beef qualities by evaluating, at the same time for the two quality traits, a list of candidate proteins so far identified by proteomics and belonging to five interconnected biological pathways: (i) energy metabolic enzymes, (ii) heat shock proteins (HSPs), (iii) oxidative stress, (iv) structural proteins and (v) cell death and protein binding. Therefore, three statistical approaches were applied, these being Pearson correlations, unsupervised learning for the clustering of WBSF and IMF into quality classes, and Partial Least Squares regressions (PLS-R) to relate the phenotypes with the 20 biomarkers. Irrespective of the statistical method and quality trait, seven biomarkers were related with both WBSF and IMF, including three small HSPs (CRYAB, HSP20 and HSP27), two metabolic enzymes from the oxidative pathway (MDH1: Malate dehydrogenase and ALDH1A1: Retinal dehydrogenase 1), the structural protein MYH1 (Myosin heavy chain-IIx) and the multifunctional protein FHL1 (four and a half LIM domains 1). Further, three more proteins were retained for tenderness whatever the statistical method, among which two were structural proteins (MYL1: Myosin light chain 1/3 and TNNT1: Troponin T, slow skeletal muscle) and one was glycolytic enzyme (ENO3: β-enolase 3). For IMF, two proteins were, in this trial, specific for marbling whatever the statistical method: TRIM72 (Tripartite motif protein 72, negative) and PRDX6 (Peroxiredoxin 6, positive). From the 20 proteins, this trial allowed us to qualify 10 and 9 proteins respectively as strongly related with beef tenderness and marbling in PDO Maine-Anjou cows.

Current Trends in Proteomic Advances for Food Allergen Analysis

Food allergies are a global food challenge. For correct food labelling, the detection and quantification of allergens are necessary. However, novel product formulations and industrial processes produce new scenarios, which require much more technological developments. For this purpose, OMICS technologies, especially proteomics, seemed to be relevant in this context. This review summarises the current knowledge and studies that used proteomics to study food allergens. In the case of the allergenic proteins, a wide variety of isoforms, post-translational modifications and other structural changes during food processing can increase or decrease the allergenicity. Most of the plant-based food allergens are proteins with biological functions involved in storage, structure, and plant defence. The allergenicity of these proteins could be increased by the presence of heavy metals, air pollution, and pesticides. Targeted proteomics like selected/multiple reaction monitoring (SRM/MRM) have been very useful, especially in the case of gluten from wheat, rye and barley, and allergens from lentil, soy, and fruit. Conventional 1D and 2-DE immunoblotting have been further widely used. For animal-based food allergens, the widely used technologies are 1D and 2-DE immunoblotting followed by MALDI-TOF/TOF, and more recently LC-MS/MS, which is becoming useful to assess egg, fish, or milk allergens. The detection and quantification of allergenic proteins using mass spectrometry-based proteomics are promising and would contribute to greater accuracy, therefore improving consumer information.