PSV-25 Detection of heart and aorta tissue peptide markers by the multiple-reaction monitoring method
Abstract Functional, particularly personalized meat-based foods are of more in demand by a consumer today. Functional additives, such as plant components and animal proteins from bovine or porcine tissues have been successfully used. With many ingredients added to foods, it is important to provide quality and composition monitoring to confirm the products’ authenticity, to identify undeclared or rarely used types of raw meat in product formulations. For example, if animal heart tissue is a component of a product formulation or if aorta tissue presents in a product due to improper trimming. Different methods are used to identify raw materials, including new approaches in proteomics and peptidomics that are considered the most effective modern methods nowadays. The purpose of the study is meat product composition analysis and special biomarker peptide identification to confirm the presence of heart and aorta tissue in a finished meat product. Over 20 amino acid sequences were checked based on earlier obtained data. Those amino acid sequences were analyzed with a high-performance liquid chromatography with mass spectrometric detection as described. The MS settings were selected using the Skyline. Signal-to-Noise ratio (S/N) over 10 units were used to choose the best peptide candidates. Seven peptides were found in porcine hearts. The best candidate was peptide VNVDEVGGEALGR (S/N - 73.10±5.3) from β-Hemoglobin. Two marker peptides from serum albumin were selected for pork aorta: TVLGNFAAFVQK (S/N 53.51±2.4) and EVTEFAK (S/N 31.69±4.1). These biomarkers showed the best detection and specificity. The multiply reaction monitoring method made it possible to identify the most/best specific peptides—biomarkers that could confirm the heart and/or aorta in meat products. The method can be used for comparative research or identification of best peptides that are specific to any type of animal tissue. The work was supported by the Russian Science Foundation, project no. 16-16- 10073.
