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Application of omics tools to study performance, carcass and meat quality traits of feedlot cattle: omics integration using "from-farm-to-fork" concept

Abstract

The Brazilian beef cattle production supply about 20% of the total beef consumed in the world. However, producers and meatpacking industries have few mechanisms that allow for the identification of efficient cattle in an integrated manner, which will produce carcasses and meat cuts with superior quality to serve the most demanding and best-paying markets. The "efficiency × quality" paradigm occurs because there are limitations on the use of technology in these sectors and little basic research. This project will evaluate feedlot cattle to classify the animals in terms of feed efficiency (FE), comparing groups formed based on residual feed intake (RFI) on carcass and meat quality traits. Nellore bulls reared on pasture and contemporary groups will be used. All animals will be produced in a single commercial farm, being offspring of bulls evaluated by the breeding program of Bos indicus breeds. Data on dry matter intake (DMI), FE and daily weight gain (DWG) and RFI will be collected for at least 70 days. Subsequently, in the meatpacking industry, the carcasses of these animals will be evaluated for the characteristics of final pH, temperature, fat and meat color, ribeye area (REA), marbling and subcutaneous fat thickness (SFT). In total, 200 cattle will be evaluated, and individualized information on FE, RFI, carcass and meat traits will be obtained. Biological samples will be collected from the striploin (Longissimus thoracis muscle), being used in the laboratory for molecular biology assays (Proteomics; Lipidomics and Metabolomics; 6 to 10 animals/group) and physical-chemical analyzes of meat quality (shear force, cooking losses, color , intramuscular fat, glycogen, among others). The experimental groups (RFI negative/more efficient vs. RFI positive/less efficient) will be compared for carcass and meat quality traits. In addition, associations of Proteomics, Lipidomics and Metabolomics data from animals will be analyzed using a multivariate approach (PCA, PLS regression, clustering methods: k-means, hierarchical cluster analysis and partitioning around medoids) and multiway models. The measurement of FE phenotypes such as the RFI, along with basic research and integrative statistical methods, is still little explored in Brazilian studies, and may contribute to the generation of new technologies and improve the identification of important economic traits in beef cattle, benefiting national meat chain. (AU)

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