Livestock metabolomics and the livestock metabolome: A systematic reviewReport as inadecuate




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Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular -omics- approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits i.e., feed efficiency, growth potential and milk production. A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs. These data have been made available through an open access, comprehensive livestock metabolome database LMDB, available at http:-www.lmdb.ca. The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.



Author: Seyed Ali Goldansaz, An Chi Guo, Tanvir Sajed, Michael A. Steele, Graham S. Plastow, David S. Wishart

Source: http://plos.srce.hr/



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