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TMT‐Based Proteomics Profiling of Bovine Liver Underscores Protein Markers of Anabolic Treatments
Author(s) -
Biancotto Giancarlo,
Bovo Davide,
Mastrorilli Eleonora,
Manuali Elisabetta,
Angeletti Roberto,
Stella Roberto
Publication year - 2019
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201800422
Subject(s) - proteomics , anabolism , shotgun proteomics , anabolic agents , quantitative proteomics , computational biology , profiling (computer programming) , chemistry , bioinformatics , chromatography , biology , biochemistry , computer science , gene , operating system
Illegal use of growth promoter compounds in food production exposes consumers to health risk. Surveillance of such practices is based on direct detection of drugs or related metabolites by HPLC‐MS/MS. Screening strategies focusing on indirect biological responses are considered promising tools to improve surveillance. In this study, an untargeted shotgun proteomics approach based on tandem mass tags (TMTs) is carried out to identify proteins altered in bovine liver after different anabolic treatments. Three controlled pharmacological treatments with dexamethasone, a combination of dexamethasone and clenbuterol, or a combination of sexual steroids (trenbolone and estradiol) are analyzed. Untargeted TMT analysis of liver digests by high resolution MS allowed for the relative quantification of proteins. Thanks to partial least squarediscriminant analysis, a set of proteins capable to classify animals treated with dexamethasone alone (11 proteins), or in combination with clenbuterol (13 proteins) are identified. No significant difference is found upon administration of sexual steroids. After relative quantification of candidate markers by parallel reaction monitoring (PRM), two predictive models are trained to validate protein markers. Finally, an independent animal set of control bulls and bulls treated with dexamethasone is analyzed by PRM to further validate a predictive model giving an accuracy of 100%.