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High-throughput TMT-based proteomics for cow’s milk proteome characterization and detection of early protein biomarkers in mastitis

Objective: The occurrence of bovine mastitis (inflammation of the mammary gland) is considered to be one of the biggest challenges on dairy farms, due to reduction of the animal welfare and milk production and, consequently, economic loss for the farm. Therefore, the diagnosis of mastitis at subclinical stage and early treatment is of the utmost importance. However, the lack of clinical symptoms and unambiguous biomarkers for the diagnosis in early disease stage, prevent further advancements. Thus, the objective of this study was to implement the quantitative tandem mass tag (TMT) approach in combination with statistical and bioinformatics data processing for determination of quantitative changes in milk proteomic profiles during development and progression of mastitis on dairy farms. Methods: Milk samples (N = 138) were aseptically collected on dairy farms after physical examination and before the milking. Based on the results obtained for somatic cell count (SCC) and bacterial culture (BC) examination, milk samples were classified into experimental groups: healthy positive control (group with normal SCC and positive BC, N = 20), healthy negative control (group with normal SCC and negative BC, N = 20), subclinical mastitis (moderately increased SCC and positive BC, N = 80), clinical mastitis (high SCC and positive BC, N = 18). Samples were processed using TMT label-based quantitative approach. High resolution LC-MS/MS analysis was carried out using the Ultimate 3000 RSLCnano system (Dionex, Germering, Germany) coupled to Q Exactive Plus mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). Protein identification and quantification were performed using Proteome Discoverer software (version 2.3., Thermo Fisher Scientific) with SEQUEST algorithm implemented and database search against Bos taurus FASTA files. Statistical and bioinformatic analysis were done using R and Cytoscape. Results: Altogether, 386 quantifiable proteins were identified. There were 145 proteins with statistically different abundance between the groups. Gene Ontology analysis of proteins with significantly altered abundances showed that milk proteins were involved in biological adhesion, immune system process, biological regulation, interspecies interaction between organism, response to stimulus, signalling and others. Based on the statistical and fold change difference between investigated groups complement component 3, cathelicidin-1, cathelicidin-7, lactoferrin, haptoglobin, and chitinase-3-like protein were identified as potential biomarkers for subclinical mastitis. Conclusion: Shotgun TMT-based high-resolution proteomic profiling allowed identification of potential milk biomarkers for differentiation of mastitis grade in dairy cows. After validation of selected proteins, these findings might provide a valuable contribution to understanding of mastitis pathophysiology and might be applicable to the development of a new cow-side diagnostic tool.

milk, mastitis, label-based proteomics

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