Dataset Information

Selected reaction monitoring mass spectrometry of mastitis milk reveals pathogen-specific regulation of bovine host response proteins.

ABSTRACT: Mastitis is a major challenge to bovine health. The detection of sensitive markers for mastitis in dairy herds is of great demand. Suitable biomarkers should be measurable in milk and should report pathogen-specific changes at an early stage to support earlier diagnosis and more efficient treatment. However, the identification of sensitive biomarkers in milk has remained a challenge, in part due to their relatively low concentration in milk. In the present study, we used a selected reaction monitoring (SRM) mass spectrometry approach, which allowed the absolute quantitation of 13 host response proteins in milk for the first time. These proteins were measured over a 54-h period upon an in vivo challenge with cell wall components from either gram-negative (lipopolysaccharide from Escherichia coli; LPS) or gram-positive bacteria (peptidoglycan from Staphylococcus aureus; PGN). Whereas our data clearly demonstrate that all challenged animals have consistent upregulation of innate immune response proteins after both LPS and PGN challenge, the data also reveal clearly that LPS challenge unleashes faster and shows a more intense host response compared with PGN challenge. Biomarker candidates that may distinguish between gram-negative and gram-positive bacteria include ?-2 macroglobulin, ?-1 antitrypsin, haptoglobin, serum amyloid A3, cluster of differentiation 14, calgranulin B, cathepsin C, vanin-1, galectin 1, galectin 3, and IL-8. Our approach can support further studies of large cohorts of animals with natural occurring mastitis, to validate the relevance of these suggested biomarkers in dairy production.

SUBMITTER: Kusebauch U

PROVIDER: S-EPMC6502260 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Selected reaction monitoring mass spectrometry of mastitis milk reveals pathogen-specific regulation of bovine host response proteins.

Kusebauch Ulrike U Hernández-Castellano Lorenzo E LE Bislev Stine L SL Moritz Robert L RL Røntved Christine M CM Bendixen Emøke E

Journal of dairy science 20180411 7

Mastitis is a major challenge to bovine health. The detection of sensitive markers for mastitis in dairy herds is of great demand. Suitable biomarkers should be measurable in milk and should report pathogen-specific changes at an early stage to support earlier diagnosis and more efficient treatment. However, the identification of sensitive biomarkers in milk has remained a challenge, in part due to their relatively low concentration in milk. In the present study, we used a selected reaction moni ...[more]

PMID: 29655560

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Project description:For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

Dataset Information

Selected reaction monitoring mass spectrometry of mastitis milk reveals pathogen-specific regulation of bovine host response proteins.

Publications

Selected reaction monitoring mass spectrometry of mastitis milk reveals pathogen-specific regulation of bovine host response proteins.

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