Proteomic 2D-DIGE Analysis of Milk Whey from Dairy Cows with Staphylococcus aureus Mastitis Reveals Overexpression of Host Defense Proteins
Bibliographic record
Abstract
Bovine mastitis remains a primary focus of dairy cattle disease research due to its considerable negative economic impact on the dairy industry. Subclinical mastitis (SCM), commonly caused by Staphylococcus aureus, lacks overt clinical signs and the diagnosis is based on bacteriological culture and somatic cell counts of milk, both of which have limitations. The main objective of this study was to identify, characterize and quantify the differential abundance of milk whey proteins from cows with S. aureus SCM compared to whey from healthy cows. Using two-dimensional differential gel electrophoresis (2D-DIGE) coupled with liquid chromatography and tandem mass spectrometry, 28 high-abundant proteins were detected in whey from mastitic milk, 9 of which had host defense functions. These included acute phase proteins involved in innate immunity and antimicrobial functions (e.g., serotransferrin, complement C3, fibrinogen gamma-B chain and cathepsin B), and proteins associated with the immune response to pathogens (e.g., polymeric immunoglobulin receptor-like protein, MHC class I antigen and beta-2-microglobulin). These results provide a unique 2D map of the modulated milk proteome during S. aureus mastitis. The broader importance is that the identified proteins, particularly those with host-defense biological functions, represent potential candidate biomarkers of subclinical mastitis in dairy cows.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".