PROTEOMIC ANALYSIS OF BOVINE MILK PROTEINS TO IDENTIFY PUTATIVE BIOMARKERS OF Staphylococcus aureus SUBCLINICAL MASTITIS
Bibliographic record
Abstract
Bovine mastitis remains a primary focus of dairy cattle disease research due to its negative economic impact on the dairy industry. In Canada, total losses are estimated more than four hundred million dollars per year, or about 15 % of the industry’s total net revenue. Clinical mastitis is associated with visible local and systemic signs of inflammation of the udder. In contrast, subclinical mastitis (SCM) lacks clinical signs and often leads to persistent and chronic infections that represent a serious problem to the dairy industry. Staphylococcus aureus is the most common contagious pathogen associated with bovine SCM. Current diagnosis of S. aureus SCM is based on bacteriological culture of milk samples and somatic cell counts both of have limitations. The main objective of this study was to identify, characterize and quantify the differential expression of whey proteins in milk samples collected from healthy control cows and cows that are infected with S. aureus SCM utilizing different proteomic approaches. The first study characterized variations in the composition of the whey proteome in healthy and S. aureus mastitic milk samples using an optimized fractionation strategy to enrich for low- abundance proteins. Fractionation of the whey proteins using low speed ultracentrifugation resulted in partial depletion of casein and minimized protein losses. In addition, two-dimensional difference gel electrophoresis enhanced the separation and resolution of low abundant whey proteins. In the second study, 2D-DIGE coupled with liquid chromatography and tandem mass spectrometry (LC-MS/MS) showed the differentially expressed proteomic signatures of S. aureus-positive whey fractions compared to samples from healthy controls. Twenty-eight upregulated proteins in mastitic milk were identified, 11 of which had related host defense functions. In the third study, quantitative proteomic analyses using direct LC-MS/MS and label-free quantification resulted in identification of 90 proteins in both control and mastitic milk samples of which 25 proteins were differentially regulated including pathogen-recognition and acute phase proteins. The comprehensive proteomic and bioinformatics analyses resulted in four candidate biomarkers including cathelicidin-4, haptoglobin, cathepsin B and lactotransferrin for mastitis diagnosis that also provide insight to understanding the role of milk proteins in host-pathogen interaction during S. aureus intramammary infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".