Proteomics, Genomics, and Pathway Analyses of<i>Escherichia coli</i>and<i>Staphylococcus aureus</i>Infected Milk Whey Reveal Molecular Pathways and Networks Involved in Mastitis
Bibliographic record
Abstract
Gram-negative and -positive bacteria elicit different response patterns by the host. The proteomic profiles of milk whey samples from cows naturally infected with Escherichia coli or Staphyloccocus aureus as compared to whey from healthy cows were determined by one-dimensional, liquid chromatography-tandem mass spectrometry (LC-MS/MS), bioinformatics processing, and pathway analyses. Since mammary epithelial cells contribute to immune responses in mammary glands, the genes of selected proteins were measured in MAC-T cells by real time quantitative PCR (qPCR) after stimulation with heat inactivated E. coli strain P4 and S. aureus strain Smith CP bacteria. A total of 173 proteins were identified including 73 proteins differentially expressed among normal, E. coli, and S. aureus treatment groups. E. coli was more effective at significantly altering the concentration of the affected proteins. The mRNA of 23 proteins out of 24 measured by qPCR was significantly altered in MAC-T cells. Pathway analyses identified top canonical pathways significantly enriched in our samples, the most significant being the acute phase response signaling pathway. Also, top networks of genes with significant associations to identified proteins were identified. Our study has demonstrated a wider proteome profile of E. coli and S. aureus mastitic milk whey, identified more low abundant defense proteins than reported before, and has linked for the first time identified proteins to several network functions and Biocarta pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".