Bovine milk-derived extracellular vesicles reduce oxidative stress and ferroptosis induced by Klebsiella pneumoniae in bovine mammary epithelial cells
Bibliographic record
Abstract
BACKGROUND: Ferroptosis is characterized by increased production of reactive oxygen species (ROS) and membrane lipid peroxidation that can exacerbate inflammatory damage. Extracellular vesicles (EVs) isolated from bovine milk have many biological functions, including antioxidant properties. However, the role of EVs on Klebsiella pneumoniae-induced ferroptosis and oxidative stress in bovine mammary epithelial cells (bMECs) and murine mammary tissue is unclear. In this study, EVs were isolated from bovine colostrum, mature milk and clinical mastitis milk (defined as C-EVs, M-EVs and CM-EVs, respectively) and assessed by transmission electron microscopy, Western blot and transcriptome sequencing. Effects of EVs on K. pneumoniae-induced ferroptosis and oxidative stress in bMECs were evaluated with immunofluorescence and Western blot. RESULTS: In bMECs, infection with K. pneumoniae induced oxidative stress, decreasing protein expression of Nrf2, Keap1 and HO-1 plus SOD activity, and increasing ROS concentrations. However, protein expression of GPX4, ACSL4 and S100A4 in bMECs, all factors that regulate ferroptosis, was downregulated by K. pneumoniae. Furthermore, this bacterium compromised tight junctions in murine mammary tissue, with low expression of ZO-1 and Occludin, whereas protein expression of Nrf2 and GPX4 was also decreased in mammary tissue. Adding C-EVs, M-EVs or CM-EVs reduced oxidative stress and ferroptosis in K. pneumoniae-infected bMECs in vitro and murine mammary tissues in vivo. CONCLUSION: In conclusion, all 3 sources of milk-derived EVs alleviated oxidative stress and ferroptosis in K. pneumoniae-infected bMECs and mammary tissues.
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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) | 0.001 | 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".