Selenomethionine activates selenoprotein S, suppresses Fas/FasL and the mitochondrial pathway, and reduces Escherichia coli-induced apoptosis of bovine mammary epithelial cells
Bibliographic record
Abstract
Escherichia coli is a major environmental pathogen causing bovine mastitis, characterized by cell death and mammary tissue damage. Apoptosis, a form of cell death, has an important role in the pathogenesis of mastitis. Selenium, an essential trace element, protects against mastitis by acting through several biochemical pathways, potentially including prevention of apoptosis. Our objective was to investigate whether selenomethionine (SeMet) attenuated E. coli-induced apoptosis in bovine mammary epithelial cells (bMEC). These cells were cultured in vitro and treated with 0, 5, 10, 20, and 40 μM SeMet for 12 h, with or without E. coli (multiplicity of infection of 5) for 8 h. Treatment with SeMet/Z-IE(OMe)TD(OMe)-FMK (ZIK)/Z-LE(OMe)HD(OMe)-FMK (ZLK, specific inhibitors of caspase-8 and -9, respectively) significantly counteracted effects of E. coli on bMEC. Specifically, SeMet upregulated selenoprotein S (SeS) and increased mitochondrial membrane potential and the ratio of Bcl-2 and Bax. Furthermore, it decreased protein expressions of Fas, FasL, FADD, cleaved caspase-8, cytochrome c, cleaved caspase-9, and cleaved caspase-3, namely, decreasing protein expression of the Fas/FasL and mitochondrial pathways. Furthermore, it downregulated total apoptosis indexes in E. coli-infected bMEC. Although ZIK and ZLK (specific inhibitors of caspases 8 and 9, respectively) significantly inhibited Fas/FasL and the mitochondrial apoptotic pathway and apoptosis indexes, respectively, substantial apoptosis still occurred. In conclusion, SeMet attenuated E. coli-induced apoptosis in bMEC by activating SeS, associated with Fas/FasL and mitochondrial 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".