Bacterial lipopolysaccharide induces increased expression of toll-like receptor (TLR) 4 and downstream TLR signaling molecules in bovine mammary epithelial cells
Bibliographic record
Abstract
Bovine mammary epithelial cells contribute to the innate immune response to intramammary infections by recognizing pathogens through specialized pattern recognition receptors. Toll-like receptor 4 (TLR4) is one such receptor that binds and is activated by lipopolysaccharide (LPS), a component of the outer envelope of Gram-negative bacteria. In this study, MAC-T cells (a bovine mammary epithelial cell line) were incubated in the presence or absence of increasing concentrations of LPS for 24 h. Expression of TLR2 and TLR4 were analyzed at both mRNA and protein levels by quantitative real-time PCR (qPCR) and flow cytometry, respectively. The mRNA of both receptors were up-regulated by all concentrations of LPS used (P<0.01). Similarly, flow cytometry with specific antibodies against TLR2 and TLR4 detected increased surface expression of these proteins. Furthermore, expression of downstream TLR4 signaling molecules was examined by qPCR following varying exposure times to 1 mug/mL of LPS. Results demonstrate that the required adaptor molecules and transcription factors were up-regulated in a time-dependent manner. Both the MyD88 dependent and independent pathways in TLR4 signaling were activated in MAC-T cells. Expression of TOLLIP increased in response to LPS as did the pro-apoptotic protease, CASP8. These results suggest that the bovine mammary epithelium possesses the necessary immune repertoires required to achieve a robust defense against E. coli. The current findings, coupled with previous findings that S. aureus ligands induce up-regulation of TLR4, may indicate a positive adaptation by mammary epithelial cells to effectively respond to different types of mastitis pathogens.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".