Mevalonate Biosynthesis Intermediates Are Key Regulators of Innate Immunity in Bovine Endometritis
Bibliographic record
Abstract
Metabolic changes can influence inflammatory responses to bacteria. To examine whether localized manipulation of the mevalonate pathway impacts innate immunity, we exploited a unique mucosal disease model, endometritis, where inflammation is a consequence of innate immunity. IL responses to pathogenic bacteria and LPS were modulated in bovine endometrial cell and organ cultures by small molecules that target the mevalonate pathway. Treatment with multiple statins, bisphosphonates, squalene synthase inhibitors, and small interfering RNA showed that inhibition of farnesyl-diphosphate farnesyl transferase (squalene synthase), but not 3-hydroxy-3-methylglutaryl-CoA reductase or farnesyl diphosphate synthase, reduced endometrial organ and cellular inflammatory responses to pathogenic bacteria and LPS. Although manipulation of the mevalonate pathway reduced cellular cholesterol, impacts on inflammation were independent of cholesterol concentration as cholesterol depletion using cyclodextrins did not alter inflammatory responses. Treatment with the isoprenoid mevalonate pathway-intermediates, farnesyl diphosphate and geranylgeranyl diphosphate, also reduced endometrial cellular inflammatory responses to LPS. These data imply that manipulating the mevalonate pathway regulates innate immunity within the endometrium, and that isoprenoids are regulatory molecules in this process, knowledge that could be exploited for novel therapeutic strategies.
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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 | 0.000 |
| 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".