Tulathromycin Exerts Proresolving Effects in Bovine Neutrophils by Inhibiting Phospholipases and Altering Leukotriene B<sub>4</sub>, Prostaglandin E<sub>2</sub>, and Lipoxin A<sub>4</sub>Production
Bibliographic record
Abstract
The accumulation of neutrophils and proinflammatory mediators, such as leukotriene B4 (LTB4), is a classic marker of inflammatory disease. The clearance of apoptotic neutrophils, inhibition of proinflammatory signaling, and production of proresolving lipids (including lipoxins, such as lipoxin A4 [LXA4]) are imperative for resolving inflammation. Tulathromycin (TUL), a macrolide used to treat bovine respiratory disease, confers immunomodulatory benefits via mechanisms that remain unclear. We recently reported the anti-inflammatory properties of TUL in bovine phagocytes in vitro and in Mannheimia haemolytica-challenged calves. The findings demonstrated that this system offers a powerful model for investigating novel mechanisms of pharmacological immunomodulation. In the present study, we examined the effects of TUL in a nonbacterial model of pulmonary inflammation in vivo and characterized its effects on lipid signaling. In bronchoalveolar lavage (BAL) fluid samples from calves challenged with zymosan particles (50 mg), treatment with TUL (2.5 mg/kg of body weight) significantly reduced pulmonary levels of LTB4 and prostaglandin E2 (PGE2). In calcium ionophore (A23187)-stimulated bovine neutrophils, TUL inhibited phospholipase D (PLD), cytosolic phospholipase A2 (PLA2) activity, and the release of LTB4. In contrast, TUL promoted the secretion of LXA4 in resting and A23187-stimulated neutrophils, while levels of its precursor, 15(S)-hydroxyeicosatetraenoic acid [15(S)-HETE], were significantly lower. These findings indicate that TUL directly modulates lipid signaling by inhibiting the production of proinflammatory eicosanoids and promoting the production of proresolving lipoxins.
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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".