Impact of Anti-Inflammatory Agents on the Gene Expression Profile of Stimulated Human Neutrophils: Unraveling Endogenous Resolution Pathways
Bibliographic record
Abstract
Adenosine, prostaglandin E(2), or increased intracellular cyclic AMP concentration each elicit potent anti-inflammatory events in human neutrophils by inhibiting functions such as phagocytosis, superoxide production, adhesion and cytokine release. However, the endogenous molecular pathways mediating these actions are poorly understood. In the present study, we examined their impact on the gene expression profile of stimulated neutrophils. Purified blood neutrophils from healthy donors were stimulated with a cocktail of inflammatory agonists in the presence of at least one of the following anti-inflammatory agents: adenosine A(2A) receptor agonist CGS 21680, prostaglandin E(2), cyclic-AMP-elevating compounds forskolin and RO 20-1724. Total RNA was analyzed using gene chips and real-time PCR. Genes encoding transcription factors, enzymes and regulatory proteins, as well as secreted cytokines/chemokines showed differential expression. We identified 15 genes for which the anti-inflammatory agents altered mRNA levels. The agents affected the expression profile in remarkably similar fashion, suggesting a central mechanism limiting cell activation. We have identified a set of genes that may be part of important resolution pathways that interfere with cell activation. Identification of these pathways will improve understanding of the capacity of tissues to terminate inflammatory responses and contribute to the development of therapeutic strategies based on endogenous resolution.
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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".