TNF-α promotes LPA1- and LPA3-mediated recruitment of leukocytes in vivo through CXCR2 ligand chemokines
Bibliographic record
Abstract
Lysophosphatidic acid (LPA) is a bioactive lysophospholipid present in low concentrations in serum and biological fluids but in high concentrations at sites of inflammation. LPA evokes a variety of cellular responses via binding to and activation of its specific G protein-coupled receptors (GPCR), namely LPA(1-6). Even though LPA is a chemoattractant for inflammatory cells in vitro, such a role for LPA in vivo remains largely unexplored. In the present study, we used the murine air pouch model to study LPA-mediated leukocyte recruitment in vivo using selective LPA receptor agonist/antagonist and LPA(3)-deficient mice. We report that 1) LPA injection into the air pouch induced leukocyte recruitment and that both LPA(1) and LPA(3) were involved in this process; 2) LPA stimulated the release of the pro-inflammatory chemokines keratinocyte-derived chemokine (KC) and interferon-inducible protein-10 (IP-10) in the air pouch; 3) tumor necrosis factor-α (TNF-α) injected into the air pouch prior to LPA strongly potentiated LPA-mediated secretion of cytokines/chemokines, including KC, IL-6, and IP-10, which preceded the enhanced leukocyte influx; and 4) blocking CXCR2 significantly reduced leukocyte infiltration. We suggest that LPA, via LPA(1) and LPA(3) receptors, may play a significant role in inducing and/or sustaining the massive infiltration of leukocytes during inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".