P2X7-Like Receptor Activation in Astrocytes Increases Chemokine Monocyte Chemoattractant Protein-1 Expression via Mitogen-Activated Protein Kinase
Bibliographic record
Abstract
Leukocyte infiltration in the CNS after trauma or inflammation is triggered in part by upregulation of the chemokine, monocyte chemoattractant protein-1 (MCP-1), in astrocytes. However the signals that induce the upregulation of MCP-1 in astrocytes are unknown. We have investigated the roles for ATP P2X7 receptor activation because ATP is an intercellular signaling transmitter that is released in both trauma and inflammation and P2X7 receptors are involved in immune system signaling. Astrocytes in primary cell culture and acutely isolated from the hippocampus were immunopositive for P2X7 receptors. In astrocyte cultures, application of the selective P2X7 agonist, benzoyl-benzoyl ATP (Bz-ATP), activated MAP kinases extracellular signal receptor-activated kinase 1 (ERK1), ERK2, and p38. Purinergic antagonists depressed this activation with a profile suggesting P2X7 receptors. Bz-ATP also increased MCP-1 expression in cultured astrocytes, and again P2X7 antagonists prevented this increase. Blocking either the ERK1/ERK2 or the p38 pathway (with PD98059 or SB203580, respectively) significantly inhibited Bz-ATP-induced MCP-1 expression. Coapplication of both antagonists caused a greater depression. We also tested the roles for ATP receptor activation in inducing MCP-1 upregulation in corticectomy, an in vivo model of trauma. This model of cortical trauma was previously shown to increase MCP-1 expression in vivo principally in astrocytes. Suramin, a wide-spectrum purinergic receptor antagonist, significantly depressed the rapid (3 hr) trauma-induced increase in MCP-1 mRNA. These data indicate that purinergic transmitter receptors in astrocytes are important in regulating chemokine synthesis. The regulation of MCP-1 in astrocytes by ATP may be important in mediating communication with hematopoietic inflammatory cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".