Activation of the JAK/STAT3 and PI3K/AKT pathways are crucial for IL-6 trans-signaling-mediated pro-inflammatory response in human vascular endothelial cells
Bibliographic record
Abstract
BACKGROUND: IL-6 classic signaling is linked to anti-inflammatory functions while the trans-signaling is associated with pro-inflammatory responses. Classic signaling is induced via membrane-bound IL-6 receptor (IL-6R) whereas trans-signaling requires prior binding of IL-6 to the soluble IL-6R. In both cases, association with the signal transducing gp130 receptor is compulsory. However, differences in the downstream signaling mechanisms of IL-6 classic- versus trans-signaling remains largely elusive. METHODS: In this study, we used flow cytometry, quantitative PCR, ELISA and immuno-blotting techniques to investigate IL-6 classic and trans-signaling mechanisms in Human Umbilical Vein Endothelial Cells (HUVECs). RESULTS: We show that both IL-6R and gp130 are expressed on the surface of human vascular endothelial cells, and that the expression is affected by pro-inflammatory stimuli. In contrast to IL-6 classic signaling, IL-6 trans-signaling induces the release of the pro-inflammatory chemokine Monocyte Chemoattractant Protein-1 (MCP-1) from human vascular endothelial cells. In addition, we reveal that the classic signaling induces activation of the JAK/STAT3 pathway while trans-signaling also activates the PI3K/AKT and the MEK/ERK pathways. Furthermore, we demonstrate that MCP-1 induction by IL-6 trans-signaling requires simultaneous activation of the JAK/STAT3 and PI3K/AKT pathways. CONCLUSIONS: Collectively, our study reports molecular differences in IL-6 classic- and trans-signaling in human vascular endothelial cells; and elucidates the pathways which mediate MCP-1 induction by IL-6 trans-signaling.
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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) | 0.003 | 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".