Heparin Synergistically Enhances Interleukin-11 Signaling through Up-regulation of the MAPK Pathway
Bibliographic record
Abstract
Using an animal model of heparin-induced osteoporosis we previously demonstrated that heparin causes bone loss, in part, by increasing osteoclast number and activity. Furthermore, we found that, although heparin alone has no effect, it is able to synergistically enhance Interleukin-11 (IL-11)-induced signal transducer and activator of transcription 3 (STAT3) activation and thus increase osteoclast formation in vitro. In the present study, we examine the effect of various serine kinase inhibitors on the ability of heparin to act synergistically with IL-11. Inhibition of the c-Jun N-terminal kinase (JNK), p38 mitogen-activated protein kinase (MAPK), or the phosphatidylinositol 3-kinase pathways had no effect on the ability of heparin to promote either IL-11-induced STAT3.DNA complex formation or osteoclast formation in vitro. In contrast, PD098059, a MAPK kinase inhibitor, completely abolished the synergy between heparin and IL-11. In an attempt to resolve the mechanism by which this was occurring, we examined the effect of heparin on STAT3 Ser-727 phosphorylation and extracellular signal-regulated kinases 1 and 2 (Erk1/2) activation, either in the presence or absence of IL-11. Heparin alone was found to have no effect on Ser-727 phosphorylation, nor did heparin alter the phosphorylation status of Ser-727 in the presence of IL-11. Heparin was, however, found to increase Erk1/2 activation in both a time- and dose-dependent manner. When taken together, these findings suggest that heparin enhances IL-11-induced STAT3 activation and thus osteoclast formation, by a mechanism that is independent of STAT3 Ser-727 phosphorylation but that involves up-regulation of the MAPK pathway.
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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) | 0.001 | 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".