Adaptor Proteins and Ras Synergistically Regulate IL-1-Induced ADAMTS-4 Expression in Human Chondrocytes
Bibliographic record
Abstract
Aggrecanases (a disintegrin [corrected] and metalloproteinase with thrombospondin motif, ADAMTSs) are principal proteases involved in cartilage extracellular matrix aggrecan degradation. The role and relative contribution of MyD88, IRAK1, and TRAF6 adaptor proteins in IL-1beta regulation of aggrecanase-1 (ADAMTS-4) is unknown. By small interfering RNAs-mediated knockdown, we show that IL-1beta-induced up-regulation of ADAMTS-4 in chondrocytes requires MyD88, IRAK1, and TRAF6 adaptor proteins. However, partial inhibition of ADAMTS-4 induction by their knockdown suggested the involvement of additional signaling proteins. Because IL-1beta is also known to induce reactive oxygen species (ROS) through Ras-mediated activation of NADPH oxidase, we investigated the implication of Ras in ADAMTS-4 regulation. Ras knockdown, or inhibition of ROS by antioxidants along with the ablation of MyD88, IRAK1, or TRAF6 more potently down-regulated IL-1beta-induced ADAMTS-4. In addition, IL-1beta-induced phosphorylation of downstream effectors, IkappaB kinase alphabeta, IkappaBalpha, and activation of transcription factor NF-kappaB was significantly reduced in the MyD88-, IRAK1-, TRAF6-, or Ras-deficient cells. The combined knockdown of Ras and individual adaptor proteins strongly blocked the activation of IKKalphabeta, IkappaBalpha, and NF-kappaB. These findings suggest that Ras, ROS along with MyD88, IRAK1, or TRAF6 synergistically mediate ADAMTS-4 regulation by IL1-beta. Thus, complete ablation of ADAMTS-4 induction could be achieved by combined inhibition of Ras and individual adaptor proteins, which may be of therapeutic value in arthritis.
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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 | 0.001 |
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".