Increased IL-6 receptor expression and signaling in ageing cartilage can be explained by loss of TGF-β-mediated IL-6 receptor suppression
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) development is strongly associated with ageing, possibly due to age-related changes in transforming growth factor-β (TGF-β) signaling in cartilage. Recently, we showed that TGF-β suppresses interleukin (IL)-6 receptor (IL-6R) expression in chondrocytes. As IL-6 is involved in cartilage degeneration, we hypothesized that age-related loss of TGF-β signaling results in increased IL-6R expression and signaling in ageing cartilage. DESIGN: Bovine articular cartilage was collected and immediately processed to study age-related changes in IL-6R expression using qPCR and IHC (age-range: 0.5-14 years). Moreover, cartilage from young and aged cows was stimulated with rhIL-6 and/or rhTGF-β1 to measure IL-6-induced p-STAT3 using Western blot. Expression of STAT3-responsive genes was analyzed using qPCR. RESULTS: Expression of IL-6 receptor (bIL-6R) significantly increased in cartilage upon ageing (slope: 0.32, 95%CI: 0.20-0.45), while expression of glycoprotein 130 (bGP130) was unaffected. Cartilage stimulation with IL-6 showed increased induction of p-STAT3 upon ageing (slope: 0.14, 95%CI: 0.08-0.20). Furthermore, IL-6-mediated induction of STAT3-responsive genes like bSOCS3 and bMMP3 was increased in aged compared to young cartilage. Interestingly, the ability of TGF-β to suppress bIL6R expression in young cartilage was lost upon ageing (slope: 0.21, 95%CI: 0.13-0.30). Concurrently, an age-related loss in TGF-β-mediated suppression of IL-6-induced p-STAT3 and bSOCS3 expression was observed. CONCLUSIONS: Ageing results in enhanced IL-6R expression and subsequent IL-6-induced p-STAT3 signaling in articular cartilage. This is likely caused by age-related loss of protective TGF-β signaling, resulting in loss of TGF-β-mediated IL-6R suppression. Because of the detrimental role of IL-6 in cartilage, this mechanism may be involved in age-related OA development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".