Mechanical stress and inflammation have opposite effects on Wnt signaling in human chondrocytes
Bibliographic record
Abstract
Dysregulation of Wingless and Int-1 (Wnt) signaling has been strongly associated with development and progression of osteoarthritis (OA). Here, we set out to investigate the independent effects of either mechanical stress (MS) or inflammation on Wnt signaling in human neocartilage pellets, and to relate this Wnt signaling to OA pathophysiology. OA synovium-conditioned media (OAS-CM) was collected after incubating synovium from human end-stage OA joints for 24 h in medium. Cytokine levels in the OAS-CM were determined with a multiplex immunoassay (Luminex). Human neocartilage pellets were exposed to 20% MS, 2% OAS-CM or 1 ng/mL Interleukin-1β (IL-1β). Effects on expression levels of Wnt signaling members were determined by reverse transcription-quantitative polymerase chain reaction. Additionally, the expression of these members in articular cartilage from human OA joints was analyzed in association with joint space narrowing (JSN) and osteophyte scores. Protein levels of IL-1β, IL-6, IL-8, IL-10, tumor necrosis factor α, and granulocyte-macrophage colony-stimulating factor positively correlated with each other. MS increased noncanonical WNT5A and FOS expression. In contrast, these genes were downregulated upon stimulation with OAS-CM or IL-1β. Furthermore, Wnt inhibitors DKK1 and FRZB decreased in response to OAS-CM or IL-1β exposure. Finally, expression of WNT5A in OA articular cartilage was associated with increased JSN scores, but not osteophyte scores. Our results demonstrate that MS and inflammatory stimuli have opposite effects on canonical and noncanonical Wnt signaling in human neocartilage. Considering the extent to which MS and inflammation contribute to OA in individual patients, we hypothesize that targeting specific Wnt pathways offers a more effective, individualized approach.
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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 | 0.000 |
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".