microRNA-544a as a new modulator of the Wnt-signalling network in the articular cartilage and osteoarthritis
Bibliographic record
Abstract
Abstract Objective Determining the effect of microRNA-544a (miR-544a) in articular chondrocytes isolated from patients affected by osteoarthritis (OA) and its role in the modulation of the Wnt signalling. Methods Articular chondrocytes were isolated from patients undergoing joint replacement because of OA. Expression levels of miR-544a were measured by PCR and by in situ hybridization. Putative targets of miR-544a were confirmed by reporter assay and by qPCR in cells stimulated with a miR-544a mimic. The effect of miR-544a on chondrocyte metabolism was monitored by qPCR for phenotypic markers, protein expression levels of aggrecan neoepitopes/MMP-13 and modulation of alcian blue content in micromass cultures, upon stimulation with a miR-544a mimic. The expression levels of MMP-13 and Aggrecan neoepitopes in response to miR-544a stimulation was also measured in co-stimulation with Xav-939 and KN93, which are respectively β-catenin and CaMKII inhibitors. Results Our results suggest that miR-544a enhances the activation of the Wnt-signalling in the articular chondrocytes, by downregulating the expression of components of the Wnt/β-catenin destruction complex. The expression of miR-544a is higher in chondrocytes isolated from damaged areas of the articular cartilage removed from OA patients, and can be upregulated by pro-inflammatory and pro-fibrotic cytokines. miR-544a exerts a pro-catabolic effect of articular chondrocytes, which is rescued both by the inhibition of the Wnt/β-catenin and Wnt/CaMKII signalling pathways. Conclusion our results point to miR-544a as a new, important modulator of the Wnt signalling network within the articular cartilage suggesting a key role for microRNAs in regulating how the multiple branches of the network and their interaction modulate cartilage homeostasis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".