Activating EGFR Signaling Attenuates Osteoarthritis Development Following Loading Injury in Mice
Bibliographic record
Abstract
Posttraumatic osteoarthritis (PTOA) results in joint pain, loss of joint function, and impaired quality of daily life in patients with limited treatment options. We previously demonstrated that epidermal growth factor receptor (EGFR) signaling is essential for maintaining chondroprogenitors during articular cartilage development and homeostasis. Here, we used a nonsurgical, loading-induced PTOA mouse model to investigate the protective action of EGFR signaling. A single bout of cyclic tibial loading at a peak force of 6 N injured cartilage at the posterior aspect of lateral femoral condyle. Similar loading at a peak force of 9 N ruptured the anterior cruciate ligament, causing additional cartilage damage at the medial compartment and ectopic cartilage formation in meniscus and synovium. Constitutively overexpression of an EGFR ligand, heparin binding EGF-like growth factor (HBEGF), in chondrocytes significantly reduced cartilage injury length, synovitis, and pain after 6 N loading and mitigated medial side cartilage damage and ectopic cartilage formation after 9 N loading. Mechanistically, overactivation of EGFR signaling protected chondrocytes from loading-induced apoptosis and loss of proliferative ability and lubricant synthesis. Overexpressing HBEGF in adult cartilage starting right before 6 N loading had similar beneficial effects. In contrast, inactivating EGFR in adult cartilage led to accelerated PTOA progression with elevated cartilage Mankin score and synovitis score and increased ectopic cartilage formation. As a therapeutic approach, we constructed a nanoparticle conjugated with the EGFR ligand TGFα. Intra-articular injections of this nanoconstruct once every 3 weeks for 12 weeks partially mitigated PTOA symptoms in cartilage and synovium after 6 N loading. Our findings demonstrate the anabolic actions of EGFR signaling in maintaining articular cartilage during PTOA development and shed light on developing a novel nanomedicine for PTOA. © 2022 American Society for Bone and Mineral Research (ASBMR).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".