A Damaging <i>COL6A3</i> Variant Alters the <i>MIR31HG</i> ‐Regulated Response of Chondrocytes in Neocartilage Organoids to Hyperphysiologic Mechanical Loading
Bibliographic record
Abstract
The pericellular matrix (PCM), with its hallmark proteins collagen type VI (COLVI) and fibronectin (FN), surrounds chondrocytes and is critical in transducing the biomechanical cues. To identify genetic variants that change protein function, exome sequencing is performed in a patient with symptomatic OA at multiple joint sites. A predicted damaging variant in COL6A3 is identified and introduced by CRISPR-Cas9 genome engineering in two established human induced pluripotent stem cell-derived in-vitro neocartilage organoid models. The downstream effects of the COL6A3 variant on the chondrocyte phenotypic state are studied by a multi-omics (mRNA and lncRNA) approach in interaction with hyper-physiological mechanical loading conditions. The damaging variant in COL6A3 results in significantly lower binding between the PCM proteins COLVI and FN and provokes an osteoarthritic chondrocyte state. By subsequently exposing the neocartilage organoids to hyperphysiological mechanical stress, it is demonstrated that the COL6A3 variant in chondrocytes abolishes the characteristic inflammatory signaling response after mechanical loading with PTGS2, PECAM1, and ADAMTS5, as central genes. Finally, by integrating epigenetic regulation, the lncRNA MIR31HG is identified as key regulator of the characteristic inflammatory signaling response to mechanical loading.
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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) | 0.002 | 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".