Time‐series transcriptional profiling yields new perspectives on susceptibility to murine osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Chronological age is a powerful epidemiologic risk factor for osteoarthritis (OA), a multifactorial disease that is characterized by articular cartilage (AC) degradation. It is unclear from a molecular perspective how aging interacts with OA to produce this risk to AC integrity. To address this key question, we used in vivo time-course analysis of OA development and murine interstrain variability in natural susceptibility to OA to examine changes in non-OA-prone CBA mice versus OA-prone STR/Ort mice, which develop disease that bears significant histologic resemblance to human OA. Through global transcriptome profiling, we attempted to discover the molecular signature linked with both OA vulnerability and progression. METHODS: Affymetrix Mouse Gene 1.0 ST Array profiles were generated from AC samples derived from CBA and STR/Ort mice at 3 different ages, corresponding to the stages prior to, at, and late after the natural onset of OA in the STR/Ort mice. RESULTS: We found that the OA in STR/Ort mice exhibited a molecular phenotype resembling human OA, and we pinpointed a central role of NF-κB signaling and the emergence of an immune-related signature in OA cartilage over time. We discovered that, strikingly, young healthy AC has a highly expressed skeletal muscle gene expression program, which is switched off during maturation, but is intriguingly retained in AC during OA development in STR/Ort mice. CONCLUSION: This study is the first to show that AC chondrocytes share a high-abundance gene-expression program with skeletal muscle. We show that failure to switch this program off, as well as the restoration of this program, is associated with inappropriate expression of NF-κB signaling pathways, skeletal muscle-related genes, and induction and/or progression of OA.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".