Elucidating another level of epigenetic regulation in osteoarthritis by identifying functional <i>cis</i> -acting long non-coding RNAs and their targets in articular cartilage
Bibliographic record
Abstract
ABSTRACT Objective To identify robustly differentially expressed long non-coding RNAs (lncRNAs) with osteoarthritis (OA) pathophysiology in cartilage. Moreover to explore potential target mRNAs by establishing co-expression networks, followed by functional validation. Methods RNA sequencing was performed on macroscopically lesioned and preserved OA cartilage of patients who underwent a joint replacement surgery due to OA (N=98). Differential expression (DE) analysis was performed on lncRNAs that were annotated in GENCODE and Ensembl. To identify potential interactions, correlations were calculated between the identified DE lncRNAs and previously reported DE protein-coding genes in the same samples. Modulation of chondrocyte lncRNA expression was achieved using LNA GapmeRs. Results By applying our in-house pipeline we identified 5,053 lncRNAs to be robustly expressed, of which 191 were FDR significant differentially expressed between lesioned and preserved OA cartilage. Upon integrating mRNA sequencing data, we showed that intergenic and antisense DE lncRNAs show high, positive correlations with their flanking, respectively, sense genes. To functionally validate this observation we selected P3H2-AS1 , which was downregulated in primary chondrocytes, resulting in downregulation of P3H2 gene expression levels. As such, we can confirm that P3H2-AS1 regulates its sense gene P3H2 . Conclusion By applying an improved detection strategy, robustly differentially expressed lncRNAs in OA cartilage were detected. Integration of these lncRNAs with differential mRNA expression levels in the same samples showed insight into their regulatory networks. Our data signifies that intergenic, as well as antisense lncRNAs play an important role in regulating the pathophysiology of OA.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".