Exploring the Impact of TLR‐2 Signaling on miRNA Dysregulation in Intervertebral Disc Degeneration
Bibliographic record
Abstract
Toll-like receptors (TLRs) are key mediators of inflammation in intervertebral disc (IVD) degeneration. TLR-2 activation contributes to the degenerative process by increasing the expression of extracellular matrix-degrading enzymes, pro-inflammatory cytokines, and neurotrophins. As potent post-transcriptional regulators, microRNAs can modulate intracellular mechanisms, and their dysregulation is known to contribute to numerous pathologies. This study aims to investigate the impact of TLR-2 signaling on miRNA dysregulation in the context of IVD degeneration. Small-RNA sequencing of degenerated IVD cells shows the dysregulation of ten miRNAs following TLR-2 activation by PAM2CSK4. The miR-155-5p is most significantly upregulated in degenerated and non-degenerated annulus fibrosus and nucleus pulposus cells. Sequence-based target and pathway prediction shows the involvement of miR-155-5p in inflammation- and cell fate-related pathways and TLR-2-induced miR-155-5p expression leads to the downregulation of its target c-FOS. Furthermore, changes specific to the activation of TLR-2 through fragmented fibronectin are seen in miR-484 and miR-487. Lastly, miR-100-3p, miR-320b, and miR-181a-3p expression exhibit degeneration-dependent changes. These results show that TLR-2 signaling leads to the dysregulation of miRNAs in IVD cells as well as their possible downstream effects on inflammation and degeneration. The identified miRNAs provide important opportunities as potential therapeutic targets for IVD degeneration and low back pain.
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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) | 0.001 | 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".