Beyond miRNA Cargo Profiles: Anti‐Inflammatory Roles of Extracellular Vesicle‐Enriched miRNAs Derived From Human Intervertebral Disc Cells Unveiled by Functional Testing
Bibliographic record
Abstract
ABSTRACT Intervertebral disc (IVD) degeneration is a leading cause of chronic low back pain and a major contributor to global disability. Understanding the molecular mechanisms underlying this condition is essential for developing targeted therapies. Among these mechanisms, microRNAs (miRNAs) have emerged as critical post‐transcriptional regulators of gene expression in IVD cells, influencing key processes such as extracellular matrix (ECM) homeostasis, inflammatory signalling and cellular senescence. Extracellular vesicles (EVs), which transport miRNAs between cells, represent a promising avenue for therapeutic intervention. However, the composition of their miRNA cargo across different stages of disc degeneration remains inadequately characterised. We isolated EVs from primary human IVD cells derived from non‐degenerate, mildly degenerate, and severely degenerate tissues, and performed small RNA sequencing to profile their miRNA content. Bioinformatic analyses revealed enrichment in pathways related to ECM‐receptor interaction, focal adhesion, inflammation and cell cycle regulation. Notably, let‐7b‐5p and miR‐100‐5p were among the most abundant miRNAs and were significantly lower in EVs from degenerate discs. Functional assays demonstrated that transfection of IVD cells with let‐7b‐5p or miR‐100‐5p mimics individually suppressed IL‐1ß expression at both mRNA and protein levels, confirming their anti‐inflammatory roles. Strikingly, co‐delivery of both miRNAs enhanced suppression of pro‐inflammatory mediators, reduced senescence‐associated p16 expression and upregulated TIE2 mRNA, indicating synergistic effects in promoting a regenerative cell phenotype. These findings highlight the regulatory roles of EV‐enriched let‐7b‐5p and miR‐100‐5p in modulating inflammation and senescence in IVD cells, and underscore the potential of miRNA‐loaded EVs as cell‐free regenerative therapies for disc degeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".