Long non-coding RNA rhabdomyosarcoma 2-associated transcript contributes to neuropathic pain by recruiting HuR to stabilize DNA methyltransferase 3 alpha mRNA expression in dorsal root ganglion neuron
Bibliographic record
Abstract
Introduction Long non-coding RNAs (lncRNAs) act as key regulators in multiple human diseases. In particular, the dysfunction of lncRNAs in dorsal root ganglion (DRG) contributes to the pathogenesis of neuropathic pain (NP). Nevertheless, the role and mechanism of most lncRNAs in NP remain unclear. Methods Two classic chronic NP models, including L4 spinal nerve ligation (SNL) model and chronic constriction injury (CCI) of the sciatic nerve, were performed. Mechanical allodynia and heat hyperalgesia were used to evaluate neuropathic pain. DRG microinjection was used to deliver agents into DRG. qRT-PCR, immunofluorescence, immunoprecipitation, western blotting, siRNA transfection, AAV transduction were performed to investigate the phenotypes and molecular basis. Results Here, we discovered that Rmst as a lncRNA was specifically expressed in Atf3+ injured DRG neurons and significantly upregulated following peripheral nerve damage. Rmst overexpression by direct DRG injection of AAV5-Rmst causes neuropathic symptoms in the absence of nerve damage. Conversely, blocking Rmst expression in injured DRGs alleviated nerve injury-induced pain hypersensitivities and downregulated Dnmt3a expression. Furthermore, we found peripheral nerve damage induced Rmst increase could interact with RNA-binding protein HuR to stabilize the Dnmt3a mRNA. Conclusion Our findings reveal a crucial role of Rmst in damaged DRG neurons under NP condition and provide a novel target for drug development against NP.
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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".