The Role of Long Noncoding RNA MALAT1 in Diabetic Polyneuropathy and the Impact of Its Silencing in the Dorsal Root Ganglion by a DNA/RNA Heteroduplex Oligonucleotide
Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) is the most common complication of diabetes, yet its pathophysiology has not been established. Accumulating evidence suggests that long noncoding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays pivotal roles in the regulation of cell growth and survival during diabetic complications. This study aimed to investigate the impact of MALAT1 silencing in dorsal root ganglion (DRG) sensory neurons, using an α-tocopherol-conjugated DNA/RNA heteroduplex oligonucleotide (Toc-HDO), on the peripheral nervous system of diabetic mice. We identified MALAT1 upregulation in the DRG of chronic diabetic mice that suggested either a pathological change or one that might be protective, and systemic intravenous injection of Toc-HDO effectively inhibited its gene expression. However, we unexpectedly noted that this intervention paradoxically exacerbated disease with increased thermal and mechanical nociceptive thresholds, indicating further sensory loss, greater sciatic-tibial nerve conduction slowing, and additional declines of intraepidermal nerve fiber density in the hind paw footpads. Serine/arginine-rich splicing factors, which are involved in pre-mRNA splicing by interacting with MALAT1, reside in nuclear speckles in wild-type and diabetic DRG neurons; MALAT1 silencing was associated with their disruption. The findings provide evidence for an important role that MALAT1 plays in DPN, suggesting neuroprotection and regulation of pre-mRNA splicing in nuclear speckles. This is also the first example in which a systemically delivered nucleotide therapy had a direct impact on DRG diabetic neurons and their axons.
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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".