Evidence for Epigenetic Regulation of Gene Expression and Function in Chronic Experimental Diabetic Neuropathy
Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) is a common but irreversible neurodegenerative complication of diabetes mellitus. Here we show that features of sensory neuron damage in mice with chronic DPN may have altered epigenetic micro RNA (miRNA) transcriptional control. We profiled sensory neuron messenger RNA and miRNA profiles in mice with type I diabetes mellitus and findings of DPN. Diabetic sensory dorsal root ganglia neurons showed a pattern of altered messenger RNA profiles associated with upregulated cytoplasmic sites of miRNA-mediated messenger RNA processing (GW/P bodies). Dorsal root ganglia miRNA microarray identified significant changes in expression among mice with diabetes, the most prominent of which were a 39% downregulation of mmu-let-7i and a 255% increase in mmu-miR-341; both were identified in sensory neurons. To counteract these alterations, we replenished let-7i miRNA by intranasal administration; in a separate experiment, we added an anti-miR that antagonized elevated mmu-341 after 5 months of diabetes. Both approaches independently improved electrophysiologic, structural, and behavioral abnormalities without altering hyperglycemia; control sequences did not have these effects. Dissociated adult sensory neurons exposed to an exogenous mmu-let-7i mimic displayed enhanced growth and branching, indicating a trophic action. These findings identify roles for epigenetic miRNA alterations and enhanced GW/P expression in diabetic dorsal root ganglia that contribute to the complex DPN phenotype.
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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".