A synthetic circular RNA targeting miR-340-5p promotes optic nerve regeneration and retinal ganglion cell survival following axotomy
Bibliographic record
Abstract
The regeneration of axons in the mammalian central nervous system is severely restricted, owing to an age-dependant reduction in intrinsic regenerative capacity, a loss of neurotrophic support, and an inhibitory growth environment. In the dorsal root ganglion (DRG) conditioning lesion model, peripheral axotomy results in transcriptional reprogramming of neurons into a regenerative state, but this reprogramming involves extensive multi-gene changes that are difficult to recapitulate in non-regenerating CNS neurons. MicroRNAs are small non-coding RNAs that individually regulate groups of related genes and are attractive as tool compounds for modulating complex transcriptional changes in cells. Computational modelling was applied to single-cell RNA sequencing datasets from mice subjected to the DRG conditioning lesion paradigm, identifying individual miRNAs that target multiple regeneration associated genes (RAGs). Inhibiting miR-340-5p derepresses RAGs and promotes neurite growth in vitro. A circular RNA sponge designed to sequester miR-340-5p (Circ-340-5p) disinhibits RAGs in the retinal ganglion cells of male and female C57BL/6 mice, whilst simultaneously activating pro-regenerative PI3K signalling and pro-survival BDNF/TRKB signalling. Circ-340-5p enhanced post-axotomy neuronal survival acutely following ONC, but this effect was not sustained at six weeks. Circ-340-5p promoted axon regeneration and the extent of regeneration improved over time. Our findings establish an approach for recapitulating multi-gene transcriptional changes in neurons to promote axon regeneration and neuronal survival following axotomy, and outline a platform for the development of long-acting miRNA-targeting therapeutics.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".