RARβ2 expression is induced by the down‐regulation of microRNA 133a during caudal spinal cord regeneration in the adult newt
Bibliographic record
Abstract
BACKGROUND: Adult urodele amphibians represent unique model organisms to study spinal cord regeneration. Trauma to the spinal cord induces an ependymal response, activating multipotent neural stem cells that contribute to the redifferentiation of both glia and neurons in the regenerate. The molecular events underlying this ependymal response are not completely understood, but likely involve coordinated global changes in gene expression. MicroRNAs and retinoid signaling are postulated to orchestrate these patterns of gene expression in response to trauma. Our objectives were to determine the roles played by some miRNAs as potential regulators of retinoid signaling in this process. RESULTS: We found that the expression levels of miRNAs 133a, 203, and 124a are dysregulated during the first 21 days post amputation (dpa). Interestingly, these miRNAs are expressed primarily within the ependymoglia. We have shown in vitro that a miR-133a mimic targets the 3' UTR of the newt RARβ2 transcript. Importantly, upregulation of this mimic in vivo led to a significant decline in RARβ2 protein at 14 dpa and inhibited regeneration. CONCLUSIONS: These data are the first to link miRNAs and retinoid signaling during spinal cord regeneration and provide support for miR-133a as an upstream regulator of RARβ2 expression in this process.
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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 | 0.001 |
| 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".