Loss of miR-145 promotes remyelination and functional recovery in a model of chronic central demyelination
Bibliographic record
Abstract
Strategies for treating progressive multiple sclerosis (MS) remain limited. Here, we found that miR-145-5p is overabundant uniquely in chronic lesion tissues from secondary progressive MS patients. We induced both acute and chronic demyelination in miR-145 knockout mice to determine its contributions to remyelination failure. Following acute demyelination, no advantage to miR-145 loss could be detected. However, after chronic demyelination, animals with miR-145 loss demonstrated increased remyelination and functional recovery, coincident with altered presence of astrocytes and microglia within the corpus callosum relative to wild-type animals. This improved response in miR-145 knockout animals coincided with a pathological upregulation of miR-145-5p in wild-type animals with chronic cuprizone exposure, paralleling human chronic lesions. Furthermore, miR-145 overexpression specifically in oligodendrocytes (OLs) severely stunted differentiation and negatively impacted survival. RNAseq analysis showed altered transcriptome in these cells with downregulated major pathways involved in myelination. Our data suggest that pathological accumulation of miR-145-5p is a distinctive feature of chronic demyelination and is strongly implicated in the failure of remyelination, possibly due to the inhibition of OL differentiation together with alterations in other glial cells. This is mirrored in chronic MS lesions, and thus miR-145-5p serves as a potential relevant therapeutic target in progressive forms of MS.
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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".