MicroRNA-223 protects neurons from degeneration in Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is an autoimmune disease characterized by demyelination and neurodegeneration in the brain, spinal cord and optic nerve. Neuronal degeneration and death underlie progressive forms of MS and cognitive dysfunction. Neuronal damage is triggered by numerous harmful factors in the brain that engage diverse signalling cascades in neurons thus therapeutic approaches to protect neurons will need to focus on agents that can target broad biological processes. To target the broad spectrum of signaling events that mediate neurodegeneration in MS we have focused on non-coding small microRNAs (miRNAs). microRNAs are epigenetic regulators of protein expression, targeting messenger RNAs (mRNAs) and inhibiting their translation. Dysregulation of miRNAs has been described in many neurodegenerative diseases including MS. In this study we identified two miRNAs, miR-223-3p and miR-27a-3p, that were upregulated in neurons in the experimental autoimmune encephalomyelitis (EAE) mouse model of CNS inflammation and in active MS lesions. Overexpression of miR-27a-3p or miR-223-3p protected dissociated cortical neurons from degeneration in response to peripheral blood mononuclear cell conditioned media (PBMC-CM). Introduction of miR-223-3p in vivo in mouse retinal ganglion cells (RGCs) protected RGC axons from degeneration in the EAE model. By in silico analysis we found that mRNAs in the glutamate receptor (GluR) pathway are enriched in miR-27a-3p and miR-223-3p targets. Antagonism of the GluR pathway protected neurons from PBMC-CM-dependent degeneration. Our results suggest that miR-223-3p and miR-27a-3p are upregulated in response to inflammation to mediate a compensatory neuroprotective gene expression program that desensitizes neurons to glutamate by downregulating mRNAs involved in GluR signalling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".