MiR‐146b‐5p Decreases Cytokine Release From Astrocytes and Preserves Oligodendrocyte Progenitor Cell Complexity During Inflammation
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic immune-mediated demyelinating disease of the central nervous system (CNS) and is most often clinically presented in a relapsing form. Within MS lesions, oligodendrocyte progenitor cells (OPCs) differentiate into mature myelinating oligodendrocytes and mediate repair. A further understanding of the molecular mechanisms responsible for OPC differentiation will undoubtedly influence the direction of future treatments in MS. In MS lesions, several distinct microRNAs have been previously demonstrated to influence both inflammatory and repair mechanisms, including OPC differentiation and survival. Specifically, miR-146b-5p is an anti-inflammatory microRNA that is upregulated in white matter astrocytes within active MS lesions. Our results demonstrate that increasing miR-146b-5p levels within pure primary human and murine astrocytes significantly decreases IL-6 and CXCL10 production upon IL-1β stimulation, an effect not observed in mixed glial cultures containing microglia. In addition, the inhibitory effects of IL-1β on OPC differentiation and complexity were reversed when miR-146b-5p levels were increased in astrocytes; no differences were observed in the presence of microglia. In astrocytes, the increase in miR-146b-5p levels led to a significant reduction in traf6 and irak1 expression, which are two critical signaling molecules known to enhance the pro-inflammatory activity of astrocytes. Together, these results suggest that miR-146b-5p is an MS-relevant microRNA that regulates astrocyte function in a manner that fosters OPC growth and morphological complexity. Furthermore, our results further demonstrate the need to consider the complex glial-glial interactions occurring within MS lesions and its overall influence on cellular and molecular mechanisms related to CNS injury and repair.
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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) | 0.001 | 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 | 0.000 |
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".