MicroRNA‐155 negatively affects blood–brain barrier function during neuroinflammation
Bibliographic record
Abstract
ABSTRACT Blood–brain barrier (BBB) dysfunction is a hallmark of neurological conditions such as multiple sclerosis (MS) and stroke. However, the molecular mechanisms underlying neurovascular dysfunction during BBB breakdown remain elusive. MicroRNAs (miRNAs) have recently emerged as key regulators of pathogenic responses, although their role in central nervous system (CNS) microvascular disorders is largely unknown. We have identified miR‐155 as a critical miRNA in neuroinflammation at the BBB. miR‐155 is expressed at the neurovascular unit of individuals with MS and of mice with experimental autoimmune encephalomyelitis (EAE). In mice, loss of miR‐155 reduced CNS extravasation of systemic tracers, both in EAE and in an acute systemic inflammation model induced by lipopolysaccharide. In cultured human brain endothelium, miR‐155 was strongly and rapidly upregulated by inflammatory cytokines. miR‐155 up‐regulation mimicked cytokine‐induced alterations in junctional organization and permeability, whereas inhibition of endogenous miR‐155 partially prevented a cytokine‐induced increase in permeability. Furthermore, miR‐155 modulated brain endothelial barrier function by targeting not only cell–cell complex molecules such as annexin‐2 and claudin‐1, but also focal adhesion components such as DOCK‐1 and syntenin‐1. We propose that brain endothelial miR‐155 is a negative regulator of BBB function that may constitute a novel therapeutic target for CNS neuroinflammatory disorders.—Lopez‐Ramirez, M. A., Wu, D., Pryce, G., Simpson, J. E., Reijerkerk, A., King‐Robson, J., Kay, O, de Vries, H. E., Hirst, M. C., Sharrack, B., Baker D., Male, D. K., Michael, G. J., Romero, I. A. MicroRNA‐155 negatively affects blood–brain barrier function during neuroinflammation. FASEB J . 28, 2551–2565 (2014). www.fasebj.org
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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".