Inflammation-induced TRPV4 channels exacerbate blood–brain barrier dysfunction in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Blood-brain barrier (BBB) dysfunction and immune cell migration into the central nervous system (CNS) are pathogenic drivers of multiple sclerosis (MS). Ways to reinstate BBB function and subsequently limit neuroinflammation present promising strategies to restrict disease progression. However, to date, the molecular players directing BBB impairment in MS remain poorly understood. One suggested candidate to impact BBB function is the transient receptor potential vanilloid-type 4 ion channel (TRPV4), but its specific role in MS pathogenesis remains unclear. Here, we investigated the role of TRPV4 in BBB dysfunction in MS. MAIN TEXT: In human post-mortem MS brain tissue, we observed a region-specific increase in endothelial TRPV4 expression around mixed active/inactive lesions, which coincided with perivascular microglia enrichment in the same area. Using in vitro models, we identified that microglia-derived tumor necrosis factor-α (TNFα) induced brain endothelial TRPV4 expression. Also, we found that TRPV4 levels influenced brain endothelial barrier formation via expression of the brain endothelial tight junction molecule claudin-5. In contrast, during an inflammatory insult, TRPV4 promoted a pathological endothelial molecular signature, as evidenced by enhanced expression of inflammatory mediators and cell adhesion molecules. Moreover, TRPV4 activity mediated T cell extravasation across the brain endothelium. CONCLUSION: Collectively, our findings suggest a novel role for endothelial TRPV4 in MS, in which enhanced expression contributes to MS pathogenesis by driving BBB dysfunction and immune cell migration.
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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".