Endothelial tight junctions and cell-matrix adhesions reciprocally control blood-brain barrier integrity
Bibliographic record
Abstract
Abstract Brain endothelial cells (ECs) rely on mechanical cues to provide a physical barrier that protects the brain. Yet how ECs integrate forces to establish and maintain the blood-brain barrier (BBB) remains poorly understood. Here, we show that the two main endothelial force-bearing systems, tight junctions and cell-matrix adhesions, reciprocally control BBB integrity. Using a combination of super-resolution imaging and biophysical techniques, we reveal increasing mechanical loads on cell-cell junctions vs. cell-matrix adhesions in human stem cell-derived ECs during BBB maturation. This force redistribution is enabled by cytoskeletal remodeling, a compacted pattern of the tight junction protein claudin-5, and the emergence of specialised perinuclear cell-matrix adhesions. Mechanistically, we find an inverse relationship between claudin-5 levels and the expression of key cell-matrix adhesion proteins zyxin and vinculin in vitro and in mice. Finally, we demonstrate that this mechanobiological signature associated with BBB maturation is reversed upon BBB dysfunction after seizures in mice and in human patients with temporal lobe epilepsy. Collectively, our findings establish a novel interplay between mechanoresponsive elements in brain ECs, with implications for BBB stabilisation therapy in epilepsy.
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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 | 0.000 |
| 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".