Knockdown of endothelial <i>Serpine1</i> improves stroke recovery by attenuating peri-infarct blood flow and blood brain barrier disruption
Bibliographic record
Abstract
Focal stroke leads to complex changes in the cerebral microcirculation in surviving brain tissues that strongly influence functional recovery. The gene Serpine1 and its protein product Plasminogen Activator-Inhibitor-1 (PAI-1), are highly upregulated in endothelial cells after stroke, are known to inhibit clot breakdown and are an established biomarker of cardiovascular disease in humans. Therefore, we hypothesized that inhibiting this pathway specifically within brain endothelial cells could be beneficial for stroke recovery by promoting fibrinolysis and cerebral blood flow (CBF). Using longitudinal in vivo imaging, we first show in wild-type mice that focal ischaemic stroke leads to a transient reduction in peri-infarct CBF at 6 h (~41%), which is then followed by hyperperfusion at 3 days (~29%). Contrary to expectation, viral knockdown of Serpine1 in brain endothelial cells led to a persistent reduction in peri-infarct capillary width (~20-37%) and red blood cell velocity (~36-50%) over this time. Consistent with this effect on CBF, topical application of PAI-1 increased capillary diameter and flow. Of note, lowered peri-infarct CBF in Serpine1 knockdown mice appeared to play a protective role in stroke recovery since it attenuated deleterious blood-brain barrier disruption and pro-inflammatory gene expression. In agreement with this, Serpine1 knockdown mice displayed enhanced recovery of sensory evoked cortical responses, as well as improved cognitive and sensorimotor function relative to wild-type mice. These findings suggest that endothelial Serpine1/PAI-1 signalling can influence vessel tone and highlight its therapeutic potential in promoting stroke recovery. Further, our data challenge the assumption that increased CBF after the hyper-acute phase of stroke is better for recovery and suggest that carefully tuning flow, rather than maximizing it, may be an optimal strategy.
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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) | 0.003 | 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".