SARS-Cov-2 Replication in a Blood–Brain Barrier Model Established with Human Brain Microvascular Endothelial Cells Induces Permeability and Disables ACE2-Dependent Regulation of Bradykinin B1 Receptor
Bibliographic record
Abstract
Endothelial dysfunction plays a central role in COVID-19 pathogenesis, by affecting vascular homeostasis and worsening thromboinflammation. This imbalance may contribute to blood-brain barrier (BBB) disruption, which has been reported in long COVID-19 patients with neurological sequelae. The kallikrein-kinin system (KKS) generates bradykinin (BK), a proinflammatory peptide that induces microvascular leakage via B2R. Under inflammatory conditions, BK is converted to Des-Arg-BK (DABK), which activates B1R, a receptor upregulated in inflamed tissues. DABK is degraded by ACE2, the main SARS-CoV-2 receptor; thus, viral binding and ACE2 downregulation may lead to DABK/B1R imbalance. Here, we investigated these interactions using human brain microvascular endothelial cells (HBMECs), as a model of the BBB. Since endothelial cell lines express low levels of ACE2, HBMECs were modified with an ACE2-carrying pseudovirus. SARS-CoV-2 replication was confirmed by RNA, protein expression, and infectious particles release. Infection upregulated cytokines and endothelial permeability, enhancing viral and leukocyte transmigration. Additionally, viral replication impaired ACE2 function in HBMECs, amplifying the response to DABK, increasing nitric oxide (NO) production, and further disrupting endothelial integrity. Our findings reveal a mechanism by which SARS-CoV-2 impacts the BBB and highlights the ACE2/KKS/B1R axis as a potential contributor to long COVID-19 neurological symptoms.
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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".