SARS-CoV-2 spike protein induces endothelial inflammation via ACE2 independently of viral replication
Bibliographic record
Abstract
COVID-19, caused by SARS-CoV-2, is a respiratory disease associated with inflammation and endotheliitis. Mechanisms underling inflammatory processes are unclear, but angiotensin converting enzyme 2 (ACE2), the receptor which binds the spike protein of SARS-CoV-2 may be important. Here we investigated whether spike protein binding to ACE2 induces inflammation in endothelial cells and determined the role of ACE2 in this process. Human endothelial cells were exposed to SARS-CoV-2 spike protein, S1 subunit (rS1p) and pro-inflammatory signaling and inflammatory mediators assessed. ACE2 was modulated pharmacologically and by siRNA. Endothelial cells were also exposed to SARS-CoV-2. rSP1 increased production of IL-6, MCP-1, ICAM-1 and PAI-1, and induced NFkB activation via ACE2 in endothelial cells. rS1p increased microparticle formation, a functional marker of endothelial injury. ACE2 interacting proteins involved in inflammation and RNA biology were identified in rS1p-treated cells. Neither ACE2 expression nor ACE2 enzymatic function were affected by rSP1. Endothelial cells exposed to SARS-CoV-2 virus did not exhibit viral replication. We demonstrate that rSP1 induces endothelial inflammation via ACE2 through processes that are independent of ACE2 enzymatic activity and viral replication. We define a novel role for ACE2 in COVID-19- associated endotheliitis.
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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".