Regulated Intramembrane Proteolysis of ACE2: A Potential Mechanism Contributing to COVID-19 Pathogenesis?
Bibliographic record
Abstract
Since being identified as a key receptor for SARS-CoV-2, Angiotensin converting enzyme 2 (ACE2) has been studied as one of the potential targets for the development of preventative and/or treatment options. Tissue expression of ACE2 and the amino acids interacting with the spike protein of SARS-CoV-2 have been mapped. Furthermore, the recombinant soluble extracellular domain of ACE2 is already in phase 2 trials as a treatment for SARS-CoV-2 infection. Most studies have continued to focus on the ACE2 extracellular domain, which is known to play key roles in the renin angiotensin system and in amino acid uptake. However, few also found ACE2 to have an immune-modulatory function and its intracellular tail may be one of the signaling molecules in regulating cellular activation. The implication of its immune-modulatory role in preventing the cytokine-storm, observed in severe COVID-19 disease outcomes requires further investigation. This review focuses on the regulated proteolytic cleavage of ACE2 upon binding to inducer(s), such as the spike protein of SARS-CoV, the potential of cleaved ACE2 intracellular subdomain in regulating cellular function, and the ACE2's immune-modulatory function. This knowledge is critical for targeting ACE2 levels for developing prophylactic treatment or preventative measures in SARS-CoV infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".