Identification of a SARS-CoV-2 host metalloproteinase-dependent entry pathway differentially used by SARS-CoV-2 and variants of concern Alpha, Delta, and Omicron
Bibliographic record
Abstract
ABSTRACT To infect cells, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) binds to angiotensin converting enzyme 2 (ACE2) via its spike glycoprotein (S), delivering its genome upon S-mediated membrane fusion. SARS-CoV-2 uses two distinct entry pathways: 1) a surface, serine protease-dependent or 2) an endosomal, cysteine protease-dependent pathway. In investigating serine protease-independent cell-cell fusion, we found that the matrix metalloproteinases (MMPs), MMP2/9, can activate SARS-CoV-2 S fusion activity, but not that of SARS-CoV-1. Importantly, metalloproteinase activation of SARS-CoV-2 S represents a third entry pathway in cells expressing high MMP levels. This route of entry required cleavage at the S1/S2 junction in viral producer cells and differential processing of variants of concern S dictated its usage. In addition, metalloproteinase inhibitors reduced replicative Alpha infection and abrogated syncytia formation. Finally, we found that the Omicron S exhibit reduced metalloproteinase-dependent fusion and viral entry. Taken together, we identified a MMP2/9-dependent mode of activation of SARS-CoV-2 S. As MMP2/9 are released during inflammation and severe COVID-19, they may play important roles in SARS-CoV-2 S-mediated cytopathic effects, tropism, and disease outcome.
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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".