SKI-1/S1P Facilitates SARS-CoV-2 Spike Induced Cell-to-Cell Fusion via Activation of SREBP-2 and Metalloproteases, Whereas PCSK9 Enhances the Degradation of ACE2
Bibliographic record
Abstract
Proprotein convertases activate various envelope glycoproteins and participate in cellular entry of many viruses. We recently showed that the convertase furin is critical for the infectivity of SARS-CoV-2, which requires cleavage of its spike protein (S) at two sites: S1/S2 and S2'. This study investigates the implication of the two cholesterol-regulating convertases SKI-1 and PCSK9 in SARS-CoV-2 entry. The assays used were cell-to-cell fusion in HeLa cells and pseudoparticle entry into Calu-3 cells. SKI-1 increased cell-to-cell fusion by enhancing the activation of SREBP-2, whereas PCSK9 reduced cell-to-cell fusion by promoting the cellular degradation of ACE2. SKI-1 activity led to enhanced S2' formation, which was attributed to increased metalloprotease activity as a response to enhanced cholesterol levels via activated SREBP-2. However, high metalloprotease activity resulted in the shedding of S2' into a new C-terminal fragment (S2″), leading to reduced cell-to-cell fusion. Indeed, S-mutants that increase S2″ formation abolished S2' and cell-to-cell fusion, as well as pseudoparticle entry, indicating that the formation of S2″ prevents SARS-CoV-2 cell-to-cell fusion and entry. We next demonstrated that PCSK9 enhanced the cellular degradation of ACE2, thereby reducing cell-to-cell fusion. However, different from the LDLR, a canonical target of PCSK9, the C-terminal CHRD domain of PCSK9 is dispensable for the PCSK9-induced degradation of ACE2. Molecular modeling suggested the binding of ACE2 to the Pro/Catalytic domains of mature PCSK9. Thus, both cholesterol-regulating convertases SKI-1 and PCSK9 can modulate SARS-CoV-2 entry via two independent mechanisms.
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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 | 0.001 |
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".