Proteolytic processing of the Marburg virus glycoprotein depends on Sec61β and is required for cell entry
Bibliographic record
Abstract
Abstract Ebola and Marburg virus (EBOV, MARV) cause severe disease and therapeutic options are urgently needed. The Sec61 translocon facilitates ER import of viral glycoproteins (GPs) and may represent a therapeutic target. Here, we report that the Sec61 subunit Sec61β, although dispensable for GP expression, is required for proteolytic cleavage of MARV- but not EBOV-GP and that an intact furin motif is essential for robust cell entry of Marburg- but not Ebolaviruses. Further, MARV- but not EBOV-GP was cleaved by the furin-related enzyme SKI-1, for which a cleavage motif was identified in silico, and cleavage by SKI-1 was impaired in SEC61B -KO cells. In addition, Sec61β was required for normal N-glycosylation of MARV-GP and mutation of a sequon (N563D) abrogated cleavage. Finally, the absence of Sec61β modestly, and blockade of Sec61 via apratoxin S4 markedly, inhibited EBOV and MARV infection. These results reveal a differential protease dependence of MARV and EBOV and identify Sec61 as a potential therapeutic target. Author summary The filoviruses Ebola virus (EBOV) and Marburg virus (MARV) spread from animals to humans and can cause deadly outbreaks. These viruses rely on a surface glycoprotein (GP) for infection, which is processed by the enzyme furin in infected human cells. Cleavage of EBOV-GP was thought to be non-essential for infection. However, using lab models for filovirus entry into cells, we discovered that MARV, unlike EBOV, needs this cleavage step to infect cells efficiently. We also found that the host cell protein Sec61β is necessary for proteolytic processing and glycosylation of MARV-GP but not EBOV-GP. In addition, we showed that another cellular enzyme, SKI-1, can process MARV- but not EBOV-GP. Finally, we found that removing Sec61β or blocking Sec61 activity reduced infection by both viruses. These findings show key differences in how the two viruses interact with host cells and suggest that targeting Sec61 could be a promising new strategy to fight Ebola and Marburg virus 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) | 0.000 | 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 | 0.001 |
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".