Effects of Ebola Virus Glycoproteins on Endothelial Cell Activation and Barrier Function
Bibliographic record
Abstract
Ebola virus causes severe hemorrhagic fever with high mortality rates in humans and nonhuman primates. Vascular instability and dysregulation are disease-decisive symptoms during severe infection. While the transmembrane glycoprotein GP(1,2) has been shown to cause endothelial cell destruction, the role of the soluble glycoproteins in pathogenesis is largely unknown; however, they are hypothesized to be of biological relevance in terms of target cell activation and/or increase of endothelial permeability. Here we show that virus-like particles (VLPs) consisting of the Ebola virus matrix protein VP40 and GP(1,2) were able to activate endothelial cells and induce a decrease in barrier function as determined by impedance spectroscopy and hydraulic conductivity measurements. In contrast, the soluble glycoproteins sGP and delta-peptide did not activate endothelial cells or change the endothelial barrier function. The VLP-induced decrease in barrier function was further enhanced by the cytokine tumor necrosis factor alpha (TNF-alpha), which is known to induce a long-lasting decrease in endothelial cell barrier function and is hypothesized to play a key role in Ebola virus pathogenesis. Surprisingly, sGP, but not delta-peptide, induced a recovery of endothelial barrier function following treatment with TNF-alpha. Our results demonstrate that Ebola virus GP(1,2) in its particle-associated form mediates endothelial cell activation and a decrease in endothelial cell barrier function. Furthermore, sGP, the major soluble glycoprotein of Ebola virus, seems to possess an anti-inflammatory role by protecting the endothelial cell barrier function.
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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".