A New Ebola Virus Nonstructural Glycoprotein Expressed through RNA Editing
Bibliographic record
Abstract
Ebola virus (EBOV), an enveloped, single-stranded, negative-sense RNA virus, causes severe hemorrhagic fever in humans and nonhuman primates. The EBOV glycoprotein (GP) gene encodes the nonstructural soluble glycoprotein (sGP) but also produces the transmembrane glycoprotein (GP₁,₂) through transcriptional editing. A third GP gene product, a small soluble glycoprotein (ssGP), has long been postulated to be produced also as a result of transcriptional editing. To identify and characterize the expression of this new EBOV protein, we first analyzed the relative ratio of GP gene-derived transcripts produced during infection in vitro (in Vero E6 cells or Huh7 cells) and in vivo (in mice). The average percentages of transcripts encoding sGP, GP₁,₂, and ssGP were approximately 70, 25, and 5%, respectively, indicating that ssGP transcripts are indeed produced via transcriptional editing. N-terminal sequence similarity with sGP, the absence of distinguishing antibodies, and the abundance of sGP made it difficult to identify ssGP through conventional methodology. Optimized 2-dimensional (2D) gel electrophoresis analyses finally verified the expression and secretion of ssGP in tissue culture during EBOV infection. Biochemical analysis of recombinant ssGP characterized this protein as a disulfide-linked homodimer that was exclusively N glycosylated. In conclusion, we have identified and characterized a new EBOV nonstructural glycoprotein, which is expressed as a result of transcriptional editing of the GP gene. While ssGP appears to share similar structural properties with sGP, it does not appear to have the same anti-inflammatory function on endothelial cells as sGP.
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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) | 0.000 | 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".