Anti-EBOV GP IgGs Lacking α1-3-Galactose and Neu5Gc Prolong Survival and Decrease Blood Viral Load in EBOV-Infected Guinea Pigs
Bibliographic record
Abstract
Polyclonal xenogenic IgGs, although having been used in the prevention and cure of severe infectious diseases, are highly immunogenic, which may restrict their usage in new applications such as Ebola hemorrhagic fever. IgG glycans display powerful xenogeneic antigens in humans, for example α1-3 Galactose and the glycolyl form of neuraminic acid Neu5Gc, and IgGs deprived of these key sugar epitopes may represent an advantage for passive immunotherapy. In this paper, we explored whether low immunogenicity IgGs had a protective effect on a guinea pig model of Ebola virus (EBOV) infection. For this purpose, a double knock-out pig lacking α1-3 Galactose and Neu5Gc was immunized against virus-like particles displaying surface EBOV glycoprotein GP. Following purification from serum, hyper-immune polyclonal IgGs were obtained, exhibiting an anti-EBOV GP titer of 1:100,000 and a virus neutralizing titer of 1:100. Guinea pigs were injected intramuscularly with purified IgGs on day 0 and day 3 post-EBOV infection. Compared to control animals treated with IgGs from non-immunized double KO pigs, the anti-EBOV IgGs-treated animals exhibited a significantly prolonged survival and a decreased virus load in blood on day 3. The data obtained indicated that IgGs lacking α1-3 Galactose and Neu5Gc, two highly immunogenic epitopes in humans, have a protective effect upon EBOV infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".