Human type I interferons protect Vero E6 and ARPE-19 cells against West Nile virus and are neutralized by pathogenic autoantibodies
Bibliographic record
Abstract
Auto-antibodies (auto-Abs) that neutralize type I interferons (IFNs) have been implicated in severe viral infections, including ~ 40% of cases of West Nile virus (WNV) neuroinvasive disease (WNND). Developing robust in vitro models to evaluate the protective effects of type I IFNs against viral infection, as well as the disruptive effects of auto-Abs, is essential for understanding disease pathogenesis and identifying patients at risk. In this study, we used Vero E6 and ARPE-19 cell lines to investigate the ability of type I (IFN-α, IFN-β, IFN-ω), type II (IFN-γ), and type III (IFN-λ1) IFNs to restrict WNV infection. Our results demonstrate that IFN-α, IFN-β, and IFN-ω effectively protect ARPE-19 cells from WNV infection, with IFN-β exhibiting the strongest antiviral effect. In contrast, Vero E6 cells required higher concentrations of IFN-ω to achieve comparable protection. Neither IFN-γ nor IFN-λ1 conferred protection in either cell line. We further screened serum samples from WNV-infected patients for auto-Abs neutralizing type I IFNs. Our findings confirm that the ARPE-19-based assay is consistent with other established methods for detecting neutralizing auto-Abs against type I IFNs. This simple and reliable assay offers a valuable tool for assessing the antiviral effects of type I IFNs and the neutralizing activity of auto-Abs in both research and clinical settings. Future studies should aim to validate the clinical utility of the ARPE-19-WNV infection model on a larger scale.
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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) | 0.001 | 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) | 0.002 | 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".