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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".