Differential interferon-\(\alpha\) subtype induced immune signatures are associated with suppression of SARS-CoV-2 infection
Bibliographic record
Abstract
Type I interferons (IFN-I) exert pleiotropic biological effects during viral infections, balancing virus control versus immune-mediated pathologies, and have been successfully employed for the treatment of viral diseases. Humans express 12 IFN-alpha (\(\alpha\)) subtypes, which activate downstream signaling cascades and result in distinct patterns of immune responses and differential antiviral responses. Inborn errors in IFN-I immunity and the presence of anti-IFN autoantibodies account for very severe courses of COVID-19; therefore, early administration of IFN-I may be protective against life-threatening disease. Here we comprehensively analyzed the antiviral activity of all IFN\(\alpha\) subtypes against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to identify the underlying immune signatures and explore their therapeutic potential. Prophylaxis of primary human airway epithelial cells (hAEC) with different IFN\(\alpha\) subtypes during SARS-CoV-2 infection uncovered distinct functional classes with high, intermediate, and low antiviral IFNs. In particular, IFN\(\alpha\)5 showed superior antiviral activity against SARS-CoV-2 infection in vitro and in SARS-CoV-2–infected mice in vivo. Dose dependency studies further displayed additive effects upon coadministration with the broad antiviral drug remdesivir in cell culture. Transcriptomic analysis of IFN-treated hAEC revealed different transcriptional signatures, uncovering distinct, intersecting, and prototypical genes of individual IFN\(\alpha\) subtypes. Global proteomic analyses systematically assessed the abundance of specific antiviral key effector molecules which are involved in IFN-I signaling pathways, negative regulation of viral processes, and immune effector processes for the potent antiviral IFN\(\alpha\)5. Taken together, our data provide a systemic, multimodular definition of antiviral host responses mediated by defined IFN-I. This knowledge will support the development of novel therapeutic approaches against SARS-CoV-2.
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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".