FGFR signaling and neddylation facilitate SARS-CoV-2 infection by modulating interferon induction and viral entry, respectively
Bibliographic record
Abstract
SARS-CoV-2 is the causative agent of COVID-19, and although vaccines have reduced disease severity, emerging variants remain a significant public health issue. Broadly effective therapeutics, particularly those targeting host pathways essential for coronavirus infection, are still needed. Here, we used a CRISPR knockout screen to identify druggable host factors required for SARS-CoV-2 infection. The screen revealed NAE1 and FGFR1 as key contributors to infection. Inhibitors, either FDA-approved or those in clinical trials, of these pathways reduced replication of both ancestral and contemporary viral variants. Mechanistic studies showed that FGFR1 promotes viral replication through downstream MEK/ERK signaling, while neddylation appears to support viral entry or infectivity rather than replication itself. In a murine model of severe COVID-19, inhibitors of NAE1 and FGFR1 significantly decreased viral load and lung pathology. These findings support the development of host-targeted antiviral strategies.
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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) | 0.003 | 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".