Vaccinia Virus K1L and C7L Inhibit Antiviral Activities Induced by Type I Interferons
Bibliographic record
Abstract
Cellular tropism of vaccinia virus (VACV) is regulated by host range genes, including K1L, C7L, and E3L. While E3L is known to support viral replication by antagonizing interferon (IFN) effectors, including PKR, the exact functions of K1L and C7L are unclear. Here, we show that K1L and C7L can also inhibit antiviral effectors induced by type I IFN. In human Huh7 and MCF-7 cells, a VACV mutant lacking both K1L and C7L (vK1L-C7L-) replicated as efficiently as wild-type (WT) VACV, even in the presence of IFN. However, pretreating the cells with type I IFN, while having very little effect on WT VACV, blocked the replication of vK1L-C7L- at the step of intermediate viral gene translation. Restoring either K1L or C7L to vK1L(-)C7L(-) fully restored the IFN resistance phenotype. The deletion of K1L and C7L from VACV did not affect the ability of the virus to inhibit IFN signaling or its ability to inhibit the phosphorylation of PKR and the alpha subunit of eukaryotic initiation factor 2, indicating that K1L and C7L function by antagonizing an IFN effector(s) but with a mechanism that is different from those of IFN antagonists previously identified for VACV. Mutations of K1L that inactivate the host range function also rendered K1L unable to antagonize IFN, suggesting that K1L supports VACV replication in mammalian cells by antagonizing the same antiviral factor(s) that is induced by IFN in Huh7 cells.
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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".