Human papillomavirus E7 oncoprotein targets RNF168 to hijack the host DNA damage response
Bibliographic record
Abstract
High-risk human papillomaviruses (HR-HPVs) promote cervical cancer as well as a subset of anogenital and head and neck cancers. Due to their limited coding capacity, HPVs hijack the host cell's DNA replication and repair machineries to replicate their own genomes. How this host-pathogen interaction contributes to genomic instability is unknown. Here, we report that HPV-infected cancer cells express high levels of RNF168, an E3 ubiquitin ligase that is critical for proper DNA repair following DNA double-strand breaks, and accumulate high numbers of 53BP1 nuclear bodies, a marker of genomic instability induced by replication stress. We describe a mechanism by which HPV E7 subverts the function of RNF168 at DNA double-strand breaks, providing a rationale for increased homology-directed recombination in E6/E7-expressing cervical cancer cells. By targeting a new regulatory domain of RNF168, E7 binds directly to the E3 ligase without affecting its enzymatic activity. As RNF168 knockdown impairs viral genome amplification in differentiated keratinocytes, we propose that E7 hijacks the E3 ligase to promote the viral replicative cycle. This study reveals a mechanism by which tumor viruses reshape the cellular response to DNA damage by manipulating RNF168-dependent ubiquitin signaling. Importantly, our findings reveal a pathway by which HPV may promote the genomic instability that drives oncogenesis.
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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) | 0.002 | 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".