Naturally Occurring Variations Modulate the Activity of the HPV33 Early Promoter and its Affinity for the E2 Transcription Factor
Bibliographic record
Abstract
The human papillomavirus (HPV) Long Control Region (LCR) encompasses the early promoter (EP) that drives transcription of the E6 and E7 oncogenes in keratinocytes and HPV-associated cancers. In this study, the transcriptional activities of the HPV33 EP from the prototype LCR and from eight variants representative of the worldwide diversity of the virus were examined in primary human keratinocytes (PHK) and in the HeLa cervical carcinoma cell line by luciferase reporter-gene assays. Remarkably, the two variations with the greatest effect on the EP in PHK were C7732G and a 79-bp deletion that were associated with high-grade cervical lesions and persistent infections, respectively, in epidemiological studies. In contrast, the three variations most active in HeLa cells were C7537A, A7874C and A7879G. A7874C, which lies within an E2-binding sequence, is also shown to increase the activity and binding of E2 at this site. Collectively, these results indicate that naturally-occurring variations affect the HPV33 EP differentially in PHK than in cancer cells and, furthermore, that they can also alter its regulation by E2. These findings provide a molecular basis for rationalizing the results of previous epidemiological studies and for understanding the contribution of LCR polymorphisms to the oncogenicity and persistence of HPV33 infections.
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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".