Lopinavir Up-Regulates Expression of the Antiviral Protein Ribonuclease L in Human Papillomavirus-Positive Cervical Carcinoma Cells
Bibliographic record
Abstract
BACKGROUND: We have previously shown that the HIV protease inhibitor lopinavir has selective toxicity against human papillomavirus (HPV)-positive cervical carcinoma cells via an unknown mechanism. METHODS: SiHa cervical carcinoma cells were stably transfected with the proteasome sensor vector pZsProSensor-1 to confirm lopinavir inhibits the proteasome in these cells. The Panorama Xpress profiler 725 antibody array was then used to analyse specific changes in protein expression in lopinavir-treated versus control untreated SiHa cells followed by PCR and western blotting. Colorimetric growth assays of lopinavir-treated E6/E7 immortalised versus control human keratinocytes were performed. Targeted small interfering RNA gene silencing followed by growth assay comparison of lopinavir-treated/untreated SiHa cells was also used. RESULTS: Lopinavir induced an increase in the fluorescence of pZsProSensor-1 transfected SiHa cells, indicative of proteasomal inhibition. Ribonuclease L (RNASEL) protein was shown to be up-regulated in lopinavir-treated SiHa cells, which was confirmed by PCR and western blot. Targeted silencing of RNASEL reduced the sensitivity of SiHa cells to lopinavir. Selective toxicity against E6/E7 immortalised keratinocytes versus control cells was also seen with lopinavir and was associated with up-regulated RNASEL expression. CONCLUSIONS: These data are consistent with the toxicity of lopinavir against HPV-positive cervical carcinoma cells being related to its ability to block viral proteasome activation and induce an up-regulation of the antiviral protein RNASEL. This is supported by the drug's selective toxicity and up-regulation of RNASEL in E6/E7 immortalised keratinocytes combined with the increased resistance to lopinavir observed in SiHa cells following silencing of RNASEL gene expression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".