Superinduction of P‐glycoprotein messenger RNA in vivo in the presence of transcriptional inhibitors
Bibliographic record
Abstract
P-Glycoprotein (P-gp) is comprised of a small family of plasma membrane proteins, and its presence in high amounts often correlates with multidrug resistance in cultured cells. Dramatically increased levels of a single member of P-gp mRNA (pgp2) have been observed in experimental liver carcinogenesis models, during liver regeneration, upon culturing of hepatocytes and in the uterus of pregnant animals. In all cases, the increase in mRNA level appears to be the result of an increase in mRNA half-life (stability). Previously, we have used transcriptional inhibitors alpha-amanitin and actinomycin D to measure P-gp mRNA half-life in normal liver and in liver tumors. We showed that the level of all three P-gp mRNAs decreased with time in the presence of transcriptional inhibitors, yielding measured half-lives of less than 2 h in liver but greater than 12 h in liver tumors. This observation raised the possibility that regulation of P-gp mRNA stability plays a role in liver carcinogenesis. In the present study, we measured P-gp mRNA half-life in other normal tissues to determine if a short P-gp mRNA half-life is unique to the liver. Our study reveals that in contrast to liver, measured P-gp mRNA half-lives in most tissues examined are greater than 12 h. Moreover, we observed an unexpected, marked increase in the level of pgp2 mRNA with time after injection of transcriptional inhibitors. This can only be explained if the transcriptional inhibitors directly or indirectly inhibit the normally high degradation rate of pgp2 mRNA, resulting in the superinduction of this mRNA. These findings have implications for our understanding of the regulation of P-gp gene expression and drug resistance in vivo.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".