Distinct Modes of Activation of Phosphatidylinositol 3-Kinase in Response to Cyclic Adenosine 3′, 5′-Monophosphate or Insulin-Like Growth Factor I Play Different Roles in Regulation of Cyclin D1 and p27Kip1 in FRTL-5 Cells
Bibliographic record
Abstract
Bioactivities of IGFs in various cells are often potentiated in the presence of other hormones. In previous studies we showed that pretreatment of rat FRTL-5 thyroid cells with TSH or other cAMP-generating agents markedly potentiated DNA synthesis induced by IGF-I. Under these conditions we found that phosphatidylinositol (PI) 3-kinase was activated in response to either cAMP or IGF stimulus, and both activation modes were indispensable for the potentiation of DNA synthesis. The present studies were undertaken to elucidate how cAMP and/or IGF-I stimulus regulated the G1 cyclin-cyclin dependent kinase (CDK)-inhibitor system, and to determine the roles of PI 3-kinase activation by cAMP or IGF-I stimulus in this system. We found that cAMP pretreatment enhanced IGF-I-dependent increases in cyclin D1, due to synergistic increases in mRNA and elevation of translation rates. Furthermore, cAMP pretreatment enhanced IGF-I-induced protein degradation of the CDK inhibitor, p27(Kip1). These changes well explained an increase in cyclin E, leading to marked activation of G1 CDKs, followed by retinoblastoma protein phosphorylation. Our results using a PI 3-kinase inhibitor showed that cAMP-dependent PI 3-kinase activation plays an important role in the increase in cyclin D1 translation. In contrast, IGF-I-dependent PI 3-kinase activation was required for the increase in cyclin D1 mRNA levels and degradation of p27(Kip1). Together, the present study elucidates the role of cAMP and IGF-I in differentially activating PI 3-kinase as a mediator of multiple molecular events. These events converge in the regulation of cyclin D1 and p27(Kip1), leading to cAMP-dependent potentiation of IGF-I-dependent CDK activation and DNA synthesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".