PIM1 Protein Kinase regulates PRAS40 phosphorylation and mTOR activity in FDCP1 cells
Bibliographic record
Abstract
PIM1 is a serine/threonine kinase that has diverse biological roles in cell survival, proliferation and differentiation. PIM1 has been implicated in early transformation and tumor progression in haematopoietic malignancies and prostate carcinomas. The ability of PIM1 to regulate these processes is thought to be in part secondary to its activity in stimulating 4EBP1 phosphorylation and enhancement of protein synthesis. Because 4EBP1 is an mTOR substrate, we have investigated how PIM1 might regulate this latter enzyme. We have examined the ability of PIM1 to modulate PRAS40, a protein known to negatively regulate mTOR activity in FDCP1 cells. Upon phosphorylation, PRAS40 dissociates from the mTOR complex and increases mTOR kinase activity. We find that enforced overexpression of PIM1 increases PRAS40 phosphorylation at Thr(246), an AKT phosphorylation site, whether grown in complete media or deprived of IL-3 and serum. The increase in PRAS40 phosphorylation was independent of AKT activation and not inhibited by wortmannin. In vitro kinase assays indicate that the PIM1 protein kinase is capable of directly phosphorylating Thr(246) in PRAS40. PIM1 protein kinase overexpression reduced the association of PRAS40 with mTOR, and increased the mTOR directed phosphorylation of 4EBP1 and p70S6Kinase. Treatment of FDCP1 cells transfected with PIM1 (FD/mpim44) with small molecule inhibitors of PIM1 kinase activity reduced both PRAS40 and 4EBP1 phosphorylation. These results suggest that PIM1 regulates mTOR activity through phosphorylation of PRAS40. Thus, increases in mTOR activity mediated by the PIM protein kinase may have the potential to control cell growth.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".