Regulation of Adipocyte Differentiation by Distinct Subcellular Pools of Protein Kinase B (PKB/Akt)
Bibliographic record
Abstract
The phosphatidylinositol 3-kinase (PI3K)-protein kinase B (PKB)/Akt-PTEN signal transduction pathway orchestrates a variety of fundamental cell processes and its deregulation is implicated in many human diseases. Although the importance of this pathway to many cellular functions is well established, the mechanisms by which it achieves context-specific physiological outcomes in response to a variety of stimuli, using a relatively limited pool of effectors, remain largely unknown. Spatial restriction of signaling events is one means by which cells coordinate specific responses using common molecules. To investigate the subcellular location-specific roles of the major PI3K effector PKB/Akt in various cell processes, we have developed a novel experimental system employing cellular compartment-directed PKB/Akt pseudosubstrate inhibitors. Subcellular location-restricted PKB/Akt inhibition in the 3T3L1 adipocyte differentiation model revealed that nuclear and plasma membrane, but not cytoplasmic, PKB/Akt activity is required for terminal adipocyte differentiation. Nuclear and plasma membrane pools of PKB/Akt were found to contribute to distinct stages of adipocyte differentiation, revealing that PKB/Akt activity impacts multiple points of this program. Our work establishes the use of localized pseudosubstrate PKB/Akt inhibitors as an effective method for the dissection of PKB/Akt signaling.
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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".