GIP increases human adipocyte LPL expression through CREB and TORC2-mediated trans-activation of the LPL gene
Bibliographic record
Abstract
GIP (glucose-dependent insulinotropic polypeptide) is a gastrointestinal hormone that regulates pancreatic islet function. Additionally, emerging evidence suggests an important physiological role for GIP in the regulation of adipocyte metabolism. In previous studies on the lipogenic effects of GIP, it was shown to increase adipocyte lipoprotein lipase (LPL) activity in both differentiated 3T3-L1 cells and human adipocytes through a pathway involving activation of protein kinase B (PKB)/Akt. In the current study, we examined the effects of GIP on LPL gene expression. GIP in the presence of insulin increased LPL gene expression in human adipocytes and LPL promoter activity in GIP receptor-expressing HEK-293 cells, and both effects were greatly reduced by the transcription inhibitor actinomycin D. Subsequent studies established that GIP increased phosphorylation of Serine 133 in cAMP-response element binding protein (CREB) and the nuclear localization of cAMP-responsive CREB coactivator 2 (TORC2) through a pathway involving phosphatidylinositol 3-kinase (PI3-K), PKB, and AMP-activated protein kinase (AMPK). However, in the presence of insulin, GIP failed to activate the cAMP/PKA pathway. Knockdown of CREB and TORC2 using RNA interference reduced LPL expression, supporting a functional regulatory role. GIP-induced phospho-CREB and TORC2 were shown to bind to a cAMP-response element (-II) site in the human LPL promoter and GIP increased protein-protein interactions of these two factors. The lipogenic effects of GIP in the presence of insulin are therefore at least partially mediated by upregulation of adipocyte LPL gene transcription through a pathway involving PI3-K/PKB/AMPK-dependent CREB/TORC2 activation.
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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) | 0.001 | 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) | 0.003 | 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".