CREBL2, interacting with CREB, induces adipogenesis in 3T3-L1 adipocytes
Bibliographic record
Abstract
The factors that influence preadipocyte determination remain poorly understood. In the present paper, we report that CREBL2 [CREB (cAMP-response-element-binding protein)-like 2], a novel bZIP_1 protein, is up-regulated during MDI-induced preadipocyte differentiation. During both overexpression and under physiological conditions, CREBL2 interacted and was entirely co-localized with CREB. Overexpression of CREBL2 was sufficient to promote adipogenesis via up-regulating the expression of PPARγ (peroxisome-proliferator-activated receptor γ) and C/EBPα (CCAAT/enhancer-binding protein α) and accelerate lipogenesis accompanied with increased GLUT (glucose transporter) 1 and GLUT4. CREBL2 knockdown restrained adipogenic conversion and lipogenesis. Additionally, depletion of CREB could completely block the effects of overexpressed CREBL2, whereas an increase in CREB could not drive adipogenesis in the absence of CREBL2, indicating that the roles for CREBL2 on adipogenesis were CREB-dependent. Furthermore, siCREBL2 [siRNA (short interfering RNA) against CREBL2] could down-regulate CREB transcriptional activity and suppress CREB phosphorylation. CREB knockdown decreased the CREBL2 protein levels and vice versa. Collectively, the results of the present study indicate that CREBL2 plays a critical role in adipogenesis and lipogenesis via interaction with CREB.
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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".