Adipocyte Enhancer‐Binding Protein 1 Modulates Adiposity and Energy Homeostasis
Bibliographic record
Abstract
OBJECTIVE: To determine whether adipocyte enhancer binding protein (AEBP) 1, a transcriptional repressor that is down-regulated during adipogenesis, functions as a critical regulator of adipose tissue homeostasis through modulation of phosphatase and tensin homolog deleted on chromosome ten (PTEN) tumor suppressor activity and mitogen-activated protein kinase (MAPK) activation. RESEARCH METHODS AND PROCEDURES: We examined whether AEBP1 physically interacts with PTEN in 3T3-L1 cells by coimmunoprecipitation analysis. We generated AEBP1-null mice and examined the physiological role of AEBP1 as a key modulator of in vivo adiposity. Using adipose tissue from wild-type and AEBP1-null animals, we examined whether AEBP1 affects PTEN protein level. RESULTS: AEBP1 interacts with PTEN, and deficiency of AEBP1 increases adipose tissue PTEN mass. AEBP1-null mice have reduced adipose tissue mass and enhanced apoptosis with suppressed survival signal. Primary pre-adipocytes from AEBP1-null adipose tissues exhibit lower basal MAPK activity with defective proliferative potential. AEBP1-null mice are also resistant to diet-induced obesity, suggesting a regulatory role for AEBP1 in energy homeostasis. DISCUSSION: Our results suggest that AEBP1 negatively regulates adipose tissue PTEN levels, in conjunction with its role in proliferation and differentiation of pre-adipocytes, as a key functional role in modulation of in vivo adiposity.
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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) | 0.003 | 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".