BMP and activin membrane-bound inhibitor (BAMBI) inhibits the adipogenesis of porcine preadipocytes through Wnt/β-catenin signaling pathway
Bibliographic record
Abstract
The process of differentiation from preadipocytes to adipocytes contributes to adipose tissue expansion in obesity. Blocking adipogenesis may be conducive to the etiology of obesity-related diseases. BMP and activin membrane-bound inhibitor (BAMBI) is a transmembrane protein, which was identified as a target of β-catenin in colorectal and hepatocellular tumor cells. However, whether BAMBI affects adipogenesis by Wnt/β-catenin signaling remains to be explored. In this study, we distinguish BAMBI as an inhibitor of preadipocytes differentiation. We found that BAMBI was downregulated during preadipocytes differentiation. Knockdown of BAMBI increased adipogenesis and blocked Wnt/β-catenin signaling by repressing β-catenin accumulation. In BAMBI overexpression cells, lipid accumulation was reduced by promoting nuclear translocation of β-catenin. Lithium chloride (LiCl) is an activator of Wnt/β-catenin signaling, which is an inhibitor of glycogen synthetase kinase-3 (GSK-3), maintaining the stability of β-catenin in cytosolic. We showed BAMBI strengthened the anti-adipogenic effects of LiCl. In addition, the results indicated that BAMBI was upregulated by β-catenin. These observations illuminated that BAMBI inhibits adipogenesis by a feedback loop (BAMBI→β-catenin nuclear translocation→BAMBI), which forms with Wnt/β-catenin 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 | 0.001 |
| 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".