Curcumin represses mouse 3T3-L1 cell adipogenic differentiation via inhibiting miR-17-5p and stimulating the Wnt signalling pathway effector Tcf7l2
Bibliographic record
Abstract
Understanding mechanisms underlying adipogenic differentiation may lead to the discovery of novel therapeutic targets for obesity. Wnt signalling pathway activation leads to repressed adipogenic differentiation while certain microRNAs may regulate pre-adipocyte proliferation and differentiation. We show here that in mouse white adipose tissue, miR-17-5p level is elevated after high fat diet consumption. miR-17-5p upregulates adipogenic differentiation, as its over-expression increased while its inhibition repressed 3T3-L1 differentiation. The Tcf7l2 gene encodes a key Wnt signalling pathway effector, and its human homologue TCF7L2 is a highly regarded diabetes risk gene. We found that Tcf7l2 is an miR-17-5p target and confirmed the repressive effect of Tcf7l2 on 3T3-L1 adipogenic differentiation. The natural plant polyphenol compound curcumin possesses the body weight lowering effect. We observed that curcumin attenuated miR-17-5p expression and stimulated Tcf7l2 expression in 3T3-L1 cells. These, along with the elevation of miR-17-5p expression in mouse epididymal fat tissue in response to high fat diet consumption, allowed us to suggest that miR-17-5p is among central switches of adipogenic differentiation. It activates adipogenesis via repressing the Wnt signalling pathway effector Tcf7l2, and its own expression is likely nutritionally regulated in health and disease.
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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) | 0.002 | 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".