Keratinocyte growth factor promotes preadipocyte proliferation via an autocrine mechanism
Bibliographic record
Abstract
Keratinocyte growth factor (KGF; also known as FGF-7) is a well-characterized paracrine growth factor for tissue growth and regeneration. However, its role in adipose tissue, which is known to undergo tremendous expansion in obesity, is virtually unknown. Given that we previously identified KGF as one of the up-regulated growth factors in adipose tissue of an early-life programmed rat model of visceral obesity, the present study was undertaken to examine the hypothesis that KGF promotes adipogenesis. Using 3T3-L1 and rat primary preadipocytes as in vitro model systems, we demonstrated that (1) KGF stimulated preadipocyte proliferation in a concentration-dependent manner with a maximal effect at 2.5 ng/ml (approximately 2-fold increase); (2) KGF mRNA was highly expressed in rat adipocytes and preadipocytes as well as 3T3-L1 cells; (3) treatment of preadipocytes with a neutralizing antibody against KGF and siRNA-mediated knockdown of KGF led to a 50% reduction in their proliferative capacity; (4) KGF activated the protein kinase Akt, and the PI3 kinase inhibitor LY294002 blocked KGF stimulation of preadipocyte proliferation; and (5) KGF did not promote differentiation of preadipocytes to mature adipocytes. Together, these results reveal adipocytes and their precursor cells as novel sites of KGF production. Importantly, they also demonstrate that KGF promotes preadipocyte proliferation by an autocrine mechanism that involves activation of the PI3K/Akt signaling pathway. Aberrant KGF expression may have consequences not only for normal adipose tissue growth but also for the pathogenesis of obesity.
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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) | 0.001 | 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".