Inhibition of Protein Farnesylation Arrests Adipogenesis and Affects PPAR<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="E1"><mml:mi>γ</mml:mi></mml:math>Expression and Activation in Differentiating Mesenchymal Stem Cells
Bibliographic record
Abstract
Protein farnesylation is required for the activation of multiple proteins involved in cell differentiation and function. In white adipose tissue protein, farnesylation has shown to be essential for the successful differentiation of preadipocytes into adipocytes. We hypothesize that protein farnesylation is required for PPARgamma2 expression and activation, and therefore for the differentiation of human mesenchymal stem cells (MSCs) into adipocytes. MSCs were plated and induced to differentiate into adipocytes for three weeks. Differentiating cells were treated with either an inhibitor of farnesylation (FTI-277) or vehicle alone. The effect of inhibition of farnesylation in differentiating adipocytes was determined by oil red O staining. Cell survival was quantified using MTS Formazan. Additionally, nuclear extracts were obtained and prelamin A, chaperon protein HDJ-2, PPARgamma, and SREBP-1 were determined by western blot. Finally, DNA binding PPARgamma activity was determined using an ELISA-based PPARgamma activation quantification method. Treatment with an inhibitor of farnesylation (FTI-277) arrests adipogenesis without affecting cell survival. This effect was concomitant with lower levels of PPARgamma expression and activity. Finally, accumulation of prelamin A induced an increased proportion of mature SREBP-1 which is known to affect PPARgamma activity. In summary, inhibition of protein farnesylation arrests the adipogenic differentiation of MSCs and affects PPARgamma expression and activity.
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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) | 0.003 | 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".