Adipocyte Low Density Lipoprotein Receptor-related Protein Gene Expression and Function Is Regulated by Peroxisome Proliferator-activated Receptor γ
Bibliographic record
Abstract
The alpha(2)-macroglobulin receptor/low density lipoprotein receptor-related protein (LRP) is a large multifunctional receptor that interacts with a variety of molecules. It is implicated in biologically important processes such as lipoprotein metabolism, neurological function, tissue remodeling, protease complex clearance, and cell signal transduction. However, the regulation of LRP gene expression remains largely unknown. In this study, we have analyzed 2 kb of the 5'-flanking region of the LRP gene and identified a predicted peroxisome proliferator response element (PPRE) from -1185 to -1173. Peroxisome proliferator-activated receptor gamma (PPARgamma) ligands such as fatty acids and rosiglitazone increased functional cell surface LRP by 1.5-2.0-fold in primary human adipocytes and in the SW872 human liposarcoma cell line as assessed by activated alpha(2)-macroglobulin binding and degradation. These agents were found to increase LRP transcription. Gel shift analysis of the putative PPRE demonstrated direct binding of PPARgamma/retinoid X receptor alpha heterodimers to the PPRE in the LRP gene. Furthermore, these heterodimers could no longer interact with a mutated PPRE probe. The isolated promoter was functional in SW872 cells, and its activity was increased by 1.5-fold with the addition of rosiglitazone. Furthermore, the isolated response element was similarly responsive to rosiglitazone when placed upstream of an ideal promoter. Mutagenesis of the predicted PPRE abolished the ability of this construct to respond to rosiglitazone. These data demonstrate that fatty acids and rosiglitazone directly stimulate transcription of the LRP gene through activation of PPARgamma and increase functional LRP expression.
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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 | 0.001 |
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".