Pcpe2, a Novel Extracellular Matrix Protein, Regulates Adipocyte SR-BI–Mediated High-Density Lipoprotein Uptake
Bibliographic record
Abstract
Objective: To investigate the role of adipocyte Pcpe2 (procollagen C-endopeptidase enhancer 2) in SR-BI (scavenger receptor class BI)-mediated HDL-C (high-density lipoprotein cholesterol) uptake and contributions to adipose lipid storage. Approach and Results: Pcpe2, a glycoprotein devoid of intrinsic proteolytic activity, is believed to participate in extracellular protein-protein interactions, supporting SR-BI- mediated HDL-C uptake. In published studies, Pcpe2 deficiency increased the development of atherosclerosis by reducing SR-BI-mediated HDL-C catabolism, but the biological impact of this deficiency on adipocyte SR-BI-mediated HDL-C uptake is unknown. Differentiated cells from Ldlr-/-/Pcpe2-/- (Pcpe2-/-) mouse adipose tissue showed elevated SR-BI protein levels, but significantly reduced HDL-C uptake compared to Ldlr-/- (control) adipose tissue. SR-BI-mediated HDL-C uptake was restored by preincubation of cells with exogenous Pcpe2. In diet-fed mice lacking Pcpe2, significant reductions in visceral, subcutaneous, and brown adipose tissue mass were observed, despite elevations in plasma triglyceride and cholesterol concentrations. Significant positive correlations exist between adipose mass and Pcpe2 expression in both mice and humans. Conclusions: Overall, these findings reveal a novel and unexpected function for Pcpe2 in modulating SR-BI expression and function as it relates to adipose tissue expansion and cholesterol balance in both mice and humans.
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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".