Cathepsin D, a Lysosomal Protease, Regulates ABCA1-mediated Lipid Efflux
Bibliographic record
Abstract
To identify genes involved in the regulation of plasma high density lipoprotein (HDL) cholesterol (HDL-C) levels, patients with low HDL-C and age- and sex-matched controls (normal HDL-C) were extensively characterized. Comparative transcriptome analysis was carried out in cholesterol-loaded monocyte-derived macrophages from low HDL subjects segregated into groups with or without cholesterol efflux defects or ABCA1 mutations. Clusters of differentially regulated genes were evident in the low HDL groups as compared with controls. Of particular note, expression of cathepsin D (CTSD), a lysosomal proteinase, was reduced by approximately 50% in monocyte-derived macrophages of low HDL-C subjects, most significantly those with cholesterol efflux defects but without mutations in ABCA1 (p < 0.01). These results were verified by reverse transcription-PCR and replicated in a second cohort. We show here that blocking the activity or expression of CTSD, by pepstatin or CTSD small interfering RNA, respectively, reduced ABCA1 expression and protein abundance in both macrophages and CHO cells and apolipoprotein A-I-mediated lipid efflux by more than 70%. Conversely, expression of CTSD increased both ABCA1 mRNA expression and cellular ABCA1 protein. Consistent with its role in the proteolytic processing of prosaposin, inactivation of CTSD function resulted in the accumulation of glycosphingo-lipid and free cholesterol in late endosomes/lysosomes, a phenotype similar to NPC1 deficiency. Inhibition of CTSD also caused retention of ABCA1 in lysosomal compartments, reducing its trafficking to the plasma membrane. These studies demonstrate a novel and potentially important role for CTSD in intracellular cholesterol trafficking and ABCA1-mediated efflux. Therefore, decreased CTSD expression may contribute to low plasma HDL-C levels.
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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".