Regulatory Effects of Peptides from the Pro and Catalytic Domains of Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) on Low-Density Lipoprotein Receptor (LDL-R)
Bibliographic record
Abstract
BACKGROUND: Proprotein convertase subtilisin/kexin 9 (PCSK9) is a proteinase K subtype of mammalian subtilases collectively called PCSKs. PCSK9 upregulates plasma-cholesterol level by degrading low-density lipoprotein receptor (LDL-R). As a result, PCSK9 is a major target for intervention of hypercholesterolemia and in this regard PCSK9- inhibitors may find useful therapeutic and biochemical applications. OBJECTIVE: Our objective is to develop short peptide based PCSK9 inhibitors from its own pro and/or catalytic domains. RESULTS: Using human (h) hepatic HepG2 and Huh7 cells we showed that the acidic N-terminal hPCSK(931-60), 31-40 and the mid-basic hPCSK(991-120) peptides derived from hPCSK9-prodomain significantly enhanced LDL-R level without altering PCSK9 content. Moreover, the physiologically relevant phoshpho-Ser47 and sulpho-Y38 containing hPCSK(931-60) peptides diminished LDL-R level suggesting that such posttranslational modifications in the prodomain lead to gain of PCSK9- functional activity. These modifications are thus expected to lead to even higher level of plasma cholesterol. As expected, addition of purified recombinant-PCSK9 to the culture medium decreased LDL-R level which can be restored back by exogenous addition of hPCSK(931-40), (31-60) or (91-120) peptides. Using a series of truncated peptides, we identified the most potent LDL-R promoting activity to reside within the prodomain sequence hPCSK(931-37). Two catalytic domain peptides hPCSK(9181-200) and hPCSK(9368-390), containing proposed LDL-R interacting sites have been shown to diminish LDL-R level. CONCLUSION: Our study concludes that specific peptides from pro- and catalytic domains of hPCSK9 can regulate LDL-R in cell based assay and may be useful for development of novel therapeutics for cholesterol regulation.
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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".