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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".