The M2 Module of the Cys-His-rich Domain (CHRD) of PCSK9 Protein Is Needed for the Extracellular Low-density Lipoprotein Receptor (LDLR) Degradation Pathway
Bibliographic record
Abstract
PCSK9 enhances the cellular degradation of the LDL receptor (LDLR), leading to increased plasma LDL cholesterol. This multidomain protein contains a prosegment, a catalytic domain, a hinge region, and a cysteine-histidine rich domain (CHRD) composed of three tightly packed modules named M1, M2, and M3. The CHRD is required for the activity of PCSK9, but the mechanism behind this remains obscure. To define the contribution of each module to the function of PCSK9, we dissected the CHRD structure. Six PCSK9 deletants were generated by mutagenesis, corresponding to the deletion of only one (ΔM1, ΔM2, ΔM3) or two (ΔM12, ΔM13, ΔM23) modules. Transfection of HEK293 cells showed that all deletants were well processed and expressed compared with the parent PCSK9 but that only those lacking the M2 module were secreted. HepG2 cells lacking endogenous PCSK9 (HepG2/shPCSK9) were used for the functional analysis of the extracellular or intracellular activity of PCSK9 and its deletants. To analyze the ability of the deletants to enhance the LDLR degradation by the intracellular pathway, cellular expressions revealed that only the ΔM2 deletant retains a comparable total LDLR-degrading activity to full-length PCSK9. To probe the extracellular pathway, HepG2/shPCSK9 cells were incubated with conditioned media from transfected HEK293 or HepG2/shPCSK9 cells, and cell surface LDLR levels were analyzed by FACS. The results showed no activity of any secreted deletant compared with PCSK9. Thus, although M2 is dispensable for secretion, its presence is required for the extracellular activity of PCSK9 on cell surface LDLR.
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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) | 0.001 | 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 | 0.001 |
| 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".