Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Single Domain Antibodies Are Potent Inhibitors of Low Density Lipoprotein Receptor Degradation
Bibliographic record
Abstract
Single domain antibodies (sdAbs) correspond to the antigen-binding domains of camelid antibodies. They have the same antigen-binding properties and specificity as monoclonal antibodies (mAbs) but are easier and cheaper to produce. We report here the development of sdAbs targeting human PCSK9 (proprotein convertase subtilisin/kexin type 9) as an alternative to anti-PCSK9 mAbs. After immunizing a llama with human PCSK9, we selected four sdAbs that bind PCSK9 with a high affinity and produced them as fusion proteins with a mouse Fc. All four sdAb-Fcs recognize the C-terminal Cys-His-rich domain of PCSK9. We performed multiple cellular assays and demonstrated that the selected sdAbs efficiently blocked PCSK9-mediated low density lipoprotein receptor (LDLR) degradation in cell lines, in human hepatocytes, and in mouse primary hepatocytes. We further showed that the sdAb-Fcs do not affect binding of PCSK9 to the LDLR but rather block its induced cellular LDLR degradation. Pcsk9 knock-out mice expressing a human bacterial artificial chromosome (BAC) transgene were generated, resulting in plasma levels of ∼300 ng/ml human PCSK9. Mice were singly or doubly injected with the best sdAb-Fc and analyzed at day 4 or 11, respectively. After 4 days, mice exhibited a 32 and 44% decrease in the levels of total cholesterol and apolipoprotein B and ∼1.8-fold higher liver LDLR protein levels. At 11 days, the equivalent values were 24 and 46% and ∼2.3-fold higher LDLR proteins. These data constitute a proof-of-principle for the future usage of sdAbs as PCSK9-targeting drugs that can efficiently reduce LDL-cholesterol, and as tools to study the Cys-His-rich domain-dependent sorting the PCSK9-LDLR complex to lysosomes.
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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 | 0.000 |
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".