LPA disruption with AAV-CRISPR potently lowers plasma apo(a) in transgenic mouse model: A proof-of-concept study
Bibliographic record
Abstract
Lipoprotein(a) (Lp(a)) represents a unique subclass of circulating lipoprotein particles and consists of an apolipoprotein(a) (apo(a)) molecule covalently bound to apolipoprotein B-100. The metabolism of Lp(a) particles is distinct from that of low-density lipoprotein (LDL) cholesterol, and currently approved lipid-lowering drugs do not provide substantial reductions in Lp(a), a causal risk factor for cardiovascular disease. Somatic genome editing has the potential to be a one-time therapy for individuals with extremely high Lp(a). We generated an LPA transgenic mouse model expressing apo(a) of physiologically relevant size. Adeno-associated virus (AAV) vector delivery of CRISPR-Cas9 was used to disrupt the LPA transgene in the liver. AAV-CRISPR nearly completely eliminated apo(a) from the circulation within a week. We performed genome-wide off-target assays to determine the specificity of CRISPR-Cas9 editing within the context of the human genome. Interestingly, we identified intrachromosomal rearrangements within the LPA cDNA in the transgenic mice as well as in the LPA gene in HEK293T cells, due to the repetitive sequences within LPA itself and neighboring pseudogenes. This proof-of-concept study establishes the feasibility of using CRISPR-Cas9 to disrupt LPA in vivo , and highlights the importance of examining the diverse consequences of CRISPR cutting within repetitive loci and in the genome globally.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".