Introduction of human apolipoprotein E4 “domain interaction” into mouse apolipoprotein E
Bibliographic record
Abstract
Human apolipoprotein E4 (apoE4) binds preferentially to lower density lipoproteins, including very low density lipoproteins, and is associated with increased risk of atherosclerosis and neurodegenerative disorders, including Alzheimer's disease. This binding preference is the result of the presence of Arg-112, which causes Arg-61 in the amino-terminal domain to interact with Glu-255 in the carboxyl-terminal domain. ApoE2 and apoE3, which have Cys-112, bind preferentially to high density lipoproteins (HDL) and do not display apoE4 domain interaction. Mouse apoE, like apoE4, contains the equivalent of Arg-112 and Glu-255, but lacks the critical Arg-61 equivalent (it contains Thr-61). Thus, mouse apoE does not display apoE4 domain interaction and, as a result, behaves like human apoE3, including preferential binding to HDL. To assess the potential role of apoE4 domain interaction in atherosclerosis and neurodegeneration, we sought to introduce apoE4 domain interaction into mouse apoE. Replacing Thr-61 in mouse apoE with arginine converted the binding preference from HDL to very low density lipoproteins in vitro, suggesting that apoE4 domain interaction could be introduced into mouse apoE in vivo. Using gene targeting in embryonic stem cells, we created mice expressing Arg-61 apoE. Heterozygous Arg-61/wild-type apoE mice displayed two phenotypes found in human apoE4/E3 heterozygotes: preferential binding to lower density lipoproteins and reduced abundance of Arg-61 apoE in the plasma, reflecting its more rapid catabolism. These findings demonstrate the successful introduction of apoE4 domain interaction into mouse apoE in vivo. The Arg-61 apoE mouse model will allow the effects of apoE4 domain interaction in lipoprotein metabolism, atherosclerosis, and neurodegeneration to be determined.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".