Complete functional rescue of the ABCA1−/− mouse by human BAC transgenesis
Bibliographic record
Abstract
Humanized mouse models are useful tools to explore the functional and regulatory differences between human and murine orthologous genes. We have combined a bioinformatics approach and an in vivo approach to assess the functional and regulatory differences between the human and mouse ABCA1 genes. Computational analysis identified significant differences in potential regulatory sites between the human and mouse genes. The effect of these differences was assessed in vivo, using a bacterial artificial chromosome transgenic humanized ABCA1 mouse model that expresses the human gene in the absence of mouse ABCA1. Humanized mice expressed human ABCA1 protein at levels similar to wild-type mice and fully compensated for cholesterol efflux activity and lipid levels seen in ABCA1-deficient mice. Liver X receptor agonist administration resulted in significant increases in HDL values associated with parallel increases in the hepatic ABCA1 protein and mRNA levels in the humanized ABCA1 mice, as seen in the wild-type animals. Our studies indicate that despite differences in potential regulatory regions, the human ABCA1 gene is able to functionally fully compensate for the mouse gene. Our humanized ABCA1 mice can serve as a useful model system for functional analysis of the human ABCA1 gene in vivo and can be used for the generation of potential new therapeutics that target HDL metabolism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".