Rational Design of Murine Secreted Alkaline Phosphatase for Enhanced Performance as a Reporter Gene in Mouse Gene Therapy Preclinical Studies
Bibliographic record
Abstract
Many preclinical gene therapy studies use a reporter gene to evaluate vector design and performance in mouse models of human disease. Unfortunately, most commonly used reporter genes are immunogenic in mice, which confounds accurate evaluation of vector function. In previous studies, we showed that the murine secreted alkaline phosphatase (mSEAP) gene functions well as a simple and sensitive reporter gene in mice. In this study, we have used rational design to enhance mSEAP performance. The majority of native mSEAP remains attached to the outer surface of the cell through glycan phosphatidylinositol linkage; removal of the carboxy-terminal tail of mSEAP resulted in a dramatic enhancement of release of the protein into cell culture medium and into mouse plasma in whole animal experiments. We increased the heat stability of mSEAP through mutation of a key residue in the crown domain of the protein (H451E), thus allowing us to reduce endogenous, background AP activity through heat inactivation for enhanced sensitivity. We show that these alterations in mSEAP result in enhanced performance in tissue culture and mouse studies. Taken together, these data illustrate that mSEAP is a sensitive, nonimmunogenic reporter for preclinical mouse studies. In this study, Christou and Parks use rational design to enhance murine secreted alkaline phosphotase (mSEAP) performance. Specifically, the authors removed the carboxy-terminal tail of mSEAP, which resulted in a dramatic enhancement of release of the protein into cell culture medium and into mouse plasma in whole animal experiments. They also increased the heat stability of mSEAP through mutation of a key residue in the crown domain of the protein (H451E), which led to reduced endogenous, background alkaline phosphatase activity and enhanced sensitivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".