Targeting Artificial Transcription Factors to the Utrophin A Promoter
Bibliographic record
Abstract
Duchenne muscular dystrophy is caused by a genetic defect in the dystrophin gene. The absence of dystrophin results in muscle fiber necrosis and regeneration, leading to progressive muscle fiber loss. Utrophin is a close analogue of dystrophin. A substantial, ectopic expression of utrophin in the extrasynaptic sarcolemma of dystrophin-deficient muscle fibers can prevent deleterious effects of dystrophin deficiency. An alternative approach for the extrasynaptic up-regulation of utrophin involves the augmentation of utrophin transcription via the endogenous utrophin A promoter using custom-designed transcriptional activator proteins with zinc finger (ZFP) motifs. We tested a panel of custom-designed ZFP for their ability to activate the utrophin A promoter. Expression of one such ZFP efficiently increased, in a time-dependent manner, utrophin transcript and protein levels both in vitro and in vivo. In dystrophic mouse (mdx) muscles, administration of adenoviral vectors expressing this ZFP led to significant enhancement of muscle function with decreased necrosis, restoration of the dystrophin-associated proteins, and improved resistance to eccentric contractions. These studies provide evidence that specifically designed ZFPs can act as strong transcriptional activators of the utrophin A promoter. These may thus serve as attractive therapeutic agents for dystrophin deficiency states such as Duchenne muscular dystrophy.
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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) | 0.000 | 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".