Strong Muscle-Specific Regulatory Cassettes Based on Multiple Copies of the Human Slow Troponin I Gene Upstream Enhancer
Bibliographic record
Abstract
High-level tissue-specific expression of recombinant proteins in muscle is an important issue for several therapeutic applications. To achieve this goal, we generated several constructs containing one to five copies of the upstream enhancer (USE) of 160-bp of the human slow troponin I gene, linked to that gene's minimal promoter. We also tested constructs made with one to four copies of a 100-bp deletion of USE (DeltaUSE) reported to drive pan-muscle-specific expression in transgenic mice. These constructs were evaluated by measuring the activity of the reporter gene beta-galactosidase (beta-gal). In cell culture, these multimerized enhancers retained tissue specificity and their transcriptional strength increased with the number of enhancer copies. In myotube cultures (which still contain nondifferentiated cells), constructs containing four and five USE copies were stronger than the cytomegalovirus (CMV) early enhancer/promoter and slightly weaker than the hybrid CMV enhancer/beta-actin (CB) promoter. Those containing three USE, or four DeltaUSE copies were similar in strength to CMV. After electrotransfer of plasmid DNA into the mouse tibialis anterior muscle, the strengths of the two constructs (USEx3 and DeltaUSEx3) were tested; as measured by beta-gal activity in the total muscle lysate and by the number of transduced fibers, they were similar to CMV and CB. Muscle fiber typing, after electrotransfer of the soleus muscle, showed that DeltaUSEx3 and USEx3 were active in slow and fast fibers. The tissue specificity of these two constructs was also evaluated by hydrodynamic plasmid injection through the tail vein. Although significant beta-gal expression was measured in the liver when CMV was tested, no expression above background level was detected with USEx3 and DeltaUSEx3. The strength, muscle specificity, and small size of these transcriptional elements render them very attractive for gene therapy applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".