Superior Tissue-Specific Expression from Tyrosinase and Prostate-Specific Antigen Promoters/Enhancers in Helper-Dependent Compared with First-Generation Adenoviral Vectors
Bibliographic record
Abstract
The ability to target specific tissues is important in many applications of gene therapy. In this respect, a disadvantage of adenoviral vectors is the relative lack of specificity with which they transduce cells. One approach to overcome this is to express the therapeutic gene under the control of a tissue-specific promoter. However, the specificity and activity of these promoters may be altered by adenoviral sequences in the vector backbone. In contrast, helper-dependent adenoviral (HDAd) vectors [Parks, R.J., Chen, L., Anton, M., Sankar, U., Rudnicki, M.A., and Graham, F.L. (1996). Proc. Natl. Acad. Sci. U.S.A. 93, 13565-13570] are almost completely devoid of adenovirus sequences, and this may preserve the specificity of these heterologous promoters. We have compared HDAd and first-generation adenoviral (FGAd) vectors with respect to tissue-specific expression from prostate-specific antigen (PSA) or tyrosinase promoters/enhancers. A PSA-positive cell line (LNCaP) and a panel of PSA-negative cell lines were infected with HDAd vectors expressing luciferase under the control of three different kinds of PSA promoter/enhancer constructs. The results showed that these PSA promoter/enhancer cassettes in HDAd vectors maintained strict tissue-specific expression, but lost specificity when expressed from FGAd vectors. Similar results were observed with tyrosinase promoter-carrying vectors, except that the tyrosinase promoter retained a small degree of tissue specificity in FGAd vectors. Insertion of a murine cytomegalovirus immediate-early gene promoter-beta-galactosidase (MCMV-lacZ)-expressing cassette into a second site in the HDAd vector backbone significantly impaired the tissue specificity of the PSA and tyrosinase promoters. These results indicate that HDAd vectors are superior to FGAd vectors in their ability to maintain high levels of tissue-specific expression from PSA and tyrosinase promoters/enhancers. They also suggest that tissue-specific expression can be influenced not only by Ad sequences, but also by other viral and/or strong constitutive promoter/enhancers (such as the MCMV promoter) in the vector backbone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".