Host Cell Detection of Noncoding Stuffer DNA Contained in Helper-Dependent Adenovirus Vectors Leads to Epigenetic Repression of Transgene Expression
Bibliographic record
Abstract
Helper-dependent adenovirus (hdAd) vectors have shown great promise as therapeutic gene delivery vehicles in gene therapy applications. However, the level and duration of gene expression from hdAd can differ considerably depending on the nature of the noncoding stuffer DNA contained within the vector. For example, an hdAd containing 22 kb of prokaryotic DNA (hdAd-prok) expresses its transgene 60-fold less efficiently than a similar vector containing eukaryotic DNA (hdAd-euk). Here we have determined the mechanistic basis of this phenomenon. Although neither vector was subjected to CpG methylation and both genomes associated with cellular histones to similar degrees, hdAd-prok chromatin was actively deacetylated. Insertion of an insulator element between the transgene and the bacterial DNA derepressed hdAd-prok, suggesting that foreign DNA nucleates repressive chromatin structures that spread to the transgene. We found that Sp100B/Sp100HMG and Daxx play a role in repressing transgene expression from hdAd and act independently of PML bodies. Thus, we have identified nuclear factors involved in recognizing foreign DNA and have determined the mechanism by which associated genes are repressed.
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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".