Advanced Design of Dumbbell-shaped Genetic Minimal Vectors Improves Non-coding and Coding RNA Expression
Bibliographic record
Abstract
Dumbbell-shaped DNA minimal vectors lacking nontherapeutic genes and bacterial sequences are considered a stable, safe alternative to viral, nonviral, and naked plasmid-based gene-transfer systems. We investigated novel molecular features of dumbbell vectors aiming to reduce vector size and to improve the expression of noncoding or coding RNA. We minimized small hairpin RNA (shRNA) or microRNA (miRNA) expressing dumbbell vectors in size down to 130 bp generating the smallest genetic expression vectors reported. This was achieved by using a minimal H1 promoter with integrated transcriptional terminator transcribing the RNA hairpin structure around the dumbbell loop. Such vectors were generated with high conversion yields using a novel protocol. Minimized shRNA-expressing dumbbells showed accelerated kinetics of delivery and transcription leading to enhanced gene silencing in human tissue culture cells. In primary human T cells, minimized miRNA-expressing dumbbells revealed higher stability and triggered stronger target gene suppression as compared with plasmids and miRNA mimics. Dumbbell-driven gene expression was enhanced up to 56- or 160-fold by implementation of an intron and the SV40 enhancer compared with control dumbbells or plasmids. Advanced dumbbell vectors may represent one option to close the gap between durable expression that is achievable with integrating viral vectors and short-term effects triggered by naked RNA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".