Adenovirus-Delivered Antisense RNA and shRNA Exhibit Different Silencing Efficiencies for the Endogenous Transforming Growth Factor- <i>β</i> (TGF- <i>β</i> ) Type II Receptor
Bibliographic record
Abstract
Gene silencing is an essential tool in gene discovery and gene therapy. Traditionally, viral delivery of antisense RNA and, more recently, small interfering RNA (siRNA) molecules in the form of small hairpin RNAs (shRNA) has been used as a strategy to achieve gene silencing. Nevertheless, the enduring challenge is to identify molecules that specifically and optimally silence a given target gene. In this study, we tested a set of adenovirus-delivered antisense RNA fragments and adenovirus-delivered shRNA molecules for their ability to target human transforming growth factor-beta type II receptor (TGFbetaRII). We used a dicistronic reporter, consisting of the coding sequences for TGFbetaRII and green fluorescent protein (GFP) to screen for optimal silencing agents targeting TGFbetaRII. Our results show, for both antisense RNA and shRNA molecules, that their effectiveness in the GFP screen correlated directly with their ability to reduce exogenously expressed TGFbetaRII. Unexpectedly, the antisense RNAs were unable to silence endogenous TGFbetaRII. In contrast, the shRNAs were able to silence endogenous TGFbetaRII. The shRNA that demonstrated the most pronounced effect on the dicistronic TGFbetaRII/GFP reporter reduced endogenous TGFbetaRII protein expression by 70% in A549 cells and reduced TGFbeta signaling by >80% in HeLa cells.
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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 | 0.000 | 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".