Enhancement of RNA interference effect in P19 EC cells by an RNA-dependent RNA polymerase.
Bibliographic record
Abstract
BACKGROUND: RNA interference (RNAi) is a phenomenon uses double-stranded RNA (dsRNA) to specifically inhibit gene expression. The non-specific silencing caused by interferon response to dsRNA in mammalian cells limits the potential of utilizing RNAi to study gene function. Duplexes of 21-nucleotide short interfering dsRNA (siRNA) inhibit gene expression by RNAi. In some organisms, siRNA can also function as a primer converting mRNA into dsRNA that are further cleaved to produce more siRNA. This activity involves the enzyme RNA-dependent RNA polymerase (RdRP). There are no known RdRP involved in RNAi in mammals. By using an RdRP from Caenorhabditis elegance named ego-1, investigators intend to enhance RNAi effect in mammalian cells. The aims of this project were: 1) to investigate the efficiency of siRNA to enhanced green fluorescent protein (eGFP) gene silencing and 2) to enhance the RNAi effect. METHODS: We used a vector-based siRNA to target eGFP. Also we used a vector expressing ego-1 to test for a possible amplification effect of RNAi. The expression of eGFP in the cells was detected by using fluorescent microscopy, flowcytometry and Western-blotting. RESULTS: Transfection of the plasmid into P19 cells significantly decreased eGFP fluorescence. In addition, eGFP protein was reduced. Preliminary data suggested that the presence of ego-1 enhanced the RNAi effect. CONCLUSION: The results indicated that use of hairpin siRNA expression vectors for RNAi is a promising method to inhibition of gene expression in mammalian cells. Also, introducing RdRP enzyme to mammalian cells might amplify the RNAi effect in the 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 | 0.001 |
| 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 | 0.001 |
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".