Persistence of Different Forms of Transient RNAi during Apoptosis in Mammalian Cells
Bibliographic record
Abstract
Gene silencing by transient or stable RNA-interference (RNAi) is used for the study of apoptosis with an assumption that apoptotic events will not influence RNAi. However, we recently reported that stable RNAi, i.e., a permanent gene-knockdown mediated by shRNA-generating DNA vectors that are integrated in the genome, fails rapidly after induction of apoptosis due to caspase-3-mediated cleavage and inactivation of the endoribonuclease Dicer-1 that is required for conversion of shRNA to siRNA. Since apoptosis studies also increasingly employ transient RNAi models in which apoptosis is induced immediately after a gene is temporarily knocked down within a few days of transfection with RNAi-inducing agents, we examined the impact of apoptosis on various models of transient RNAi. We report here that unlike the stable RNAi, all forms of transient RNAi, whether Dicer-1-independent (by 21mer dsRNA) or Dicer-1-dependent (by 27mer dsRNA or shRNA-generating DNA vector), whether for an exogenous gene GFP or an endogenous gene poly(ADP-ribose) polymerase-1, do not fail for 2-3 days after onset of apoptosis. Our results reflect the differences in dynamics of achieving and maintaining RNAi during the early phase after transfection in the transient RNAi model and the late steady-state phase of gene-knockdown in stable RNAi model. Our results also sound a cautionary note that RNAi status should be frequently validated in the studies involving apoptosis and that while stable RNAi can be safely used for the study of early apoptotic events, transient RNAi is more suitable for the study of both early and late apoptotic events.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".