ArticlePLoS computational biology2008
Modeling recursive RNA interference.
Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Simultaneously induced mutations in eIF4E genes by CRISPR/Cas9 enhance PVY resistance in tobacco.Scientific reports · 2022Article
- Emergent RNA-RNA interactions can promote stability in a facultative phototrophic endosymbiosis.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A forward genetic screen reveals essential and non-essential RNAi factors in Paramecium tetraurelia.Nucleic acids research · 2014Article
- In vitro RNA interference targeting the DNA polymerase gene inhibits orf virus replication in primary ovine fetal turbinate cells.Archives of virology · 2014Article
- Estimate hidden dynamic profiles of siRNA effect on apoptosis.BMC bioinformatics · 2013Article
- Functional specialization of Piwi proteins in Paramecium tetraurelia from post-transcriptional gene silencing to genome remodelling.Nucleic acids research · 2011Article
- Viral RNAi suppressor reversibly binds siRNA to outcompete Dicer and RISC via multiple turnover.Journal of molecular biology · 2011Article
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Authors and funding
1 author.
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Abstract
An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.