ArticleBMC molecular biology2010
A direct comparison of strategies for combinatorial RNA interference.
Article in BMC molecular biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Exploring the Potential of Receptor Silencing in the Tumor Microenvironment by RNA Interference.Biomolecules · 2026Review
- Plasmids Expressing shRNAs Specific to the Nucleocapsid Gene Inhibit the Replication of Porcine Deltacoronavirus In Vivo.Animals : an open access journal from MDPI · 2021Article
- Promoter Orientation within an AAV-CRISPR Vector Affects Cas9 Expression and Gene Editing Efficiency.The CRISPR journal · 2020Article
- Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects.Bioinformatics (Oxford, England) · 2019Article
- Sources of Error in Mammalian Genetic Screens.G3 (Bethesda, Md.) · 2016Article
- Oligonucleotide-based theranostic nanoparticles in cancer therapy.Nanomedicine (London, England) · 2016Review
- Delayed Newcastle disease virus replication using RNA interference to target the nucleoprotein.Biologicals : journal of the International Association of Biological Standardization · 2015Article
- Cell type specific, traceable gene silencing for functional gene analysis during vertebrate neural development.Nucleic acids research · 2011Article
- A comparison of multiple shRNA expression methods for combinatorial RNAi.Genetic vaccines and therapy · 2011Article
- RNAi screening: new approaches, understandings, and organisms.Wiley interdisciplinary reviews. RNAReview
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Authors and funding
4 authors.
Funding
Abstract
backgroundCombinatorial RNA interference (co-RNAi) is a valuable tool for highly effective gene suppression of single and multiple-genes targets, and can be used to prevent the escape of mutation-prone transcripts. There are currently three main approaches used to achieve co-RNAi in animal cells; multiple promoter/shRNA cassettes, long hairpin RNAs (lhRNA) and miRNA-embedded shRNAs, however, the relative effectiveness of each is not known. The current study directly compares the ability of each co-RNAi method to deliver pre-validated siRNA molecules to the same gene targets.
resultsDouble-shRNA expression vectors were generated for each co-RNAi platform and their ability to suppress both single and double-gene reporter targets were compared. The most reliable and effective gene silencing was achieved from the multiple promoter/shRNA approach, as this method induced additive suppression of single-gene targets and equally effective knockdown of double-gene targets. Although both lhRNA and microRNA-embedded strategies provided efficient gene knockdown, suppression levels were inconsistent and activity varied greatly for different siRNAs tested. Furthermore, it appeared that not only the position of siRNAs within these multi-shRNA constructs impacted upon silencing activity, but also local properties of each individual molecule. In addition, it was also found that the insertion of up to five promoter/shRNA cassettes into a single construct did not negatively affect the efficacy of each individual shRNA.
conclusionsBy directly comparing the ability of shRNAs delivered from different co-RNA platforms to initiate knockdown of the same gene targets, we found that multiple U6/shRNA cassettes offered the most reliable and predictable suppression of both single and multiple-gene targets. These results highlight some important strengths and pitfalls of the currently used methods for multiple shRNA delivery, and provide valuable insights for the design and application of reliable co-RNAi.
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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.