Impact of RNA interference on gene networks

Malphettes, Laetitia; Fussenegger, Martin

doi:10.1016/j.ymben.2006.07.005

Cited by 31 publications

(26 citation statements)

References 90 publications

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“…EGFP expression profiles revealed that increasing the siRNA GFP copy number had a marginally improved silencing effect ( Fig. 7B; silencing of single, twin, and triple siRNA GFP copy numbers was 81%, 86%, and 91%, respectively), which is consistent with both theoretical predictions and empirical observations regarding the mechanistic impact of the relative concentrations of siRNA to target mRNA (Malphettes and Fussenegger, 2006a;Malphettes and Fussenegger, 2006b). This indicates that the multi-cistronic siRNA approach could either be used to silence multiple target genes, or to ensure stronger silencing of a single target gene.…”

Section: Multi-cistronic Engineeringsupporting

confidence: 84%

“…In both cases, siRNAs enter the RNA-induced silencing complex (RISC) and either mediate cleavage and degradation of complementary mRNAs or repress translation (Elbashir et al, 2001b). As target mRNA depletion is dependent upon relative siRNA to target mRNA levels, a key requirement for any gene silencing strategy is that sufficient siRNA is successfully introduced into target cells (Malphettes and Fussenegger, 2006a;Malphettes and Fussenegger, 2006b). …”

Section: Introductionmentioning

confidence: 99%

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Multi‐gene engineering: Simultaneous expression and knockdown of six genes off a single platform

Greber

Fussenegger

2006

Biotech & Bioengineering

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Increases in our understanding of gene function have greatly expanded the repertoire of possible genetic interventions at our disposal with the consequence that many genetic engineering applications require multiple manipulations in which target genes can be both overexpressed and silenced in a simple and co-ordinated manner. Using synthetic introns as a source of encoding shortinterfering RNA (siRNA), we demonstrate that it is possible to simultaneously express both a transgene and siRNA from a single polymerase (Pol) II promoter. By encoding siRNA as an intron between two protein domains requiring successful splicing for functionality, it was possible to demonstrate that splicing was occurring, that the coding genes (exonic transgenes) resulted in functional protein, and that the spliced siRNA-containing lariat was capable of modulating expression of a separate target gene. We subsequently extended this concept to develop pTRIDENT-based multi-cistronic vectors that were capable of co-ordinated expression of up to three siRNAs and three transgenes off a single genetic platform. Such multi-gene engineering technology, enabling concomitant transgene overexpression and target gene knockdown, should be useful for therapeutic, biopharmaceutical production, and basic research applications.

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Section: Multi-cistronic Engineeringsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Multi‐gene engineering: Simultaneous expression and knockdown of six genes off a single platform

Greber

Fussenegger

2006

Biotech & Bioengineering

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“…The parameter k T is the protein translation rate, whereas d p represents the basal protein degradation rate. At least four different models have been proposed in the literature, for the RNA interference mechanism [15-17]. Each of these models is based on the general approach described by equations (1) but each has a different functional form for δ ( X m , X s ).…”

Section: Resultsmentioning

confidence: 99%

“…Target mRNAs are then recognized by Watson-Crick base pairing [1] and bound by the siRNA-RISC complex. Finally, in step 4, mRNA degradation is induced, target mRNA is dissociated from the siRNA, and the siRNA-RISC complex is released to process further mRNA targets [15]. Here, we will focus on quantitatively modeling step 2 to step 4 of the RNA interference process using, as an experimental tool, synthetic siRNA oligomers.…”

Section: Introductionmentioning

confidence: 99%

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Modeling RNA interference in mammalian cells

et al. 2011

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BackgroundRNA interference (RNAi) is a regulatory cellular process that controls post-transcriptional gene silencing. During RNAi double-stranded RNA (dsRNA) induces sequence-specific degradation of homologous mRNA via the generation of smaller dsRNA oligomers of length between 21-23nt (siRNAs). siRNAs are then loaded onto the RNA-Induced Silencing multiprotein Complex (RISC), which uses the siRNA antisense strand to specifically recognize mRNA species which exhibit a complementary sequence. Once the siRNA loaded-RISC binds the target mRNA, the mRNA is cleaved and degraded, and the siRNA loaded-RISC can degrade additional mRNA molecules. Despite the widespread use of siRNAs for gene silencing, and the importance of dosage for its efficiency and to avoid off target effects, none of the numerous mathematical models proposed in literature was validated to quantitatively capture the effects of RNAi on the target mRNA degradation for different concentrations of siRNAs. Here, we address this pressing open problem performing in vitro experiments of RNAi in mammalian cells and testing and comparing different mathematical models fitting experimental data to in-silico generated data. We performed in vitro experiments in human and hamster cell lines constitutively expressing respectively EGFP protein or tTA protein, measuring both mRNA levels, by quantitative Real-Time PCR, and protein levels, by FACS analysis, for a large range of concentrations of siRNA oligomers.ResultsWe tested and validated four different mathematical models of RNA interference by quantitatively fitting models' parameters to best capture the in vitro experimental data. We show that a simple Hill kinetic model is the most efficient way to model RNA interference. Our experimental and modeling findings clearly show that the RNAi-mediated degradation of mRNA is subject to saturation effects.ConclusionsOur model has a simple mathematical form, amenable to analytical investigations and a small set of parameters with an intuitive physical meaning, that makes it a unique and reliable mathematical tool. The findings here presented will be a useful instrument for better understanding RNAi biology and as modelling tool in Systems and Synthetic Biology.

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Supervised Learning Methods for MicroRNA Studies

Zhang¹,

Nam²

2008

Machine Learning in Bioinformatics

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Impact of RNA interference on gene networks

Cited by 31 publications

References 90 publications

Multi‐gene engineering: Simultaneous expression and knockdown of six genes off a single platform

Multi‐gene engineering: Simultaneous expression and knockdown of six genes off a single platform

Modeling RNA interference in mammalian cells

Supervised Learning Methods for MicroRNA Studies

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