A valuable control for siRNA experiments is the rescue of the RNAi effect by expression of an siRNA-resistant form of the gene . This gene can simply be a variation of the wild type sequence. An easy way to obtain such a gene is to order an optimized gene using the GeneArt gene synthesis online portal.
GeneArt gene sequences are optimized for expression while maintaining a wild type protein sequence. They are sufficiently different from the wild type sequence to serve as an RNAi rescue control. After co-transfection of this optimized gene with siRNA, you can assess the efficiency of phenotype rescue (see figure below). To demonstrate this, we analyzed the cell cycle regulator CDC2 in MCF-7 cells and used FACS analysis to determine the extent of arrest at the G2 phase .
Rescue of siRNA-mediated knockdown of an endogenous gene with an optimized gene variant. Untreated MCF-7 cells (wild type), or cells transfected with CDC2 siRNA only (knockdown), or CDC2 siRNA plus the optimized cdc2 gene (rescue), or a non-silencing siRNA plus the optimized cdc2 construct (negative control) were stained with propidium iodide after 72 hours and subjected to FACS analysis to determine cell-cycle distribution. The percentage of negative control cells compared to cells with a knockdown phenotype shifted from 16.2%/14.9% to 36.3%, i.e., around 20%. Negative control cells compared to rescued cells shifted from 16.2%/14.9% to 23.4%, i.e. around 8%, indicating that the optimized cdc2 construct rescued around 60% of cells from knockdown. Endogenous CDC2 knockdown was confirmed by real-time RT-PCR with primers that exclusively detected endogenous cdc2, whereas expression of exogenous CDC2 from the sequence-optimized construct was confirmed by real-time RT-PCR with primers that exclusively detected exogenous cdc2 (data not shown). From Fath et al. .
We offer both Ambion Silencer Select RNAi positive and negative controls for validation. Or you can use a scrambled sequence as a negative control (take care that the scrambled siRNA is not complementary to any gene in the target organism).
One of the best ways to increase confidence in data from siRNA experiments is to independently use two or more siRNAs to a single target gene. Different siRNAs to the same gene with comparable gene silencing efficacy should induce similar changes in gene expression profiles or phenotypes. Any changes induced by one siRNA and not the other(s) may be attributed to off-target effects.
The rules for siRNA specificity are not yet fully defined. Some reports suggest that even a single nucleotide mismatch in the middle of an siRNA can abolish its activity [1,2]. In contrast, another report indicates that siRNAs can silence non-target genes containing as few as 14–15 consecutive complementary nucleotides . Therefore, until we reach a better understanding of siRNA specificity, it is best to allow for at least 2 nucleotide mismatches between an siRNA and all closely related nontarget genes. The algorithm developed by Cenix Bioscience, and used in Ambion's validated and pre-designed siRNAs, incorporates a stringent specificity check.
Nonspecific silencing effects may be seen when an siRNA is transfected into cells at concentrations of 100 nM or higher [3-5]. However, this non-specific effect is mitigated when siRNAs are used at lower concentrations (<30 nM). To ensure target specificity, therefore, it is best to titrate the siRNA and use it at its lowest effective concentration.
Using highly effective siRNAs will maximize target mRNA reduction and minimize the possibility of off-target effects by allowing the use of lower siRNA concentrations in the RNAi experiments. The algorithm developed by Cenix Bioscience, and available from Ambion for siRNA design, accurately predicts potent siRNA sequences.
Because silencing can be virtually guaranteed with siRNA populations (e.g., siRNA cocktails generated by RNase III/Dicer digestion of long dsRNAs), they are sometimes used to mediate gene silencing [6,7]. Although useful for screening purposes, siRNA cocktails may theoretically increase the chances of off-target effects. The results of RNAi experiments performed with siRNA populations, therefore, should be confirmed by using individual siRNAs.
Recent evidence indicates that upregulation of the antiviral response may be a useful indicator of nonspecific siRNA effects. The most comprehensive way to monitor the antiviral response is with genome-wide arrays. However, this may be expensive or impractical in many cases. Several simple assays have been developed to monitor the interferon response. These include analyzing the upregulation of 2′5′ oligoadenylate synthetase, STAT1 mRNA, and activation of RNase L .
The specificity of an siRNA can only be definitively determined by looking at global changes in gene expression pattern (i.e., by using DNA microarrays). In these experiments, multiple siRNAs targeting a particular gene should give rise to ‘gene-specific’ changes in expression profiles. Off-target effects, on the other hand, will be seen as ‘siRNA-specific’ rather than gene-specific changes in gene expression patterns [3,4,9,10].
In siRNA experiments it may be beneficial to monitor both mRNA and protein levels for several reasons. For instance, mRNA reduction seen without a corresponding reduction in protein levels indicates that protein turnover is slow. Protein reduction in the absence of mRNA reduction, however, may indicate that an siRNA is mediating its effects at the translation level like a microRNA .
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