Selection of hyperfunctional siRNAs with improved potency and specificity

Abstract

One critical step in RNA interference (RNAi) experiments is to design small interfering RNAs (siRNAs) that can greatly reduce the expression of the target transcripts, but not of other unintended targets. Although various statistical and computational approaches have been attempted, this remains a challenge facing RNAi researchers. Here, we present a new experimentally validated method for siRNA design. By analyzing public siRNA data and focusing on hyperfunctional siRNAs, we identified a set of sequence features as potency selection criteria to build an siRNA design algorithm with support vector machines. Additional bioinformatics filters were also included in the algorithm to increase RNAi specificity by reducing potential sequence cross-hybridization or microRNA-like effects. Independent validation experiments were performed, which indicated that the newly designed siRNAs have significantly improved performance, and worked effectively even at low concentrations. Furthermore, our cell-based studies demonstrated that the siRNA off-target effects were significantly reduced when the siRNAs were delivered into cells at the 3 nM concentration compared to 30 nM. Thus, the capability of our new design program to select highly potent siRNAs also renders increased RNAi specificity because these siRNAs can be used at a much lower concentration. The siRNA design web server is available at http://www5.appliedbiosystems.com/tools/siDesign/.

Preclinical and clinical development of siRNA-based therapeutics

“…Naito et al have developed an algorithm that selects siRNAs with lower seed-target duplex stability, since they observed high correlation between seed-dependent off-target effect and the thermodynamic stability of the duplex between the seed region of the siRNA guide strand and its target mRNA [18]. Some others focus on selection of hyperfunctional siRNAs that work effectively even at low concentrations and thus induce fewer off-target effects [40]. Regardless of which algorithm is used to select effective and specific siRNAs, global gene expression analysis is an inevitable step, especially for siRNAs that will be carried into clinics [31, 41, 42].…”

Development of a siRNA and shRNA screening system based on a kinase fusion protein

“…Combining two RNA fragments in a transcript may alter the RNA secondary structure, which can affect binding of the RNAi molecules to the target RNA sequence. For this reason we tested three different variants for each gene of interest (Wang et al 2009). We generated (1) p16-tPDGFRa or Pten-tPDGFRa fusion transcripts that encode p16-tPDGFRa or Pten-tPDGFRa fusion proteins, (2) tPDGFRap16 or tPDGFRa-Pten fusion transcripts that encode tPDGFRa-p16 or tPDGFRa-Pten fusion proteins, or (3) tPDGFRa*-p16 or tPDGFRa*-Pten fusion transcripts that will express only the tPDGFRa protein, since a stop codon is present at the end of the coding sequence of tPDGFRa and the p16 or Pten sequences are thus present in the 39 UTR of this transcript.…”

“…Although design algorithms have improved in the last years, there are still difficulties in identifying effective and specific siRNAs and shRNAs. Therefore, it is still required to experimentally validate every siRNA or shRNA that is used, and for a specific gene 10 or more sequences may need to be tested to identify one RNAi molecule that is causing sufficient knockdown (Pei and Tuschl 2006;Krueger et al 2007;Wang et al 2009). …”

An Arrayed RNA Interference Genome-Wide Screen Identifies Candidate Genes Involved in the MicroRNA 21 Biogenesis Pathway

“…In addition, the library has undergone a 5-step bioinformatics filtering process to predict and eliminate siRNA duplexes with OTEs. 23 For quality control, the siRNA duplexes are purified, analyzed by MALDI-TOF mass spectrometry, re-annealed, and then checked by gel electrophoresis to ensure proper annealing.…”