RNAi Design, Validation and Target Screening

Since Tuschl et al. published the first empirical guidelines on how to design effective siRNA [1], the most significant advancement (based on the understanding of biochemical mechanisms of RNAi such as how RISC is assembled) is the recognition of asymmetric thermostability of the 5’ end of the antisense strand (AS) relative to that of the sense strand (SS) [2, 3]. siRNAs with an A/T-rich AS 5’ end can be more easily integrated into RISC. By biasing against the sense strand for RISC loading, the off-target effects due to the presence of the SS (as one of the sources of off-targets effects) can also be minimized. In recent years datasets of increased number of siRNAs and shRNAs became available and statistical analysis suggested additional rules for RNAi design. These newer rules in general define the siRNA prediction parameters in more detail, for instance, the number of bases of the 5’ ends that should be included when calculating asymmetric thermostability, base preferences at each particular position, and the identity of the 2 nt 3’ overhang [4, 5]. Computer programs and websites are developed based on these features also resulting from NIH funded research through universities and organizations. Among the well-known ones, Design of SIRna (DSIR at biodev.extra.cea.fr/DSIR/DSIR.html) and the shRNA search program at the Broad Institute (broadinstitute.org/genome_bio/trc/publicSearchForHairpinsForm.php) are freely available.

Several companies such as Open Biosystems, System Biosciences, Dharmacon/ThermoFisher, Sigma-Aldrich, Invitrogen/LifeTech, provide premade RNAi reagents against various numbers of human and rodent genes. Although some product lines from these suppliers are labeled as validated RNAi reagents, apparently only one reveals clone sequences and only a few hundred among the claimed 4,500 shRNA clones. It is not possible to find what shRNAs are used against any target gene from most companies even though many of them claim to have a few hundred pre-validated constructs. Some of them may provide additional information upon purchase.

Even with the recent advancement of RNAi design technologies, prediction of effective RNAi is still far from accurate. Depending on the datasets used to score the success rates of the programs at DSIR, Broad or any other software, the general consensus is that about 50% of predicted RNAi target sequences will be effective, resulting in better than 70% gene knockdown. Allele Biotech uses a software that was trained with known RNAi results to predict siRNA target candidates on a given mRNA, and then applies an additional set of rules to pick the most promising candidates. Off-target effects caused by partial-matching between AS strand and untended targets are reduced by searching the chosen site against the NCBI gene base. The basic rules Allele Biotech uses include most currently known ones and are similar to what are listed by The RNAi Consortium (TRC) program at the Broad Institute.

Criteria for RNAi design:
(1) Overall GC content is between 30-55%
(2) The 4 bases at the 5’ of AS is more AT-rich than those of the SS
(3) The first base of AS and SS 5’ is preferably A/T and G/C, respectively
(4) “U” is preferred at the 10th position of the antisense from the 5’ end
(5) “C” is to be avoided as the last base of an overhang
(6) Avoid 4-nt mono-nucleotide regions
(7) Avoid 6-nt GC-rich regions
(8) If possible, do not include those with apparent secondary structures

These selected rules are based on a number of publications (for example, [4-6]), but it is impossible to include all known rules, many of which conflict with each other. In case of conflicting rules we rely more on recent discoveries and our own experience from providing RNAi service during the past 8 years.

Allele Biotech provides RNAi validation and screening services to customers using synthetic siRNA, linear DNA cassettes with engineered Pol III promoter, and shRNA expressing lentiviral vectors in high throughput formats. In a unique design, all RNAi target candidate sequences of a gene transcript are fused consecutively to a bright green fluorescent protein, mWasabi, on a lentiviral vector. Instead of analyzing gene silencing by QPCR, the initial selection of effective RNAi can be performed by measuring fluorescence.

RNAi screening has been conducted to identify correlations between gene functions and cellular phenotypes such as synthetic lethality among DNA damage signaling and repair pathway factors. Successfully performing high throughput screenings requires capabilities of efficient RNAi design, viral packaging, fluorescent proteins, and advanced cell culture and analysis techniques. In addition to these capabilities, Allele’s RNAi services are provided with access to commercial use of Allele’s own patents on Pol III promoter driven shRNA expression, and licensed patents on lentiviral vector, packaging, and fluorescent proteins.

    New Product/Service of week Nov 16-22, 09:

RNAi validation/screening service.

1. Tuschl, T., P.D. Zamore, R. Lehmann, D.P. Bartel, and P.A. Sharp, Targeted mRNA degradation by double-stranded RNA in vitro. Genes Dev, 1999. 13(24): p. 3191-7.
2. Khvorova, A., A. Reynolds, and S.D. Jayasena, Functional siRNAs and miRNAs exhibit strand bias. Cell, 2003. 115(2): p. 209-16.
3. Schwarz, D.S., G. Hutvagner, T. Du, Z. Xu, N. Aronin, and P.D. Zamore, Asymmetry in the assembly of the RNAi enzyme complex. Cell, 2003. 115(2): p. 199-208.
4. Vert, J.P., N. Foveau, C. Lajaunie, and Y. Vandenbrouck, An accurate and interpretable model for siRNA efficacy prediction. BMC Bioinformatics, 2006. 7: p. 520.
5. Zhou, H. and X. Zeng, Energy profile and secondary structure impact shRNA efficacy. BMC Genomics, 2009. 10 Suppl 1: p. S9.
6. Ui-Tei, K., Y. Naito, F. Takahashi, T. Haraguchi, H. Ohki-Hamazaki, A. Juni, R. Ueda, and K. Saigo, Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. Nucleic Acids Res, 2004. 32(3): p. 936-48.

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Wednesday, November 18th, 2009 Fluorescent proteins, RNAi patent landscape

1 Comment to RNAi Design, Validation and Target Screening

blogadmin
February 4, 2010

Off-target remains the number one concern for people performing RNAi technologies. A comprehensive review was recently published in Nature Reviews Drug Discovery, “Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application”

link: http://www.nature.com/nrd/journal/v9/n1/full/nrd3010.html

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