siSPOTR: a tool for designing highly specific and potent siRNAs for human and mouse

Abstract

RNA interference (RNAi) serves as a powerful and widely used gene silencing tool for basic biological research and is being developed as a therapeutic avenue to suppress disease-causing genes. However, the specificity and safety of RNAi strategies remains under scrutiny because small inhibitory RNAs (siRNAs) induce off-target silencing. Currently, the tools available for designing siRNAs are biased toward efficacy as opposed to specificity. Prior work from our laboratory and others' supports the potential to design highly specific siRNAs by limiting the promiscuity of their seed sequences (positions 2-8 of the small RNA), the primary determinant of off-targeting. Here, a bioinformatic approach to predict off-targeting potentials was established using publically available siRNA data from more than 50 microarray experiments. With this, we developed a specificity-focused siRNA design algorithm and accompanying online tool which, upon validation, identifies candidate sequences with minimal off-targeting potentials and potent silencing capacities. This tool offers researchers unique functionality and output compared with currently available siRNA design programs. Furthermore, this approach can greatly improve genome-wide RNAi libraries and, most notably, provides the only broadly applicable means to limit off-targeting from RNAi expression vectors.

Figure 1.

Diagram of on- and off-target silencing by siRNAs. (A) Cartoon depicting a siRNA duplex designed to exhibit proper strand-biasing [i.e. strong G-C (blue) and weak A/G-U (red) binding at the respective 5′ and 3′ ends of the sense strand] and contain a low off-targeting potential seed (green highlight). Upon loading into RISC, the antisense strand may direct on-target silencing (intended) and off-target silencing (unintended). (B) Schematic highlighting the relationship between the frequencies of seed complement binding sites in the 3′-UTRome and the off-targeting potential for siRNAs.

Figure 2.

Effect of siRNA off-targeting potential on gene silencing capacity. A siRNA database composed of 2431 randomly designed siRNAs (targeting 31 unique mRNAs) and accompanying silencing data (36) was used to determine whether low off-targeting potential siRNAs (i.e. those having <2000 potential off-targets based on seed complement hexamer distributions in human RefSeq 3′-UTRs; blue) have similar capacities for gene silencing relative to the remaining 2068 siRNAs (mid-to-high off-targeting potentials; red). Approximately 1 in 4 of the low off-targeting potential siRNAs achieved >80% silencing (a commonly accepted threshold for potency), and overall their average efficiencies were comparable with the remaining siRNAs (∼66 and 69% knockdown, respectively; dotted lines).

Figure 3.

Formulation and distribution of POTS (potential off-targeting score). (A) Diagram illustrating the various seed site types. Seed sequences are highlighted in green. The adenosine corresponding to position 1 is highlighted in yellow and represents a defining feature for the 7A1 and 8mer binding site types. (B) The effect of seed site type on off-target silencing was determined using data from 54 microarray experiments testing unique siRNAs in HeLa cells. Cumulative distribution plots for gene expression values are shown for transcripts containing the relevant seed complement binding site types in their 3′-UTRs. Note: only transcripts containing singles sites for a given type and no other site types were considered. A shift to the left indicates an increased likelihood of being down-regulated relative to baseline transcripts (i.e. those lacking seed binding sites). ***Student t test indicated that the most significant divergence of the repressive potentials among these site types occurs at ≤ −0.3 log 2 fold-change (P < 0.001). (C) Schematic illustrating how POTS is calculated using seed site type frequency and PR values, shown above each respective site type. (D) The distribution of POTS scores—based on human 3′-UTR sequences—for all possible 16 384 heptamers is plotted. POTS < 50 is highlighted to indicate a relevant cut-off which is employed for purposes of this manuscript (refer to ‘Results’ section for further information regarding the relevance of this value).

Figure 4.

Workflow schematic for designing siRNAs targeting human PPIB using the siSPOTR algorithm. All possible 631 siRNAs targeting the human PPIB coding sequence (CDS) were filtered based on strand biasing [i.e. strong G–C (blue) and weak A/G–U (red) binding at the respective 5′ and 3′ ends of the sense strand] and GC-content, and the number of siRNAs passing each criteria are provided. Note: the asterisk denotes a cytosine base in the 3′ end of the target site; this base can be converted to a uridine to produce a weak G:U base-pairing in the resulting siRNA duplex. The heptamer seed sequence used for POTS determination is highlighted.

Figure 5.

Validation of siSPOTR: efficacy and off-targeting. (A) siRNA efficacy was evaluated using a database of 2431 randomly designed siRNAs with accompanying silencing data. The number of siRNAs passing each stage of our stepwise filtering process is indicated along with the number of potent sequences among them (i.e. those with >80% silencing efficacy. *siDesign Center (Dharmacon) was used for comparison by inputting the relevant target gene sequences into the online tool (N = 29) and intersecting the top 10 hits for each gene with the 2431 siRNAs. The box and whiskers plot shows the max and min gene silencing values (whiskers) and the upper and lower quartiles (box). The accompanying Venn diagram shows that siSPOTR identified five unique and effective sequences not present among the siDesign Top Hits. (B-D) Microarray data from experiments testing 40 unique siRNAs were used to assess the reliability of POTS as an indicator for off-targeting potential. (B) Heatmaps representing sequence-specific gene “suppression signatures” unique to each siRNA were generated using hierarchical clustering of significantly down-regulated genes (>3 standard deviations from the mean) among the datasets on a per target gene basis (i.e. GAPDH, PPIB and No Target), and columns were ordered and parsed by POTS for each group. (C) A qualitative representation of “suppression signature” size (i.e. sum of dark blue regions) for each column is shown. The red dotted line marks the largest “suppression signature” among the siRNAs with POTS < 50. (D) Spearman rank correlation of the POTS scores and numbers of down-regulated off-targets (i.e. transcripts with 3′-UTRs containing 7- and 8-mer seed-binding sites and ≤ −0.3 log 2 fold-change) observed for each siRNA is plotted. Linear regression lines, including correlation coefficients and P values, for all data points (dotted line) and black dots (solid line) are provided. Red dots represent overt outliers.

Figure 6.

Comparison of off-targeting potentials among shRNA libraries. A histogram and complementing table presenting the POTS distributions and genome-wide coverage of shRNA library sequences are shown for our “Low POTS” library (green) and the TRC library (red). The POTS distribution of all possible heptamers (blue) serves as a reference. The range encompassing 90% of all sequences for each shRNA library is indicated. Yellow highlights intersect to emphasize the coverage disparities at a key point; POTS < 50 provides a conservative cut-off for low off-targeting potential, and at least four siRNAs are desired for a given gene when generating a library or performing initial efficacy screening.