TECHNICAL NOTE

Off-target effects:
disturbing thesilence of RNAinterference(RNAi).

Abstract
Off-target effects occur when an siRNA is processed by theRNA-Induced Silencing Complex (RISC) and down-regulates
unintended targets. As these changes in gene expression canlead to measurable phenotypes (such as false positives) it isof great importance to understand the mechanism behind
off-targeting so that strategies can be developed to minimize
their effects. We have discovered that bioinformatics, novel
chemical modifications, and siRNA pooling significantly decrease
off-target effects. These three strategies are incorporated into
the ON-TARGETplus™ and SMARTpool™ siRNA reagents.

Introduction
RNA interference (RNAi) is a post-transcriptional gene regulatory
mechanism that can be mediated by endogenously encoded
small RNA molecules (microRNAs), or synthetic duplexes
referred to as small interfering RNAs (siRNAs). In both cases,
these molecules partner with the RNA Induced Silencing
Complex (RISC) to target messenger RNAs for degradation
and/or translation attenuation (Figure 1).
Since the discovery that siRNAs can enter the RNAi pathway
and mediate gene knockdown, researchers have invested
considerable effort in optimizing the technology for functional
genomics studies. As is the case with most new technologies,
the transition of RNAi into a research tool has had its
obstacles. Initial problems associated with inconsistent siRNAfunctionality were addressed by detailed studies that identified
functional attributes associated with duplex performance1,2.

Off-target effects: disturbing the silence of RNA interference (RNAi).

More recently, widespread, non-specific effects thatsiRNA00

Untreated

30

Lipid30

MAP2K2-460

MAP2K2-360

MAP2K2-290

MAP2K1-1

3. RISC-dependent off-target effects6-10

90

MAP2K2-1

2. Interferon response5120

MAP2K1-4

1. Lipid-mediated response4120

MAP2K1-3

appear to have three separate origins3150

Cell Viability (%)

Non-specific effects resulting from the introduction of siRNAmRNA Expression Level (%)

mediated knockdown studies have been observed.

150

MAP2K1-2

complicate the interpretation of data generated from siRNA-microRNA

RNAi
Complete sequence
complementarity

Partial sequence
complementarity

Figure 2: Off-target effects are sequence dependent and target
independent. A side-by-side comparison of toxicity and geneknockdown of eight siRNAs targeting two separate genes(MAP2K1 and MAP2K2). While all four duplexes in each cluster
provide equivalent levels of gene silencing ( > 80%), only a single
siRNA in each group is toxic (red bars).

History
Off-target effects were first described by Jackson andmRNA cleavage

mRNA translational
repression & siRNA

co-workers in 20039. Using genome-wide microarray profilingas a method of detection, the authors identified modest,
1.5- to 3-fold changes in the expression of dozens of genes

Figure 1: Small RNA molecules, microRNA and siRNA, regulatemRNA translation through RNA interference (RNAi). Dependingon the level of complementarity between the small RNA
molecule and mRNA, the mRNA will be silenced (complete) ortranslationally repressed (partial). siRNA off-targeting effects occur
through partial complementarity of the siRNA with unintended
mRNA targets.

following transfection of individual siRNA. The levels ofcomplementarity between the sense or antisense strand ofthe siRNA and the off-targeted genes varied considerably
and the overall off-target expression profile was unique foreach siRNA, suggesting a sequence-specific component to
the phenomena.

As demonstrated by Fedorov and colleagues, cationic lipids

Initially, these “modest” changes in off-target genetypically used to deliver siRNA can induce broad changes

expression led many to dismiss the event as inconsequential.

in gene expression profiles. Separately, multiple labs have

Unfortunately, this optimism was recently dispelled byobserved that particular sequence motifs and dsRNA lengths

reports that off-target effects could induce measurable

can induce the interferon response pathway in a cell-type

phenotypes. In a broad RNAi-based phenotypic screen

specific mannerdesigned to identify kinase regulators of the Hypoxia-. While these unanticipated effects can5,11

be eliminated by adopting stringent siRNA design filters andinducible factor 1 (HIF-1), Lin and co-workers discovered

optimizing lipid concentrations and compositions, a morethat the phenotypic effects induced by several of their top

challenging contributor to non-specific siRNA-mediatedcandidates were the consequence of off-targeting of HIF-13

gene modulation, off-target effects, presents the research

Similarly, Fedorov demonstrated that a significant fraction

community with a surprisingly complex problem6-10. Havingof siRNAs targeting the housekeeping gene diazepam

conducted considerable research on off-target effects, itbinding inhibitor (DBI) produced cell viability phenotypes

was found that a combination of bioinformatics, chemicalthat had characteristic features of off-target effects, suchmodifications, and siRNA pooling significantly reduce

as target independence, sequence dependence, and RISC-unintended targeting. These three innovations are included inreliance (Figure 2)12. These findings demonstrated a clear

ON-TARGETplus SMARTpool siRNA reagents.

shortcoming of the technology. False positives generated
during phenotypic screens can lead to false leads and thewww.revvity.com

2Off-target effects: disturbing the silence of RNA interference (RNAi).

unnecessary use of resources to explore non-productive
research tracts. From a therapeutic standpoint, off-targetAphenotypes represent a drawback that can severely limit

erent siRNAs
Smith-Waterman in Silico Analysis

applications of siRNAs as future therapeutic agents. For

Microarray Analysis

10,752 PredictedO

these reasons, understanding the mechanism underlying

347 ValidatedO

off-target effects and identifying measures that can enhancesiRNA specificity is critical for developing RNAi into a reliableB

research and therapeutic tool.
10,752Prevention

1,278
54

Early studies in RNAi established that single base pair347

mismatches between the siRNA and the target transcript
dramatically alter siRNA functionality13. From this result1

it was inferred that overall sequence identity played arole in siRNA specificity, thus leading to the adoption oflocal alignment algorithms, such as BLAST and SmithWaterman to minimize off-target effects. To achieve this,users identified optimal target sequences using one or more
selection criteria, and then filtered the resulting collection

23
7

experimentally validated≥ 79% sequence complementarity≥ 84% sequence complementarity≥ 89% sequence complementarity

to eliminate sequences that shared significant levels ofidentity (such as > 15/19 bp) with other genes in the target
genome. Birmingham and colleagues recently tested thisapproach by comparing a collection of in silico predicted
off-targets with a library of validated off-targets identified
by microarray gene expression profiling14. Using the SmithWaterman algorithm, the authors demonstrated that with the
exception of cases of near-perfect identity (such as 18/19,18/20, 19/20) sequence alignment tools failed to accurately
recognize off-targets (Figure 3). In general, the number of insilico predicted off-targets exceeded the true number by
1-2 orders of magnitude, regardless of whether one or bothstrands of the siRNA duplex were included in the analysis.
Moreover, only a small fraction of the experimentally

Figure 3: Sequence alignment tools fail to accurately recognize
off-target effects. A. Investigational approach—12 siRNAs
were computationally analyzed with the Smith-Waterman(S-W) alignment algorithm as well as experimentally tested by
microarray analysis. B. Venn diagram shows overlap between
347 experimentally identified off-targets and in silico off-target
genes predicted by the S-W alignment algorithm. Blue hexagon= 347 experimentally validated off-target genes for 12 siRNAs.
Red, green and black hexagons represent the number of offtarget genes predicted by S-W using 79% (such as 15/19 orbetter, 10,752 off-targets), 84% (such as 16/19 or better, 1,278
off-targets) and 89% (such as 17/19 or better, 54 off-targets)complementarity filters, respectively. The associated numbers(23, 7, and 1) represent the number of genes that are common
between the experimental and predicted groups at each of thecomplementarity filter levels (79%, 84%, and 89%, respectively).

validated off-targets were identified by in silico methods,
suggesting that overall sequence identity was a poor
predictor of the number and identity of off-targeted genes.
While the failings of sequence alignment tools left RNAi
users without a viable alternative for lessening off-target
effects, recent efforts have identified three approaches foreliminating this source of non-specific gene modulation.
These approaches include reducing siRNA concentration,
chemically modifying siRNA, and applying more rigorous
bioinformatics to siRNA design.

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3Off-target effects: disturbing the silence of RNA interference (RNAi).

siRNA concentration

TARGET

The first strategy originated from observations that offtarget effects were concentration-dependent6,9. As shown-targets

in Figure 4, as the concentration of the siRNA is reduced,
off-target effects faded. Unfortunately, attempts to employ
this approach with all but the most potent siRNAs on abroad scale have failed due to the simultaneous depletionof target gene knockdown along with off-target effects. This
impasse is resolved by adopting the very simple strategy
known as “pooling.” SMARTpool siRNA reagents pool four
10050

101.00.50.20.1

highly functional SMARTselection designed siRNAs targetingsiRNA C oncentration (nM)
≥ 75%0%% Knock down

the same gene. Studies show that strong on-target geneknockdown can be achieved with minimal off-target effects

Figure 4: Both off-target and on-target effects greatly diminish
with siRNA concentration. Heatmaps from microarray expression
studies of HeLa cells transfected with GAPDH (Accession#NM_002046) SMARTselection designed siRNA at various
concentrations (0.01-100 nM) at 24 hours. Black represents nochange in gene expression, while green represents greater than orequal to 75% reduction in mRNA level.

if a pool consisting of highly functional multiple siRNA issubstituted for individual duplexes. This finding is in contrast
to speculation that mixtures of siRNAs can compoundoff-target effects. As shown in Figure 5, while individual
duplexes delivered at 100 nM can induce varying numbersof off-targeted genes, transfection of the corresponding
SMARTpool siRNA (100 nM total concentration) induces only
a fraction of the total off-target profile. Importantly, thereduction in off-target gene modulation is achieved withoutTARGET-targets

jeopardizing target-specific knockdown.
siRNA Dharmacon™
On-Targetplus™ siRNA%Knockdown

969690siRNA 1siRNA 2siRNA 3siRNA 40%≥ 75%

94siRNApool

94% Knockdown

Figure 5: Pooling of four siRNA duplexes greatly reduces off-target
effects of individual siRNAs, while maintaining high levels oftarget silencing. Heatmaps from microarray expression studies ofHeLa cells transfected four Cyclophilin B (Accession #NM_000942)
SMARTselection designed siRNAs and the corresponding SMARTpool
siRNA reagent (Cat. #M-004606-00) at 24 hours. White represents nochange in gene expression, while green represents greater than orequal to 75% reduction in mRNA level.

9188888385PPARg

LMNAGAPDMEK1PPIB
0102030

4050Figure 6: ON-TARGETplus siRNA significantly reduce off-target
effects while maintaining high levels of target gene knockdown.
Five SMARTpool siRNA reagents (PPARg, LMNA, GAPD, MEK1, andPPIB), both unmodified, and modified with ON-TARGETplus siRNA(100 nM) were transfected into Dharmacon HEK293 cells using
Dharmacon™ DharmaFECT™ 1 Transfection Reagent.

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4Off-target effects: disturbing the silence of RNA interference (RNAi).

siRNA modification

Bioinformatics

A second approach to minimizing off-target effects

The last approach toward eliminating off-target effects

involves recent discoveries in the field of siRNA chemical

is associated with siRNA design. In the studies by bothmodification. In the process of performing a structure-Birmingham14 and Lin3, the investigators observed that off-function analysis of chemically modified siRNA, Ourtargeted genes often contained matches between the seedscientists have identified a unique combination ofregion of the siRNA (positions 2-7) and sequences in themodifications that eliminate as much as 80% of off-target

3’-UTR of the off-targeted gene. Furthermore, Birmingham

effects . The modification pattern includes both sense

found evidence that the likelihood of a gene being off-and antisense strand enhancements. On the sense strand,

targeted is elevated by the presence of multiple 3’-UTR seedmodifications have been added to impede strand entry

matches (Figure 7). microRNAs also utilize the seed region

into RISC, thus eliminating off-target effects by this strand.

to mediate gene knockdown, thus these findings intimate aTo address off-targets generated by the antisense strand,

strong mechanistic parallel between siRNA off-targeting and

key nucleotides that are essential for off-targeting weremicroRNA-mediated gene regulation. The role of the 3’-UTR

identified. Modification of these positions eliminates

seed match can be taken into consideration during siRNAthe majority of antisense strand off-target effects while

design, allowing users to minimize off-target effects.

15

preserving on-target knockdown (Figure 6). The combinationof both sense and antisense strand modifications has

These three principles have been incorporated into
ON-TARGETplus siRNA reagents, part of the pre-recently been validated15.

designed genome-wide collection of siRNA reagents. TheON-TARGETplus product line is comprised of four individual

Non-target Genes
5’55’OR F3’33’OR FmRNA

RF

3’5’
53’3’3’33’FRFOR FOR F5’ORO

5’5modified and rationally designed to minimize off-targetmRNAs iR N A

5’s iR N A
5’O

5’four siRNAs, and SMARTpool reagents which are chemicallyOORF
3’

120

Number of Seed Matches

100

advanced RNAi silencing products available and promise toprovide users with potent, highly specific gene knockdown.

Conclusions

Non-target Genes

80
60

For RNAi to become a more rigorous technology, issues

40

pertaining to functionality, specificity, and delivery must20

be addressed. Unintended gene modulation generated

05'-UTRORF

3'-UTRmRNA Region

Figure 7. Exact complementarity between the siRNA seed region
and the 3’-UTR (but not 5’-UTR or ORF) distinguishes off-targetedfrom non-targeted genes. A search for complementarity between
the siRNA antisense seed region (positions 2-7) and 5’-UTRs,ORFs, and 3’-UTRs of off-targeted (green) and non-silenced (blue)
genes was performed. The number of seed matches in the 3’-UTRregion is almost five-fold greater in the off-targeted genes than thenon-silenced genes.

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effects. ON-TARGETplus siRNA reagents represent the mostby off-target effects represents a prominent roadblock to
RNAi technologies in research, therapeutic, and diagnostic
settings. As described above, pooling strategies, chemical
modifications, and novel rational design filters cansignificantly reduce off-targets without jeopardizing on-target
gene knockdown. Implementation of these approaches,
available with ON-TARGETplus siRNA reagents, will greatly
reduce the presence of off-target phenotypes and enhanceresearcher confidence in the specificity of RNAi.

5Off-target effects: disturbing the silence of RNA interference (RNAi).

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Modification Increases Specificity of siRNA-mediatedGene Silencing. RNA (2006).

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