RESEARCH ARTICLE

The siRNA Non-seed Region and Its Target
Sequences Are Auxiliary Determinants of OffTarget Effects
Piotr J. Kamola1,2,3,4, Yuko Nakano5, Tomoko Takahashi5, Paul A. Wilson3, Kumiko UiTei5,6*
1 Department of Surgery and Cancer, Imperial College London, London, United Kingdom, 2 Safety
Assessment, GlaxoSmithKline R&D, Ware, Hertfordshire, United Kingdom, 3 Computational Biology,
GlaxoSmithKline R&D, Stevenage, Hertfordshire, United Kingdom, 4 Centre for Radiation, Chemical andEnvironmental Hazards, Public Health England, Harwell Science and Innovation Campus, United Kingdom,
5 Department of Biological Sciences, University of Tokyo, Bunkyo, Tokyo, Japan, 6 Department ofComputational Biology and Medical Sciences, University of Tokyo, Kashiwa, Chiba, Japan* ktei@bs.s.u-tokyo.ac.jp

Abstract
OPEN ACCESS
Citation: Kamola PJ, Nakano Y, Takahashi T, Wilson
PA, Ui-Tei K (2015) The siRNA Non-seed Region and
Its Target Sequences Are Auxiliary Determinants of
Off-Target Effects. PLoS Comput Biol 11(12):e1004656. doi:10.1371/journal.pcbi.1004656
Editor: Ilya Ioshikhes, Ottawa University, CANADA
Received: August 18, 2015
Accepted: November 16, 2015
Published: December 11, 2015
Copyright: © 2015 Kamola et al. This is an open
access article distributed under the terms of theCreative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source arecredited.
Data Availability Statement: All relevant data arewithin the paper and its Supporting Information files.
Funding: This work was supported by an EPSRC
CASE Award (EP/J502017/1, GlaxoSmithKline,
Imperial College London and Public Health England)
to PJK and Ministry of Education, Culture, Sports,
Science and Technology of Japan, Cell Innovation
Program (MEXT) grant to KUT. The authors would
like to thank and acknowledge GlaxoSmithKline forsupporting the visit of PJK to KUT’s group. The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.

RNA interference (RNAi) is a powerful tool for post-transcriptional gene silencing. However,the siRNA guide strand may bind unintended off-target transcripts via partial sequence complementarity by a mechanism closely mirroring micro RNA (miRNA) silencing. To better
understand these off-target effects, we investigated the correlation between sequence features within various subsections of siRNA guide strands, and its corresponding target
sequences, with off-target activities. Our results confirm previous reports that strength ofbase-pairing in the siRNA seed region is the primary factor determining the efficiency of offtarget silencing. However, the degree of downregulation of off-target transcripts with shared
seed sequence is not necessarily similar, suggesting that there are additional auxiliary factors that influence the silencing potential. Here, we demonstrate that both the melting temperature (Tm) in a subsection of siRNA non-seed region, and the GC contents of its
corresponding target sequences, are negatively correlated with the efficiency of off-target
effect. Analysis of experimentally validated miRNA targets demonstrated a similar trend,
indicating a putative conserved mechanistic feature of seed region-dependent targetingmechanism. These observations may prove useful as parameters for off-target prediction
algorithms and improve siRNA ‘specificity’ design rules.

Author Summary
Small interfering RNAs (siRNAs) are double stranded RNA molecules designed to perfectly match the sequence of a target gene and silence its expression. The function isexerted through the RNA interference (RNAi) pathway and has revolutionised biologicalresearch due to its ease-of-use and high potency. While siRNAs were initially believed tobe highly specific, they have subsequently been observed to interact with other, unintended

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siRNA Non-seed Region and Off-Target Effects

Competing Interests: PAW is a paid employee of
GlaxoSmithKline (GSK) and PJK's EPSRC Industrial
CASE doctoral studentships is co-sponsored by
GSK. This does not alter our adherence to the PLOS
policies on sharing data and materials. All otherauthors have declared that no competing interests
exist.

messenger RNAs. However, the mechanistic details of this process remain poorly understood, and there is a paucity of strategies and guidelines directed toward mitigating thisissue. To address this potential safety liability, we performed a comprehensive analysis ofsequence characteristics of siRNA duplexes and their target regions. Results from luciferase-reporter assays and global expression data confirmed previous observations that thesiRNA seed region is the primary determinant for off-target gene recognition and binding.
Furthermore, our analysis revealed the important contribution of siRNA non-seed region,and its corresponding target sequences, to the potency of off-target knockdown. Similar
results were observed in an equivalent evaluation of the miRNA-targeting mechanism,
suggesting that the correlating features arise through an evolutionary conserved mechanistic factor.

Introduction
RNA interference (RNAi) is a highly regulated, evolutionarily conserved mechanism of posttranscriptional gene regulation. Small interfering RNAs (siRNAs), the intermediate utilised bythis pathway, are 19 base pair (bp) long double stranded RNAs (dsRNAs), with 2 nucleotide(nt) 3’ overhangs (Fig 1A)[1]. When siRNAs are transfected into a cell, one of the siRNA
strands (guide strand) is incorporated into the RNA-induced silencing complex (RISC) while
the opposite strand (passenger strand) is degraded [2,3]. The activated siRNA-containing RISC
(siRISC) recognises and binds to the target transcript in a sequence-specific manner (Fig 1B).
The perfectly complementary region within the target transcript is then cleaved between the10th and 11th nucleotide relative to the 5’ end of the guide strand [4]. This elegant, endogenous
process has been extensively utilised in functional genomics studies and shows potential as atherapeutic platform [5].
When siRNAs were first shown to suppress gene expression in mammalian cells, the process
was thought to be highly specific [1]. This belief was later challenged by reports of numerous
sequence-specific off-target effects that could potentially induce a toxic phenotype [7,8]. The
unintended targets were reported to share partial sequence complementarity with the guidestrand and were found to primarily arise through a mechanism akin to miRNA targeting [9].
Specifically, 2nd to 7th/8th nt in the 5’ region (seed region) of siRNA recognises the unintended
target gene within the 3’UTR of mRNA sequence (Fig 1C)[10]. While unintended interactions
can also arise from the passenger strand entering RISC, in this study, ‘off-target effects’ willrefer specifically to the guide strand seed-dependent type.
The combined false positive results in genomic studies and potential safety liabilities in clinical application are driving the need to design specific, off-target-free siRNAs. Strategies suchas using multiple siRNAs with a shared-single target to dilute out siRNA-specific off-target
activity or adjusting the concentration based on on-target potency both have their technicallimitations [11,12], particularly in the case of therapeutics. Rational sequence design, with rules
based on mechanistic insight, offers a more practical solution. The RISC is a ribonucleoprotein
complex composed of multiple RNA-binding proteins, with Argonaute (Ago) playing a corerole in the silencing process [13]. Ago interacts with the guide strand of an siRNA through two
RNA-binding pockets in the PAZ (bound to 3’ end) and MID and PIWI (bound to 5’ end)
domains [14]. We have found that the highly effective siRNA sequence simultaneously satisfy
the following four rules; A/U at the 5’ end of the siRNA guide strand, G/C at the 5’ end of thepassenger strand, AU richness in the 5’ one-third region of the guide strand, and the absence oflong GC stretches [15]. Furthermore, the seed region is exposed on the Ago surface and is most

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siRNA Non-seed Region and Off-Target Effects

Fig 1. Graphical representation of siRNA molecule. (A) siRNA duplex with presumed binding sites for RISC proteins, Ago and TRBP [6]. (B) Interaction
pattern between siRNA guide strand and on-target transcript and (C) interaction between siRNA guide strand and its seed-dependent off-target transcript.
The mechanistic overview shows the guide strand of siRNA (A) as the strand which enters RISC, and acts in downstream silencing events (B and C).
Nucleotide position numbers mirror those used in the text and consequent figures.
doi:10.1371/journal.pcbi.1004656.g001

likely mediating the initial target recognition through Watson-Crick base-pairing [16]. We also
observed that the off-target efficiency is highly correlated with the thermodynamic stability ofprotein-free, seed-target duplex [15]. However, knockdown efficiency of seed-dependent offtargets with identical seed sequence but diverse non-seed region (positions 9–21) has shown
varying levels of knockdown. While the seed region was confirmed as the primary driving
force of off-target activity, our analysis also indicated a possible involvement of the non-seedregion and its corresponding target sequences in the observed response [17].
To comprehensively estimate the contribution of sequence-based features to the potency ofoff-target effects, we correlated the thermodynamic profile (i.e. melting temperature [Tm]) ofvarious subsections within siRNA guide strand with experimental results derived from off-target effect screens. Subsequently, we have utilized DNA microarray data generated using siRNAs that satisfy the sequence rules of highly effective siRNAs [15], to determine featuresassociated with target sequence that influence the efficiency of off-target downregulation.
Although little-to-no base-pairing is usually observed between siRNA non-seed region (nucleotides 9–20 in Fig 1C) and the corresponding off-target region, it was previously reported with
miRNAs that such partial base-pairing is an additional factor that influences the efficiency ofseed-dependent knockdown [18], and was thus included in our analysis. To assess the general

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siRNA Non-seed Region and Off-Target Effects

applicability of our findings for miRNA silencing, a dataset of experimentally verified miRNAtargets was also studied to determine potential conservation of observed features. Understanding and utilization of parameters of thermodynamic control, along with target recognition features, will provide a reliable and practical method to design effective and safe siRNAs, for bothresearch and therapeutic applications.

Results
Hybridization thermodynamics of three sub-regions in the siRNA duplex
correlate with potency of off-target effectTo investigate the contribution of hybridization thermodynamics to the efficiency of RNAinterference in detail, the correlation between melting temperature (Tm) of all possible
sequence subsections within siRNA duplex and corresponding off-target knockdown measured
by luciferase-reporter assays was plotted as two dimensional arrays (i.e. heatmap). Melting
temperature has been shown to be a strong predictor of thermodynamic stability of seed-target
duplex [17,19] and was the measure of choice in this study. The approach allowed us to distinguish three, well separated clusters which sequence composition correlated with RNAi effects(Fig 2): [1] positively correlated cluster in the siRNA seed region (nucleotides 2–8), [2] negatively correlated cluster in the non-seed region (specifically nucleotides 8–15), [3] positively
correlated 3’ termini position (nucleotides 18–19). The correlation coefficient (r) of the ‘seed’
cluster exceeded 0.7 in results from higher siRNA concentrations (Fig 2C and 2D). The highdegree of overlap between plots at different concentrations of siRNAs increases confidence thatthe observations are significant, and the results confirmed our previous report, which demonstrated strong positive correlation between Tm values of seed-target duplex and off-target
silencing efficiency. Longer sequence subsections that overlap a highly correlated shorter
region, are more likely to show higher correlation. It is thus appropriate to concentrate on thesubsections showing the highest correlation coefficient and the shortest length. Interestingly,
nucleotides 2–5 and not the whole seed (nucleotides 2–8) showed the highest positive correlation at all siRNA concentrations (from r = 0.51 at 0.05 nM to r = 0.78 at 50 nM, all pvalues < 0.01), suggesting that positions 2–5 may be the most crucial in the interaction
between the guide strand and off-target transcripts. The correlation between 3’ termini positions and knockdown efficiencies exceeded r = 0.7 in results from assays at 0.5 and 5 nM concentrations. Asymmetry in thermodynamics of 3’ and 5’ termini bases in the siRNA duplex
determines which strand will be preferential selected and loaded into RISC [17,20,21]. The 3’
terminal nucleotide is known to be anchored in the PAZ domain of the Ago protein [22]. However, it might be found that the 3’ terminal region is more strongly associated with RISC incorporation compared to 5’ terminal region. While the contribution of seed region binding and 3’
terminal base to silencing potency has been well established [15,17], our results revealed anovel negatively correlated cluster within the non-seed region of siRNA. The correlation
between Tm values at non-seed nucleotides 8–15 and off-target knockdown efficiencies was relatively lower but evident at all tested concentrations (r = ~-0.5, all p-values < 0.01).

GC content in the target sequence corresponding to siRNA non-seedregion affects off-target efficiency
While the thermodynamics analysis of siRNA duplex revealed significant contribution of nonseed sub-region to off-target effect, a question remained regarding the potential effect of offtarget sequence (especially at the off-target sub-section contributing to the observed negative
correlation in Fig 2). Although the interaction between guide strand and off-target transcripts

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siRNA Non-seed Region and Off-Target Effects

Fig 2. Correlation between Tm values of sequence subsections within siRNA duplexes and the corresponding off-target silencing efficiency. The
start of the subsection within a duplex is plotted on the Y axis (‘Start Position’) whereas the end of the subsection is plotted on the X axis (‘End Position’). The
position numbering mirrors that used in Fig 1. The analysis was performed separately for each siRNA concentration–(A) 0.05, (B) 0.5, (C) 5 and (D) 50 nM.
The siRNA sequences used in the analysis, together with corresponding knockdown percentages, are listed in S1 Table.
doi:10.1371/journal.pcbi.1004656.g002

is mainly driven by base-pairing at the seed region, there is a likelihood of limited interaction
between sequences at the siRNA non-seed region (Fig 1C). To comprehensively test this possibility, we analysed global gene expression changes induced by two siRNAs, siVIM-270 and
siVIM-805 designed against human Vimentin (VIM) gene, which were previously shown tohave potent off-target effects [17]. Putative off-targets were predicted based on perfect complementarity between the siRNA seed (nucleotides 2–8) and 3’UTR sequences derived from thelongest transcript of every protein-coding gene. While perfect complementarity between seed
and target is not always required for inducing off-target effect, analysis of off-target effects withvaried seed binding (and thus different binding energy) might introduce additional variability,

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siRNA Non-seed Region and Off-Target Effects

and further complicate the analysis. First, expression profiles of genes annotated as the putative
off-targets were compared to non-off-target genes without seed-matched regions. For both siRNAs, there is a clear separation between the two groups of transcripts, with seed-matchedsequences showing significantly lower expression profile relative to the remaining genes without seed-matched sequences (Fig 3A; siVIM-270, KS-test d-value = 0.21 with p-value < 2.2E16, Fig 3B; siVIM-805, KS-test d-value = 0.24 with p-value < 2.2E-16).
The level of downregulation was different among off-target transcripts, which may be partly
explained by the variation in the sequence of their non-seed region. While potential and limitedbase-pairing can be found between siRNA non-seed region and its corresponding target
sequences, the currently established calculation procedure of thermodynamic profiles is notapplicable for such discontinuous duplexes. The correlation profiles between all possible subsections within target sites corresponding to siRNA non-seed region and off-target effects werethus created based on sequence characteristics measured through GC content (Fig 3C and 3D).
While all off-target genes share the same sequence at positions 2–8, which is complementary tosiRNA seed region, the analysis included nucleotide at position 8 as it was found to be important in analysis shown in Fig 2. The correlation based on luciferase-reporter assay (Fig 2) was
calculated using % of repression whereas the results based on DNA microarrays (Fig 3) were
calculated using fold change. Lower fold change translates into greater downregulation andthus the correlations in the two analyses are opposite in direction. With the difference between
values being much smaller compared to the data shown in Fig 2, we have ranked and classified
them into ‘Low’, ‘Medium‘, ‘High’ and ‘Top’ groups using quantiles. The degree (i.e. strength)of correlation in both plots is strongest around the bottom region, indicating that sequencesubsections encompassing most of the non-seed region show the strongest positive relationshipwith fold change (Fig 3C and 3D). The patterns observed in global expression data derived
from samples treated with siVIM-270 and siVIM-805 confirmed the results from luciferasereporter assays–siRNA with high GC content in the non-seed region have ‘weaker’ off-target
effects. While the top correlations are not as high as those observed with the seed region (Fig2), they are both statistically and biologically significant (siVIM-270, r = 0.23, p-value = 1.2E13; siVIM-805, r = 0.17, p-value = 9.3E-07). Comparing results from both siRNAs revealed
that the common positions with top correlation were 8–15 (p-values < 0.01) followed by 9–15
(p-values < 0.01). The former was also identified as the optimal region in the luciferasereporter assay (Fig 2) and was thus selected as the sequence subsection for all further analyses.
The GC content of positions 8–15 was then used to divide off-target transcripts into fourgroups (based on quantiles), whose expression patterns were plotted as cumulative distributions (Fig 3E and 3F). From left to right, a clear pattern can be seen where targets with lower
GC content in the subsection corresponding to siRNA non-seed positions 8–15, show greater
downregulation. For each siRNA, the Kolmogorov-Smirnov test was performed between the‘Low’ and ‘Very High’ groups, and generated statistically significant results for both siRNAs(Fig 3E; siVIM-270, KS-test d-value = 0.29 with p-value of 7.9E-07, Fig 3F; siVIM-805, KS-test
d-value = 0.2 of p-value = 0.003). The higher number of predicted off-target transcripts for
siVIM-270 (particularly for off-target transcripts with 1 or 2 GC non-seed matches) most likely
determines the corresponding distinct profile.

Non-canonical interaction between guide strand non-seed region andthe corresponding target might influence off-target effects
As the off-target effects were identified based on perfect complementarity to the seed region(positions 2–8) and both terminal nucleotides of siRNA are known to be incorporated into
Ago protein (positions 1 and 21)[22], the potential base-pairing between siRNA and off-target

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siRNA Non-seed Region and Off-Target Effects

Fig 3. Seed and non-seed region-dependent off-target effect analyses for siVIM-270 and siVIM-805. (A, B) Expression profiles of off-target genes with
3’UTR sequences that perfectly match the corresponding siRNA seed region (i.e. off-targets) were compared to genes without such sequence. (C, D) The
correlation between GC content in all the subsections within mRNA targets of siRNA non-seed region (8–21) and fold change of off-target effect wascalculated in a similar manner to that shown in Fig 2. The correlation coefficient was grouped using quantiles as boundary values and target position
corresponds to the numbering shown in Fig 1. GC contents in the non-seed regions at positions 8–15 showed the highest correlation with off-target effect (Fig
3C; siVIM-270, r = 0.22, p-value = 1.13E-12, Fig 3D; siVIM-805, r = 0.15, p-value = 1.57E-05). (E, F) Off-target transcripts for siVIM-270 and siVIM-805 weredivided into four groups defined by the number of GC nucleotides in their non-seed regions (positions 8–15). Quantiles were used as the boundary values for

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siRNA Non-seed Region and Off-Target Effects

classification; ‘Low’ (GC content < 3), ‘Medium’ (GC content = 3), ‘High’ (GC content = 4) and ‘Very High’ (GC content  5)(out of a total of 8). The number ofoff-target genes was 1065 for siVIM-270 (E) and 823 for siVIM-805 (F).
doi:10.1371/journal.pcbi.1004656.g003

mRNA was determined via positions 9–20 (i.e. 3’ region). The number of GC and AU basepairs was calculated for each off-target sites, which allowed the transcript to be placed into five
distinctive groups; 2AU, 1AU, None (i.e. no found matches), 1GC and 2GC. Although most ofthese groups were relatively small, the distribution of each group for both siVIM-270 (Fig 4A)
and siVIM-805 (Fig 4B) showed a clear separation and comparable ordering. The higher numbers of AU matches resulted in greater downregulation while the higher numbers of GC
matches showed lower downregulation. The profile of off-target effects without possible nonseed binding was located between the AU and GC groups. To better understand this pattern,
average GC content of positions 8–15 was calculated for each group of transcripts, and plottedin the same order as observed in Fig 4A and 4B. A very strong relationship was observed
between the average GC contents of sequences corresponding to siRNA non-seed positions
8–15 and the off-target transcripts containing a particular non-seed binding pattern. Off-target
transcript with AU matches in region 9–20 have on average lower GC content in region corresponding to the siRNA positions 8–15, while those off-target transcript with GC matches haveon average higher GC content. The ordering based on average GC values of the groups shownin Fig 4C and 4D closely mirrors that in Fig 4A and 4B.
To further explore the relationship between off-target effects and GC content in the target
region corresponding to the siRNA positions 8–15, we investigated the cumulative distributionof off-target subgroups according to their GC content. The average GC content for each of thegroup was used to further divide the transcripts with a particular non-seed binding pattern
into two subgroups: one with GC contents less than the mean (‘Low’) and the other with GC
contents more than the mean (‘High’). Unfortunately, in majority of cases, the subgroups were
too small to perform an accurate analysis. We have thus limited our analysis to the two largest
groups (Fig 4E; siVIM-270, 1GC match, 133 off-target transcripts, Fig 4F; siVIM-270, 2GC
matches, 151 off-target transcripts). In both groups, the results clearly indicated that off-targets
with lower GC content in the region corresponding to siRNA positions 8–15 show a greater
downregulation. It is difficult to verify whether base-pairing in the siRNA non-seed region
occurs in a ‘justified’/canonical (e.g. binding occurs between nucleotides directly opposite eachother, as calculated in Fig 4A and 4B) or ‘slided’/non-canonical (e.g. binging occurs between
nucleotides not opposite each other, which could explain the pattern observed in all plots in
Fig 4) fashion. Nevertheless, it was apparent that the high GC contents in the off-target
sequences corresponding to siRNA positions 8–15 reduces the potency of off-target effects.
Based on the ordering of the groups in Fig 4A–4D it seems as the ‘justified’ base-pairing does
not fully occur/affect the seed dependent interaction. Rather, the groups indirectly represent
the GC content of the target region as regions with high GC content are more likely to havemore GC base-pairs with siRNA non-seed region (as shown in Fig 4C and 4D).

GC content of target sequence corresponding to miRNA non-seedregion may influence silencing potential
Since our present observations demonstrate that siRNA non-seed region may be a novel auxiliary determinant of siRNA-based off-target effect, we extended our investigation to miRNAbased silencing events. While the availability of accurate estimates of the silencing potential ofa particular miRNA is severely limited due to their endogenous origin, our analysis took advantage of a database that summarizes their experimentally confirmed targets [23]. The presence

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siRNA Non-seed Region and Off-Target Effects

Fig 4. Analysis of off-target effects based on sequence similarity between an siRNA non-seed region and its corresponding target sequences. (A,B) Cumulative distribution of off-target transcripts grouped by their non-seed base-pairing (Fig 4A; siVIM-270 off-target effects, 2AU (70), 1AU (52), None(27), 1GC (133), 2GC (151), Fig 4B; siVIM-805 off-target effects, 2AU (55), 1AU (52), None (20), 1GC (52), 2GC (46)). Off-target transcripts with more than2AU or 2GC match were omitted due to their low number. (C, D) The average GC contents for non-seed region (positions 8–15) were calculated for eachgroup of off-target transcripts. (E, F) The cumulative distribution of off-target transcripts of siVIM-270 with 1GC match (133 transcripts)(E) and 2GC matches(151 transcripts)(F), were sub-divided based on their GC contents at positions 8–15. ‘Low’ subgroups have GC content lower than the average while ‘High’subgroups have GC content higher than the average.
doi:10.1371/journal.pcbi.1004656.g004

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siRNA Non-seed Region and Off-Target Effects

Table 1. Contribution of miRNA non-seed region to gene silencing. The list of putative targets perfectly matching the miRNA seed region (positions 2–8)
was intersected with a list of experimentally validated miRNA targets [23]. Genes present on both lists were placed in the ‘Validated Targets’ group, whilegenes predicted to interact with miRNA but which have not been experimentally confirmed, were placed in ‘Remaining Genes’ group. The GC content in thepositions 8–15 was calculated for both groups and compared. The difference was calculated by subtracting values of the ‘remaining’ group from the values inthe ‘validated’ group.
miRNA ID

LengthValidated Targets (GC)Remaining Genes (GC)

Difference

hsa-miR-24-3p

22

3.77

4.49

-0.71

hsa-miR-93-5p

23

3.08

3.57

-0.5

hsa-miR-16-5p

22

2.61

3.08

-0.46

hsa-miR-98-5p

22

3.60

4.00

-0.39

hsa-miR-7-5p

23

3.51

3.90

-0.39

hsa-miR-17-5p

23

3.19

3.56

-0.37

hsa-miR-34a-5p

22

4.06

4.41

-0.35

hsa-let-7b-5p

22

3.66

4.00

-0.34

hsa-miR-192-5p

21

2.23

2.56

-0.33

hsa-miR-215-5p

21

2.22

2.54

-0.32

hsa-miR-744-5p

22

4.90

5.22

-0.32

hsa-miR-124-3p

20

3.44

3.74

-0.29

hsa-miR-21-5p

22

2.06

2.33

-0.27

hsa-miR-193b-3p

22

3.79

4.05

-0.26

hsa-let-7a-5p

22

3.72

3.98

-0.26

hsa-miR-149-5p

23

4.10

4.32

-0.22

hsa-miR-331-3p

21

4.46

4.67

-0.21

hsa-miR-155-5p

23

2.16

2.31

-0.15

hsa-miR-92a-3p

22

3.56

3.66

-0.1

hsa-miR-30a-5p

22

2.25

2.35

-0.09

hsa-miR-26b-5p

21

2.27

2.33

-0.05

hsa-miR-186-5p

22

2.40

2.37

0.03

hsa-miR-877-3p

21

4.12

4.07

0.05

hsa-miR-222-3p

21

2.54

2.41

0.13

hsa-miR-335-5p

23

4.01

3.79

0.22

Average

3.27

3.51

-0.24

doi:10.1371/journal.pcbi.1004656.t001

or absence of a target in the list formed the basis of our comparison. Twenty five miRNAs withthe highest number of experimentally-ascertained targets, including those with perfect andnon-perfect seed-complementarity, were selected for the analysis. Putative miRNA targets
were predicted as for siRNA (i.e. perfect complementarity between 3’UTR sequence and seedregion), and consequently intersected with the list of confirmed hits (miRNAs with at least 500
predicted targets were considered). Predicted off-target genes that were experimentally confirmed were placed in ‘Validated Targets’ group while predicted off-target genes that were notexperimentally validated were placed in ‘Remaining Genes’ group. Average GC content of theregion corresponding to the miRNA positions 8–15 for all genes was calculated separately foreach group and compared (Table 1). Of the 25 evaluated miRNAs, 21 had lower GC content(nucleotides 8–15) in the ‘Validated Targets’ group compared to the ‘Remaining Genes’ group.
While a more thorough approach is needed to accurately explore the relationship (e.g. target
prediction not limited to those with perfect seed complementarity, analysis based on correlation between GC content and quantified silencing potential, comparison between expression
profiles), the results provide evidence that the phenomenon observed with siRNA might represent a mechanistic feature which also regulate the efficiency in miRNA target silencing.

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siRNA Non-seed Region and Off-Target Effects

Discussion
To uncover the rational design rules of potent siRNAs, several studies have attempted to reveal
sequence-dependent features within the siRNA molecule that correlate with efficiency of RNAi
[21,24]. To better understand the regulatory mechanisms of off-target effects we exhaustively
analysed the contribution of each subregion within the siRNA duplex to clarify their relative
contributions to potency of off-target silencing. The significance of seed region (nucleotides2–8) thermodynamic stability in the process of off-target effects has been thoroughly explored[17]. Our detailed study revealed that the siRNA seed region encompassing nucleotides 2–5
has the highest positive correlation with off-target effect (Fig 2). This result may not be so surprising, since the crystal structure of human Ago2 revealed that nucleotides 2–6 of the guide
RNA are splayed out and stably positioned on the MID and PIWI domains in an A-form conformation for base pairing with target mRNAs [25]. This preorganization into A-form likegeometry significantly increases affinity for the target RNA [26,27]. However, there is a kinkbetween nucleotide 6 and 7 that breaks the A-form structure. These types of conformationalchange are considered to be related to the importance of base-pairing of nucleotide 7 with offtarget transcripts. Furthermore, base-pairing at nucleotide 8 can subsequently stabilise theduplex, although mismatch at this position does not perturb the duplex formation at positions
2–7. A more recent study has also revealed that initial target scan performed by Ago2 identifies
target sequences complementary to nucleotides 2–4 of the miRNA [28]. Thus, the efficiencies
of off-target effects might show a stronger correlation with the thermodynamic stabilities ofnucleotides 2–5 compared to those of nucleotides 2–8.
The majority of siRNAs used in this study have A/Us at 5’ ends of the guide strands (positions 1–2) and G/Cs at the 5’ end of the passenger strands (positions 18–19)(see S1 Table). The
calculated Tm values at nucleotides 1–2 showed no or little correlation with the efficiencies ofoff-target effects, while those at nucleotides 18–19 showed strong positive correlation (Fig 2).
One of the most widely accepted design algorithms of functional siRNAs for on-target repression is based on the asymmetrical thermodynamics of 5’ end of the guide strand (position 1)
and 5’ end of passenger strand (position 19). The strand with lower base-pairing stability at its
5’ end is preferentially incorporated and retained in the RISC [17,20,21]. Selective entry of theintended guide RNA strand into RISC significantly increases the efficiency of target gene cleavage, while entry of the opposite passenger strand is undesirable. Furthermore, A/U at the guidestrand 5’ terminal shows the 30-fold higher affinity for anchoring in the Ago pocket comparedto G/C [29]. While it is possible that 5’ terminal A/U of the guide strand is important foranchoring in the Ago pocket, the thermodynamic profile of nucleotides 1–2 may have little tono correlation with off-target efficiencies. Such an observation is also consistent with previous
experiments, indicating no contribution of A/U base pairs at position 1 to the off-target efficiency [9,15]. In contrast, the calculated Tm values at nucleotides 18–19 showed a high correlation with off-target silencing efficiencies across all siRNA concentrations (from r = 0.45 tor = 0.64, all p-values < 0.01). The results indicate that asymmetrical thermodynamics of bothends of siRNA duplex is predominantly regulated by the 3’ terminal nucleotide rather than the
5’ terminal nucleotide. Alternative explanation can be based on structural analysis, whichshowed relatively stronger electron density, indicating stronger binding, in the 5’ pocket of the
PIWI domain with G/C nucleotide [29] relative to 3’ binding pocket of the PAZ domain [25].
The most striking result of this study was that the siRNA non-seed region is also significantly involved in determining the efficiency of off-target effect. The negative cluster in siRNA
analysis (Fig 2) overlapped sequences positioned between nucleotides 8 and 17, which agrees
with structural studies showing that nucleotides 18–21 are held away from the target RNA[30,31]. The base-pairing stability in the non-seed region positioned at 8–15 showed the

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siRNA Non-seed Region and Off-Target Effects

highest negative correlation with off-target activity (r = ~0.5 across all concentrations, all pvalues < 0.01) in duplicated experiments using two different siRNAs, siVIM-270 and siVIM805. This tendency of correlation is apparent even at the lowest concentration of 0.05 nM (Fig
2A). The non-seed region did not, however, show an increase in correlation coefficient withincreasing siRNA concentration. In contrast, the effect of the seed region was dose-dependent,
suggesting that the initial interaction through seed-region is a rate-limiting step, with consequent interactions being of lesser hindrance. Interestingly, GC content in target sequence corresponding to siRNA non-seed region (nucleotides 8–15) showed the highest correlation with
off-target efficiencies (Fig 3), which appears to be independent of a ‘justified’ base-pairing
within the non-seed region (nucleotides 9–20)(Fig 4). Nevertheless, results from both analyses
showed non-seed region 8–15 as the highest negatively correlating feature, which suggest thatinteraction between those regions might be behind the effect. It is thus likely that the regionsinteract via base-pairing with the nucleotides not exactly opposite each other. Such event might
disrupt the seed-target duplex by pulling apart both strands. The further away from the seedregion, the less effect any potential base pairing may have on the stability of the seed duplex.
Alternatively, high GC contents of both regions might interfere with initial recognition (e.g.
binding competition between seed and non-seed), affect protein binding (with RISC or otheras yet uncharacterised molecule) or alter downstream events, which translate into reduced
OTE knockdown.
A recent comprehensive study of miRNA target interactions reported widespread noncanonical interaction between miRNA non-seed region and its target mRNA sequences [32].
Similar to siRNA, miRNA identifies and binds to its targets primarily through its seed region.
Our analysis based on dataset of experimentally validated miRNA targets [23] indicate that validated targets have on average lower GC content in the sequence corresponding to miRNA
non-seed region (nucleotides 8–15) relative to predicted off-target effects that were not experimentally validated (Table 1). Given these combined observations we propose that the non-seedregion may function through a conserved mechanism shared by both miRNA and siRNA
molecules.
We have previously reported that highly functional siRNAs satisfy the following four rules[15]: (1) A or U at 5’ end of siRNA guide strand, (2) G or C at 5’ end of siRNA passenger
strand, (3) AU richness at 5’ one-third region of siRNA guide strand, (4) absence of any GC
stretches more than 9 nt in length. Based on our current and previous analyses [17], we recommend that additional emphasis is placed on three design aspects tailored specifically towards
minimising off-target effects (summarised in Fig 5): (3’) low Tm in the siRNA seed region(nucleotides 2–5 to 2–7), (5) high Tm in siRNA duplex/high GC content in the guide strand
within non-seed region (nucleotides 8–15), (6) high average GC content for target sequences
corresponding to nucleotides 8–15 of the siRNA guide strand. Although the first ‘specificity’rule (3’) overlaps with the third functional siRNA rule (3) the revised recommendation is more
precise regarding the location of AU base-pairs (nucleotides 2–5 to 2–7). While the first two
specificity indications, (3’) and (5), will be easily satisfied by calculating Tm values in the selection step of a particular siRNA duplex, the third (6) is inevitably varied and will require a rigorous off-target effects analysis to be performed to select the most promising designs. This can beaccomplished by extracting the sequences upstream of the sites predicted as targets for siRNAseed region and calculating their average GC content. Data presented in Figs 2–4 indicate thatadhering to these rules will reduce global off-target effect liability.
RNAi is a cascade of protein-RNA interaction events, with reaction rate and efficiency ofeach step directly affecting the downstream event. The potency of siRNA-mediated knockdown
is largely dependent on siRNA sequence, with particular features within the molecule affecting
the rate of reaction steps [24]. This phenomenon has led to the general acceptance of several

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siRNA Non-seed Region and Off-Target Effects

Fig 5. Summary of ‘highly effective’ and ‘off-target effect-reduced’ design rules for siRNA molecules. Based on our previous analysis and the results
shown in Figs 2–4, we recommend designing molecules with: (1) A or U at position 1, (2) G or C at position 19, (3) low predicted Tm/AU richness at positions
2–7, (4) absence of GC stretches longer than 9 nt, (5) high predicted Tm in siRNA duplex/high GC content in guide strand at positions 8–15, and (6) highaverage GC content for all off-target sequences corresponding to positions 8–15 in the siRNA non-seed region.
doi:10.1371/journal.pcbi.1004656.g005‘design rules’ of functional siRNA [33], which may reflect a mechanistic feature of the RNAi.
While high on-target knockdown is essential, it is important to address the problem of unintended off-target effects [34–36]. Previously reported factors which contribute to the potencyof off- (strength of base-pairing between seed region and target) and on-target (asymmetry inend stability of siRNA duplex) functions [15] were confirmed in this study. Additionally, Tm in
the siRNA duplex at non-seed positions 8–15 and the GC content of target sequence corresponding to the siRNA non-seed positions 8–15 were found to be correlated with the off-target
downregulation. The results were also replicated in a database of confirmed miRNA targets
suggesting that the contribution of the non-seed region to silencing event arise through a conserved mechanistic feature of seed-dependent targeting mechanism.

Methods
Oligonucleotide sequence
Data from our previously reported off-target effect assays of 32 siRNAs was pooled, averaging
values for duplicate measurements, setting the maximum luciferase response at 100% andtransforming into knockdown % to facilitate interpretation [17,19]. The measurements are
based on reporter plasmids expressing Renilla luciferase gene with three tandem repeats
inserted into the 3’UTR region of the mRNA. The repeat sequence contains a region of perfectcomplementarity to the seed region of corresponding siRNA (positions 2–8 in Fig 1C), while
the remaining regions are intentionally devoid of homology to the non-seed region. The
sequence details of all siRNAs, together with corresponding % knockdown at each concentration, are listed in S1 Table.

Thermodynamics analysis
Thermodynamic profiles were created by evaluating every subsection within the guide strandof siRNA (sequences are listed in S1 Table). For every subsection a Tm was calculated using anearest neighbour model with thermodynamics parameters for RNA-RNA interaction based

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siRNA Non-seed Region and Off-Target Effects

on Xia et al. [37]. As the comparison is performed for ‘internal’ sequence, the helix initiation
factor and symmetry correction were both omitted. The final Tm equation used was:Tm ð CÞ ¼

1000 x DH 273:15 þ 16:6 log½Naþ 
DS þ In Ct4

Where ΔH is the sum of enthalpy changes (kcal mol-1), ΔS is the sum of entropy changes (kcal
mol-1 K-1), Ct is the total molecular concentration of the siRNA (variable) and [Na+] is thesodium ion concentration (100 mM).

Microarray data analysis
The microarray analysis was performed according to our previous report [17]. Briefly, HeLa
cells were transfected with either of 50 nM siVIM-270 or siVIM-805. Total RNA was purified
and hybridized to Human Genome U133 Plus 2.0 GeneChip (Affymetrix). RNA from mocktransfected cells was used as a control. The transcript expression value was calculated using
Microarray Suite 5.0 (MAS5)[38] with quantile normalization [39,40]. The 3’UTR sequence
used in off-target transcript prediction and analysis were generated based on gene sequences
and coordinate, and probe mapping annotation available in the Ensembl repository [41]. Several steps were taken to ensure accuracy and non-redundancy of the downstream analysis:probes overlapping multiple genes were removed, probes annotated as ‘absent’ or ‘marginal’ by
MAS5 analysis suite were omitted, a single probe was selected per gene when several were available, a single 3’UTR sequence was selected per gene (based on the longest transcript) in case ofmultiple splice variants with annotated UTR regions. To reduce potential variability, a gene
was treated as a target only when its 3’UTR sequence had a fragment perfectly matching theseed region of the siRNA or miRNA molecule (positions 2–8). Genes with more than one putative prediction were also excluded from further analysis to ensure independence of analysedsamples. A custom script was prepared to locate sites within 3’UTR sequences with perfectmatch to siRNA seed region and extract the flanking sequences for further analyses. Potential
base-pairing in the non-seed region was calculated based on region 9–20 as the nucleotide inposition 21 is hidden in the PAZ domain of the Ago protein [22]. GC content was calculated
for all possible subsections within predicted targets corresponding to siRNA guide strand nonseed region (positions 8–21)(Fig 1C), mirroring the analysis method of the guide strand
described in the ‘Thermodynamics Analysis’. Results for particular sets of off-target effects
were plotted as empirical cumulative distribution functions, which is a convenient method ofvisualizing differences in distribution and expression patterns between analysed groups. Kolmogorov-Smirnov test (KS-test) was used to quantify the separation between plotted cumulative distributions and test its significance.

Correlation and visualization
The strength of a linear association between melting temperatures of all possible subsections
within siRNA duplexes and their off-target efficiencies measured by Renilla luciferase assay
was calculated separately for all siRNA concentration (0.05, 0.5, 5 and 50 nM). Similarly, thecorrelation between GC contents of all possible target sequences corresponding to the various
siRNA non-seed subsections, and the fold changes derived from DNA microarray experiments,
was calculated for siVIM-270 and siVIM-805. The profiles were plotted as heatmaps, as a convenient method to represent and interpret two dimensional data (Figs 2 and 3C and 3D). The
nucleotide position numbering is identical to that used in Fig 1 (Y axis–start position on theguide strand/target, X axis–end position on the guide strand/target). Two different correlation
tests were used: Pearson product-moment correlation and Spearman rank correlation

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siRNA Non-seed Region and Off-Target Effects

coefficient. Though the results from both methods were largely overlapping, the latter was chosen for final plotting as it generated more consistent and defined clusters, most likely due to themonotonic nature of the data. All statistical calculations and plotting were performed in R.

Supporting Information
S1 Table. A list of siRNA molecules used in the heatmap analysis.
(DOCX)

Author Contributions
Conceived and designed the experiments: PJK KUT. Performed the experiments: YN TT. Analyzed the data: PJK. Contributed reagents/materials/analysis tools: PJK KUT. Wrote the paper:
PJK PAW KUT.

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