Gene expression silencing with ‘specific’ small interfering RNA goes beyond specificity - a...

Gene expression silencing with ‘specific’ small interferingRNA goes beyond specificity – a study of key parametersto take into account in the onset of small interfering RNAoff-target effectsSebastien Vankoningsloo1, Francoise de Longueville2, Stephanie Evrard2, Pierre Rahier2, AndreeHoubion1, Antoine Fattaccioli1, Melanie Gastellier1, Jose Remacle2, Martine Raes1, Patricia Renard1

and Thierry Arnould1

1 Laboratoire de Biochimie et Biologie Cellulaire, University of Namur (F.U.N.D.P), Belgium

2 Eppendorf Array Technologies, Namur, Belgium

RNA interference (RNAi) is a recently discovered

gene-silencing pathway [1] triggered by dsRNA-

derived molecules such as small interfering RNAs

(siRNAs) or microRNAs, leading to the degradation

of a particular mRNA (slicing) or to repression of

translation [2,3]. Thereafter, the use of chemically

synthesized siRNAs as a new loss-of-function strategy

exploded during the last decade, mainly because the

RNAi pathway is believed to apply to all genes in

several species. Therefore, siRNAs became useful

tools for the silencing of genes playing a role in many

biological processes.

The extensive use of siRNAs, designed to match

perfectly with a particular mRNA target, is based on

the assumption of their high specificity. Indeed, it was

initially suggested that only one mismatch could abol-

ish the siRNA-induced slicing activity [4]. However,

several studies using DNA microarray and ⁄or compu-

tational approaches have shown that siRNAs can

generate side effects, by inducing the degradation of

nontarget mRNAs sharing sequence homology with

the siRNA seed region, or by repressing the translation

of unintended proteins [5–10]. Indeed, in some circum-

stances, and especially in immune cells, siRNAs are

Keywords

cell type; gene expression; off-target

effects; silencing; siRNA

Correspondence

T. Arnould, Laboratoire de Biochimie et

Biologie Cellulaire, University of Namur

(F.U.N.D.P), 61 rue de Bruxelles, 5000

Namur, Belgium

Fax: +32 81 724125

Tel: +32 81 724129

E-mail: [email protected]

(Received 10 January 2008, revised 12

March 2008, accepted 19 March 2008)

doi:10.1111/j.1742-4658.2008.06415.x

RNA-mediated gene silencing (RNA interference) is a powerful way to

knock down gene expression and has revolutionized the fields of cellular

and molecular biology. Indeed, the transfection of cultured cells with small

interfering RNAs (siRNAs) is currently considered to be the best and easi-

est approach to loss-of-function experiments. However, several recent stud-

ies underscore the off-target and potential cytotoxic effects of siRNAs,

which can lead to the silencing of unintended mRNAs. In this study, we

used a low-density microarray to assess gene expression modifications in

response to five different siRNAs in various cell types and transfection con-

ditions. We found major differences in off-target signature according to:

(a) siRNA sequence; (b) cell type; (c) duration of transfection; and (d)

post-transfection time before analysis. These results contribute to a better

understanding of important parameters that could impact on siRNA side

effects in knockdown experiments.

Abbreviations

DF, DharmaFECT1; IFN, interferon; IRF, interferon responsive factor; LAMP2, lysosome-associated membrane protein 2; NT, nontargeting;

RISC, RNA-induced silencing complex; RNAi, RNA interference; siRNA, small interfering RNA; SREBF1, sterol-responsive element-binding

protein 1; TLR3, Toll-like receptor 3.

2738 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS

also able to trigger an ‘interferon (IFN) response’

through the activation of cytosolic proteins such as

dsRNA-dependent protein kinase and ⁄or membrane

receptors such as Toll-like receptor 3 (TLR3), leading

to a general repression of translation [11]. An inflam-

matory response was also observed in primary human

chondrocytes transfected with siRNAs [12]. These con-

siderations cast some doubts on the validity of several

results previously published – particularly on the strict

specificity for targets supposed to be responsible for a

biological response – and highlight the importance of

studies intended to increase our understanding of the

extent of siRNA nonspecific effects and the conditions

under which they occur.

In this work, we used a low-density DNA micro-

array that allows gene expression analysis of 273 genes,

in order to determine the off-target effects generated

by five siRNAs in different cell types and experimental

conditions. We first studied the side effects of two

different siRNAs targeting the sterol-responsive

element-binding protein 1 (SREBF1) mRNA encoding

a transcription factor, two different siRNAs targeting

the lysosome-associated membrane protein 2 (LAMP2)

mRNA encoding a lysosomal glycoprotein, and a non-

targeting (NT) siRNA. Gene expression profiles were

determined for each siRNA in transiently transfected

human osteosarcoma 143B cells, lung adenocarci-

noma A549 cells, and lung IMR-90 fibroblasts.

Furthermore, in 143B cells, we studied the effects of

different transfection and post-transfection periods on

the modifications in gene expression triggered by

siRNA.

Results

Verification of siRNA efficiency

We used two siRNAs designed to specifically knock

down expression of the transcription factor SREBF1

(SREBF1 ⁄ siRNA1 and SREBF1 ⁄ siRNA2), and two

siRNAs targeting the transcript coding for the lyso-

somal glycoprotein LAMP2, which is not known to be

directly involved in transcription events (LAMP2 ⁄ siR-

NA1 and LAMP2 ⁄ siRNA2). The particular targets

were chosen on the basis of their interest for other

research programmes in our laboratory. The efficiency

of these siRNAs at concentrations ranging from 5 nm

to 100 nm was first demonstrated by real-time PCR in

143B, A549 and IMR-90 cells (Fig. 1). The choice of

these cell types was based on the selection of trans-

formed or nontransformed cells expressing or not

expressing the siRNA-responsive TLR3 receptor.

Indeed, 143B and A549 are tumor-derived cell lines,

the latter being reported to express TLR3 [13], whereas

IMR-90 is a nonimmortalized cell type. We observed

that the transfection reagent DharmaFECT1 (DF) has

no or little effect on the abundance of SREBF1

(Fig. 1A,B) or LAMP2 (Fig. 1C) transcript in these

cell types. The SREBF1-specific siRNAs (100 nm) were

both very efficient at decreasing SREBF1 transcript

abundance, with reductions of 81%, 66% and 69% for

SREBF1 ⁄ siRNA1 and reductions of 79%, 78% and

71% for SREBF1 ⁄ siRNA2 in 143B, A549 and IMR-

90 cells, respectively (Fig. 1A). Under these conditions,

the effect of the SREBF1-targeting siRNA was pro-

longed, at least, up to 72 h post-transfection, as dem-

onstrated in 143B (Fig. 1B). Both LAMP2-specific

siRNAs (100 nm) were also efficient, as the abundance

of the corresponding transcript was decreased by 89%,

78% and 86% for LAMP2 ⁄ siRNA1 and by 64%, 58%

and 66% for LAMP2 ⁄ siRNA2 in 143B, A549 and

IMR-90 cells, respectively (Fig. 1C). In contrast, an

siRNA with an NT sequence did not dramatically alter

the abundance of SREBF1 or LAMP2 mRNAs in

these conditions. The main observed effect was even a

slight increase in the abundance of SREBF1 transcript

in each cell type.

The efficiency of SREBF1 ⁄ siRNA1 was also investi-

gated at the protein level by western blotting analysis

of SREBF1 abundance in 143B cells (Fig. 2). We

found a concentration-dependent and time-sustained

decrease in SREBF1 protein abundance in 143B cells.

The signals present at 48 h and 72 h after cell transfec-

tion with SREBF1 ⁄ siRNA1 (100 nm), apparently not

correlated with mRNA levels (Fig. 1B), probably result

from differences in exposure times during western blot-

ting. We also observed a slight increase in SREBF1

protein level triggered in the presence of the NT

siRNA, in agreement with the slight increase in

SREBF1 mRNA observed under the same conditions

(Fig. 1A,B).

Off-target signatures elicited by five siRNAs in

three different cell types

We next studied the effects of DF and of the five

siRNAs at 100 nm on gene expression. The side effects

of these siRNAs were systematically investigated in

143B (Fig. 3), A549 (Fig. 4) and IMR-90 cells (Fig. 5)

transiently transfected for 24 h before total RNA

extraction and microarray analysis. Please note that

the scales are different for each heat map. Each experi-

ment was performed on biological triplicates, and the

complete lists of relative transcript level values and

corresponding standard deviations are provided in sup-

plementary Tables S1–S12. Several transcripts were

S. Vankoningsloo et al. siRNA off-target effects in different cell types

FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS 2739

0.0

0.5

1.0

1.5

2.0

2.5

Rel

ativ

e S

RE

BF

1 m

RN

A a

bu

nd

ance

240 7248 240 7248 240 7248

DF SREBF1/siRNA1(100 nM)

NT siRNA(100 nM)

Time post-transfection (h)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Rel

ativ

e S

RE

BF

1 m

RN

A a

bu

nd

ance

DF 100 20 5

SREBF1siRNA1

(nM)

100 20 5

SREBF1siRNA2

(nM)

100 20 5

NT siRNA (nM)

DF 100 20 5

SREBF1siRNA1

(nM)

100 20 5

SREBF1siRNA2

(nM)

100 20 5

NT siRNA (nM)

DF 100 20 5

SREBF1siRNA1

(nM)

100 20 5

SREBF1siRNA2

(nM)

100 20 5

NT siRNA (nM)

143B A549 IMR90

Rel

ativ

e L

AM

P2

mR

NA

ab

un

dan

ce

143B A549 IMR90

DF 100 20 5

LAMP2siRNA1

(nM)

100 20 5

LAMP2siRNA2

(nM)

100 20 5

NT siRNA (nM)

DF 100 20 5

LAMP2siRNA1

(nM)

100 20 5

LAMP2siRNA2

(nM)

100 20 5

NT siRNA (nM)

DF 100 20 5

LAMP2siRNA1

(nM)

100 20 5

LAMP2siRNA2

(nM)

100 20 5

NT siRNA (nM)

A

B

C

Fig. 1. Effect of the SREBF1-targeting and the LAMP2-targeting siRNAs on SREBF1 and LAMP2 mRNA levels analyzed by real-time PCR in

143B, A549 and IMR-90 cells. (A) 143B, A549 and IMR-90 cells were incubated for 24 h with DF or transfected for 24 h with SREBF1 ⁄ siR-

NA1, SREBF1 ⁄ siRNA2 or the NT siRNA at the indicated concentrations before RNA extraction, reverse transcription, and amplification in the

presence of SYBR Green and specific primers. (B) 143B cells were incubated for 24 h with DF or transfected for 24 h with SREBF1 ⁄ siRNA1

or the NT siRNA at 100 nM. RNA was extracted 0, 24, 48 and 72 h post-transfection and processed for real-time PCR analysis. (C) 143B,

A549 and IMR-90 cells were incubated for 24 h with DF or transfected for 24 h with LAMP2 ⁄ siRNA1, LAMP2 ⁄ siRNA2 or the NT siRNA at

the indicated concentrations before RNA extraction and processing for real-time PCR analysis. TBP was used as a housekeeping gene for

data normalization. Results are expressed as relative SREBF1 or LAMP2 transcript abundance in treated cells as compared to untreated con-

trol cells (n = 1).

siRNA off-target effects in different cell types S. Vankoningsloo et al.


not detected, most probably because of their absence

or low abundance: depending on the experiment, the

total number of mRNAs detected ranged between 185

and 260 out of 273. The results discussed here below

are only related to genes for which mRNA relative

abundance in siRNA-transfected cells was found to be

significantly different when compared with the mRNA

abundance determined in DF-treated cells.

First, we observed that treatment with DF alone

affected the expression of a few genes, especially in

IMR-90 cells, such as IGFBP3 (insulin-like growth fac-

tor-binding protein 3) (3.3-fold decrease), ICAM1

(intercellular adhesion molecule 1) (1.9-fold decrease)

and PCNA (proliferating cell nuclear antigen) (1.8-fold

decrease) (supplementary Table S9). Second, we estab-

lished gene expression profiles for the five siRNAs in

the three cell types. The number of genes differentially

expressed in response to siRNAs with statistical signifi-

cance ranged between one and 12, according to the

condition. The main conclusion drawn from these

experiments is that each siRNA is associated with a

unique molecular signature on gene expression. For

example, transcripts that are downregulated by

LAMP2 ⁄ siRNA2 in A549 cells, such as JUN (jun

oncogene), PLAU (plasminogen activator, urokinase),

PLAUR (plasminogen activator, urokinase receptor),

RRM1 (ribonucleotide reductase M1 polypeptide),

TERF1 (telomeric repeat binding factor 1) and

TGFBR2 (transforming growth factor, beta recep-

tor II) (Fig. 4), were not systematically downregulated

by either LAMP2 ⁄ siRNA1, SREBF1 ⁄ siRNA1,

SREBF1 ⁄ siRNA2 or the NT siRNA. Importantly, the

fact that two different siRNAs targeting the same tran-

script do not provide the same gene expression profiles

(see Venn diagrams in Figs 3–5) rules out potential

secondary effects due to target knockdown, and indi-

cates that the unintended mRNA downregulations

observed are most probably siRNA off-target effects.

To some extent, the signatures of siRNAs also seem

to be dependent on the cell type in which siRNAs are

introduced. Indeed, whereas several mRNAs were con-

sistently downregulated by a given siRNA in every cell

type, we found that the abundance of some transcripts

was clearly differently affected by siRNA according to

the cell type, as illustrated by the 2.3-fold downregula-

tion of SOD2 (superoxide dismutase 2) found exclu-

sively in IMR-90 cells transfected with

SREBF1 ⁄ siRNA2. A global analysis of all data cross-

ing siRNAs and cell types revealed that about 60% of

the siRNA off-target effects observed in this study

appear to be cell type-specific.

Finally, in order to validate these data with another

method, we performed real-time PCR analyses for

some selected transcripts (CTGF, JUN, PLAU,

SPARC, TGFBR2) on samples used for microarray

experiments (RNAs extracted directly after a 24 h

transfection of 143B or A549 cells with SREBF1 ⁄ siR-

NA2 or LAMP2 ⁄ siRNA2) (supplementary Table S13).

We observed that mRNA abundances were modified

similarly with both methods, attesting to the reliability

of the results.

Kinetics of off-target effects induced by siRNA

In order to determine the time-course of siRNA side

effects in 143B cells transfected for 24 h with

SREBF1 ⁄ siRNA1 or the NT siRNA (100 nm), gene

expression data obtained at 0, 24 and 48 h post-trans-

fection were compared in experiments performed on

biological triplicates (Fig. 6). Again, we observed that

the transfection reagent alone induced only small vari-

ations in the abundance of gene transcripts, no matter

what the post-transfection time was (Fig. 6, col-

umns 1–3). In contrast, the relative abundance of sev-

eral mRNAs (between two and 15) was significantly

modified in response to the introduction of

SREBF1 ⁄ siRNA1 (Fig. 6, columns 4–6) or the NT

siRNA (Fig. 6, columns 7–9) into 143B cells. In these

conditions, the highest number of modifications was

observed 24 h post-transfection (Fig. 6, columns 5 and

CTL DF 100 50 20 5 100 50 20 5

SREBF1siRNA1 (nM)

NTsiRNA (nM)

SREBF1

α-tubulin

SREBF1

α-tubulin

SREBF1

α-tubulin

SREBF1

α-tubulin

0 h post-transfection




Fig. 2. Effect of the SREBF1-targeting siRNA on SREBF1 protein

level analyzed by western blotting in 143B cells. 143B cells were

incubated for 24 h with DF or transfected for 24 h with

SREBF1 ⁄ siRNA1 or the NT siRNA at the indicated concentrations.

Clear cell lysates were prepared 0, 24, 48 or 72 h post-transfection.

SREBF1 abundance was determined by western blotting on 25 lg

of protein, and immunodetection of a-tubulin was used as a loading

control.



8) (see also supplementary Tables S1 and S14). Repre-

sentative results are presented in Fig. 7, which summa-

rizes and illustrates each kind of kinetic profile that we

obtained. As shown in Fig. 7A a moderate but sus-

tained upregulation of CDKN1B (cyclin-dependent

kinase inhibitor 1B, also known as p27Kip1) was

observed after the transfection of 143B cells with

SREBF1 ⁄ siRNA1. In Fig. 7B, we illustrate the upregu-

lation of PLAU (plasminogen activator urokinase) in

cells responding to either SREBF1-specific or the NT

siRNA. A similar profile was also obtained for

SPARC (secreted protein acidic cysteine-rich, also

known as osteonectin). The abundance of several tran-

scripts was also decreased in cells transfected with

SREBF1 ⁄ siRNA1, such as CCND2 (cyclin D2)

(Fig. 7C), UNG (uracil-DNA glycosylase), ALDOA

(aldolase A), CENPF (centromere protein F), CKB

(brain creatine kinase) or CTGF (connective tissue

growth factor). The NT siRNA also downregulated

the expression of several genes, such as EGFR (epider-

mal growth factor receptor) (Fig. 7D), MAP2K1 (also

known as MEK1, mitogen-activated protein kinase

kinase 1) and RAF1 (murine leukemia viral oncogene

homolog 1). Finally, downregulation of IGFBP3 was

observed in cells transfected with either SREBF1 ⁄siRNA1 or the NT siRNA (Fig. 7E).

Effect of duration of transfection period on siRNA

off-target signature

To assess the putative effect of the transfection period

on the siRNA nonspecific effects, gene expression pro-

files in 143B cells transfected for 24 or 48 h with

SREBF1 ⁄ siRNA1 or the NT siRNA at 100 nm were

next determined in three independent experiments.

RNA extractions were performed between 0 and 48 h

post-transfection (see also supplementary Tables S14

and S15). As shown in Fig. 8, the number of genes

differentially expressed was higher after a 48 h than

after a 24 h transfection period. The heat map (Fig. 9)

compares, in all tested conditions, the relative

abundances of mRNAs differentially expressed in at

least one condition. In the presence of SREBF1 ⁄siRNA1, we usually observed higher upregulation or

SPARC

NT

_siR

NA

SR

EB

F1-

siR

NA

2S

RE

BF

1-si

RN

A1

DF N

T_s

iRN

A

LAM

P2-

siR

NA

1LA

MP

2-si

RN

A2

DF

PLAUCCND3CANXCAV1MAP2K1IGFBP3RAF1TNFRSF10BUNGYWHAZCCND1PLAUREGFRDUSP1CTGFJUN

SPARC

CCND3

CCND1

CANX

CAV1

MAP2K1

IGFBP3

RAF1

TNFRSF10B

UNG

YWHAZ

PLAU

PLAUREGFR

DUSP1

CTGF

JUN

A B

Fig. 3. Effect of the SREBF1-targeting and the LAMP2-targeting siRNAs on gene expression profiles analysed by microarray in 143B cells.

Cells were incubated for 24 h with DF or transfected for 24 h with SREBF1 ⁄ siRNA1, SREBF1 ⁄ siRNA2 (A), LAMP2 ⁄ siRNA1, LAMP2 ⁄ siRNA2

(B) or the NT siRNA at 100 nM before RNA extraction, reverse transcription, and processing for microarray analysis. Expression plots present

the genes displaying significant differences in relative transcript level between siRNA-transfected cells and DF-treated cells (n = 3). Color

key: green, downregulation; red, upregulation. A scale for heat maps as minimum and maximum fold differences is presented. The Venn

diagrams present the numbers of mRNAs differentially expressed with statistical significance in the presence of the indicated siRNAs in

143B cells. The numbers of transcripts differentially expressed in the presence of both siRNAs specific for the same target are indicated in

diagram intersections.



downregulation magnitudes after a 48 h transfection

(Fig. 9, columns 7 and 8) than after a 24 h transfection

(Fig. 9, columns 5 and 6). Similar conclusions can be

drawn from data obtained for cells transfected with

the NT siRNA (Fig. 9, columns 11 and 12 versus 9

and 10). Therefore, it seems that the longer the trans-

fection period, the stronger the off-target effects of

siRNA on gene expression.

mRNA homology with siRNA seed region

Perfect mRNA ⁄ siRNA pairing is not necessary for

siRNA off-target effects. Indeed, homology between

mRNA and siRNA seed region (encompassing nucleo-

tides 2–8 or 2–7 of the antisense strand) was shown to

be sufficient to induce off-target silencing [6,7,10].

Hence, we searched for regions of sequence homology

between the guide strands of the five siRNAs used in

this study and their respective unspecific targets

(Fig. 10). The transcripts used for this analysis were

found to be significantly downregulated in 143B, A549

and IMR-90 cells transfected for 24 h with the siRNAs

(Figs 3–5 and supplementary Tables S1–S12). For sev-

eral mRNAs, we found small stretches of sequence

identity with the 3¢-end of siRNA sense sequences

(5¢-end of antisense sequences). However, only 65% of

them (21 of 33) can lead to perfect mRNA pairing

with siRNA seed regions, as defined above. Therefore,

an important proportion (about 35%) of the siRNA

side effects observed here cannot be directly explained

by seed homology. This analysis was also repeated

with the siRNA passenger strands, but no perfect seed

match was found in these conditions (data not shown).

Discussion

It is now well established that off-target silencing is a

fundamental feature of siRNAs [5,6,9,14]. The present

investigation was conducted in order to increase our

knowledge about siRNA off-target effects under vari-

ous experimental conditions. Molecular signatures of

siRNAs were determined with a commercial low-

density microarray designed for siRNA side effect

studies. This microarray comprises 273 capture probes

0.50 2.10 0.50 2.00

0 0 12

SREBF1 siRNA1

SREBF1 siRNA2

8 0 10

LAMP2 siRNA1

LAMP2 siRNA2

NT

_siR

NA

S

RE

BF

1-si

RN

A2

SR

EB

F1-

siR

NA

1 D

F

RAF1

GPX1 TERF1

MAP2K1 IGFBP3 CTNNB1 TGFBR2 YWHAZ CCND1 RRM1 PLAUR IL8 JUND CDKN1A PLAU BIN1 MYC JUN CSF1 GADD45A

EGFR

NT

_siR

NA

LA

MP

2-si

RN

A2

LAM

P2-

siR

NA

1 D

F

RAF1

GPX1

MAP2K1

TGFBR2

TERF1 RRM1

CTNNB1

IGFBP3

YWHAZ

CCND1

PLAUR

IL8 JUND

CDKN1A

PLAU

BIN1

MYC JUN

CSF1

GADD45A

EGFR

A B

Fig. 4. Effect of the SREBF1-targeting and the LAMP2-targeting siRNAs on gene expression profiles analyzed by microarray in A549 cells.

Cells were incubated for 24 h with DF or transfected for 24 h with SREBF1 ⁄ siRNA1, SREBF1 ⁄ siRNA2 (A), LAMP2 ⁄ siRNA1, LAMP2 ⁄ siRNA2

(B) or the NT siRNA at 100 nM before RNA extraction, reverse transcription, and processing for microarray analysis. Expression plots present


key: green, downregulation, red, upregulation. A scale for heat maps as minimum and maximum fold differences is presented. The Venn


A549 cells. The numbers of transcripts differentially expressed in the presence of both siRNAs specific for the same target are indicated in

diagram intersections.



allowing the expression analysis, at the transcriptomic

level, of genes mainly involved in cell responses to

IFN challenge, apoptosis, DNA repair, cell cycle, and

metabolism.

The few effects of DF on gene expression were

found to be dependent on cell type. Indeed, whereas

variations observed in both 143B and A549 cells incu-

bated with DF alone were generally negligible, they

were more numerous in IMR-90 cells, as illustrated by

the slight but reproducible downregulation of ADPRT

(ADP-ribosyltransferase), CCNB1 (cyclin B1), DDIT3

(DNA-damage-inducible transcript 3), ICAM1, IG-

FBP3, PCNA, PRKDC (protein kinase, DNA-acti-

vated, catalytic polypeptide), SERPINE1 ⁄PAI-1(serpin peptidase inhibitor 1 ⁄plasminogen activator

inhibitor-1), TFDP1 (transcription factor Dp-1),

TNFRSF10B (tumor necrosis factor receptor super-

family, member 10b) and TYMS (thymidylate synthe-

tase). This transfection reagent might therefore alter

some cellular processes in a cell type-dependent man-

ner. For instance, an increase in the cell cycle timing

could be expected following the downregulation of

PCNA, coding for a protein involved in the control of

DNA replication and CDK2-cyclin A activity [15].

In most cases (about 70%), and as expected,

DF-induced effects on gene expression were also

observed in the presence of any tested siRNA, as illus-

trated by the comparable downregulation of ADPRT

in IMR-90 cells in the presence of DF alone

(0.68 ± 0.25) or in combination with LAMP2 ⁄siRNA2 (0.64 ± 0.17) or the NT siRNA (0.64 ± 0.12)

(supplementary Table S12; see also supplementary

Tables S9–S11). However, additional or antagonistic

effects of DF and siRNAs were also observed. For

example, the SERPINE1 mRNA level was reduced by

DF alone (0.65 ± 0.08) but was increased with statisti-

cal significance by LAMP2 ⁄ siRNA1 (2.24 ± 0.82) in

IMR-90 cells (supplementary Table S11).

The four targeting siRNAs used in this study pro-

vide efficient knockdown of their respective targets at

100 nm. This concentration might seem rather high,

but was chosen in order to generate side effects allow-

ing a comparative study of the importance of siRNA

sequence, cell type, transfection period and post-trans-

fection time before analysis. The differences in siRNA

on-target efficiencies observed between 143B, A549

and IMR-90 cells (Fig. 1), as previously found for

other cell lines [16], could probably be explained by

0.35 1.50 0.35 2.40

7 0 3

SREBF1 siRNA1

SREBF1 siRNA2

9 0 1

LAMP2 siRNA1

LAMP2 siRNA2

NT

_siR

NA

S

RE

BF

1-si

RN

A2

SR

EB

F1-

siR

NA

1D

F

NT

_siR

NA

LA

MP

2-si

RN

A2

LAM

P2-

siR

NA

1 D

F

MYBL2 BAD UNG MADH3

WARS SERPINE1 ICAM1 IGFBP3 HIST1H3ITGFBR2HSPCA MAPK1 SOD2 EGFR

JUND

MYBL2

BAD UNG

MADH3

WARS

SERPINE1

ICAM1

IGFBP3

HIST1H3I

TGFBR2

HSPCA

MAPK1

SOD2

EGFR

JUND

A B

Fig. 5. Effect of the SREBF1-argeting and the LAMP2-targeting siRNAs on gene expression profiles analyzed by microarray in IMR-90 cells.

Cells were incubated for 24 h DF or transfected for 24 h with SREBF1 ⁄ siRNA1, SREBF1 ⁄ siRNA2 (A), LAMP2 ⁄ siRNA1, LAMP2 ⁄ siRNA2 (B)

or the NT siRNA at 100 nM before RNA extraction, reverse transcription, and processing for microarray analysis. Expression plots present


key: green, downregulation; red, upregulation. A scale for heat maps as minimum and maximum fold differences is presented. The Venn


IMR-90 cells. The numbers of transcripts differentially expressed in the presence of both siRNAs specific for the same target are indicated

in diagram intersections.



the expression level of the RNAi pathway components

in each cell type and ⁄or by different transfection effi-

ciencies. These hypotheses highlight the importance of

the cellular environment in the determination of both

efficiency and specificity of siRNA molecules, not only

for in vitro studies, but also when siRNA-based thera-

peutic approaches are considered. Moreover, it was

suggested that the cellular background could modify

the degree of siRNA off-target effects elicited through

1 2 3 4 5 6 7 8 9

PLAU

NT

_48h

N

T_2

4h

NT

_0h

SR

EB

F1_

48h

SR

EB

1F_2

4h

SR

EB

1F_0

h D

F_4

8hD

F_2

4hD

F_0

h

SPARC CTGF CDKN1B HSPB1 MLH1 IGFBP2 BCL2L1 HSPCB BIN1 K-ALPHA-1 ADPRT ALDOA CKB CENPF HPRT1 UNG CCND2 CANX CASP3 RAF1 UBE2V1 MAP2K1 EGFR PLAUR IGFBP3

A

B

Fig. 6. Kinetics of the gene expression profiles induced by

SREBF1 ⁄ siRNA1 in 143B cells. (A) Design of the experiment. The

24 h transfection period is indicated on a gray background. (B)

143B cells were incubated for 24 h with DF or transfected for 24 h

with SREBF1 ⁄ siRNA1 or the NT siRNA at 100 nM. RNA was

extracted 0, 24 or 48 h post-transfection, reverse transcribed, and

processed for microarray analysis. Expression plots present the

genes displaying significant differences in relative transcript level

between siRNA-transfected cells and DF-treated cells (n = 3). Color

key: green, downregulation; red, upregulation. A scale for heat

maps as minimum and maximum fold differences is presented.

0.00.20.40.60.81.01.21.41.6

A

B

C

D

E

Rel

. mR

NA

ab

un

dan

ce

0 h 24 h 48 h

0 h 24 h 48 h

0 h 24 h 48 h

0 h 24 h 48 h

0 h 24 h 48 h

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Rel

. mR

NA

ab

un

dan

ce

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Rel

. mR

NA

ab

un

dan

ceR

el. m

RN

A a

bu

nd

ance

Rel

. mR

NA

ab

un

dan

ce

Fig. 7. Representative kinetic profiles of gene expression in 143B

cells incubated with DF (circles) or transfected with SREBF1 ⁄ siR-

NA1 (squares) or the NT siRNA (triangles). Gene expression was

analyzed by microarray 0, 24 and 48 h post-transfection, and pro-

files are illustrated for CDKN1B (A), PLAU (B), CCND2 (C), EGFR

(D) and IGFBP3 (E).



an IFN response pathway, as the IFN response was

found to be stronger in TLR3-expressing cells [11] and

in nontumor cells [17]. However, genes classically asso-

ciated with the siRNA-induced IFN response, such as

IFITM2 (interferon-induced transmembrane protein 2),

IFNAR1 (interferon receptor 1) or IRF1 (interferon-

responsive factor 1), were not upregulated in the

presence of siRNAs, even in the TLR3-expressing

A549 cells or in the nonimmortalized IMR-90 cells.

We showed that two different siRNAs designed to

knock down SREBF1 can also modify the expression

of unintended genes in 143B, A549 and IMR-90 cells.

Interestingly, the sets of misregulated genes are not the

same for each siRNA. This lack of overlapping effects

rules out an indirect effect resulting from the silencing

of the transcription factor SREBF1, which would

modify gene expression in an siRNA-independent

manner. Therefore, these variations in mRNA abun-

dance can be considered as real siRNA off-target

effects. Similar conclusions can be drawn from experi-

ments performed with two other siRNAs targeting the

LAMP2 transcript. Furthermore, we observed that an

NT siRNA, used as a negative control in our experi-

ments, unexpectedly altered the expression of several

genes affected or not affected by the siRNAs targeting

SREBF1 or LAMP2. Thus, the unique nonspecific

molecular signature generated by each siRNA supports

previous studies showing that off-target effects are

dependent on siRNA sequence [6,7]. The role of

sequence pairing in siRNA side effects is also

supported by data showing that these effects can be

dramatically reduced in the presence of another con-

trol, the RNA-induced silencing complex (RISC)-free

siRNA (data not shown). Unlike the NT siRNA, this

negative control is not loaded onto RISC, is unable to

interact with mRNA, and thus cannot direct slicing. It

is also important to note that the unexpected effects of

the NT siRNA on gene expression underline the diffi-

culty of choosing the most relevant control in RNAi

experiments in order to obtain reliable results, as

emphasized recently [18].

The seed region is particularly important in siRNA

side effects, because mRNA ⁄ siRNA pairing in this

short region may be sufficient to induce mRNA deg-

radation [6,19]. Thus, we investigated whether siRNA

seed regions share homology with the sequences of

mRNAs downregulated directly after cell transfection

with SREBF1 ⁄ siRNA1, SREBF1 ⁄ siRNA2, LAMP2 ⁄siRNA1, LAMP2 ⁄ siRNA2 or the NT siRNA. We

determined that about 35% of these downregulated

mRNAs do not show perfect sequence matching with

the seed region of the corresponding siRNA, suggest-

ing that these off-target effects are not directed by

seed pairing. These results might seem inconsistent

with the current description of siRNA off-targeting

mechanisms, in which seed regions play a predomi-

nant role [6,10]. It is possible that these 35% of seed-

independent variations represent a secondary effect

resulting from the downregulation of the 65% seed-

matching off-targets. However, as these variations

were observed at the earliest tested time point (0 h

post-transfection), we could not establish whether

these two categories of genes have different kinetics,

and thus could not determine the mechanisms gener-

ating all siRNA side effects, a point that will require

further investigation.

Sequence-dependent side effects of siRNAs on gene

expression are expected to be identical in different cell

types. Gene expression profiles obtained for 143B,

A549 and IMR-90 cells allow a cell type-to-cell type

comparison of siRNA side effects, but only for

0 h 24 h 48 h 72 h

0 h 24 h 48 h 72 h

Trans-

A

B

fectionExtraction24 h post-T

Extraction48 h post-T

20 genes

10 genes

4 genes

3 genes

SREBF1/siRNA1

NT siRNA

Trans-fection



26 genes

27 genes

12 genes

15 genesNT siRNA

SREBF1/siRNA1

Fig. 8. Effect of two different transfection periods on gene expres-

sion profiles in 143B cells transfected with SREBF1 ⁄ siRNA1 or the

NT siRNA at 100 nM. (A) Twenty-four hours of transfection and

RNA extraction 24 or 48 h post-transfection. (B) Forty-eight hours

of transfection and RNA extraction 0 or 24 h post-transfection.

Design of the experiments and number of genes differentially

expressed in siRNA-transfected cells when compared with

DF- treated cells.



0.35 5.60

IGFBP5

DF

_T24

_E24

DF

_T24

_E48

DF

_T48

_E0

DF

_T48

_E24

SR

EB

F1_

T24

_E24

SR

EB

F1_

T24

_E48

SR

EB

F1_

T48

_E0

SR

EB

F_T

48_E

24N

T_T

24_E

24N

T_T

24_E

48N

T_T

48_E

0N

T_T

48_E

24

SPARCMMP2BCL6S100A4FOSTFRCENPP1PLAUIGFBP4CTGFCDH11GSNHSPB1ITGA5MMP14CDKN1BFGF2PCNADHFRUNGKIF23HSPCBMLH1IGFBP2CAV1EF21BCL2L1CDC42PRAMEMADH1BAXCANXMAPK9UBE2CRAD51TFDP1ADPRTBIN1K-ALPHA-1CKBALDOACDK2CENPFTFDP2TERTTGFBR2FGFR1CASP3CTSLABL1UBE2V1RAF1BSGTIMP1COL6A2MAP2K1EGFRCDH13PLAURJUNITGA6HPRT1PLATWARSTNFRSF10BCCND2TK1DUSP1

Fig. 9. Effect of two different transfection

periods on gene expression profiles in 143B

cells transfected with SREBF1 ⁄ siRNA1 or

the NT siRNA at 100 nM. 143B cells were

incubated for 24 h (T24) or 48 h (T48) with

DF or transfected for 24 h (T24) or 48 h

(T48) with SREBF1 ⁄ siRNA1 or the NT siRNA

at 100 nM. RNA was extracted 0 h (E0),

24 h (E24) or 48 h (E48) post-transfection,

reverse transcribed, and processed for

microarray analysis. Expression plots pres-

ent the genes displaying significant differ-

ences in relative transcript level between

siRNA-transfected cells and DF-treated cells

(n = 3). Color key: green, downregulation;

red, upregulation. A scale for heat maps as

minimum and maximum fold differences is

presented.



mRNAs abundant enough to be detected by the micro-

array in all cell types. Thus, after removing mRNAs

only detected in one or two cell types, we ended up

with a list of 33 mRNAs that are significantly down-

regulated by at least one of the five siRNAs in at least

one of the three cell types (supplementary Table S16).

We found that about 40% of these mRNAs (13 of 33)

were consistently downregulated in all cell types,

although statistical significance was only found for one

or two cell types. This could be a consequence of large

standard deviations, lack of standard deviations,

and ⁄or insufficient number of replicates. Nevertheless,

the most interesting observation is that about 60% of

siRNA off-target effects are dependent on the cell type

(supplementary Table S16). These unexpected results

might imply that cell type-specific factors influence the

sets of transcripts affected by an siRNA of interest in

a particular cellular background.

It is also interesting to stress that siRNAs induce

reproducible upregulation of several mRNAs, although

the underlying mechanisms are unclear. siRNAs can

activate dsRNA-dependent protein kinase and TLR3

pathways, leading to the activation of transcription

factors involved in the IFN response, such as IRFs

and nuclear factor-jB [11,20]. However, as mentioned

above, no IFN response was observed in 143B, A549

or IMR-90 cells. In fact, the IFN response is activated

by dsRNAs longer than 21 bp, particularly in immune

cells [11]. Another explanation for gene upregulation

could be that siRNAs silence a transcript encoding a

transcriptional repressor, leading to the upregulation

of some transcripts controlled by this repressor.

In order to evaluate the time lapse during which

siRNA side effects can be observed, we established the

kinetics of the modifications in gene expression at 0,

24 and 48 h post-transfection in 143B cells transfected

Fig. 10. Sequence alignments between siR-

NA sense strands and mRNAs downregulat-

ed by these siRNAs after 24 h of

transfection. Identical nucleotides in mRNA

and siRNA sequences are indicated on a

gray background, and mismatched nucleo-

tides on a white background. The degree of

sequence identity to siRNAs is indicated as

number of contiguous identical nucleo-

tides ⁄ total number of identical nucleotides.

Homology stretches fitting exactly with

siRNA seed regions (nucleotides 2–7 of the

siRNA antisense strand) are labeled ‘yes’ in

the seed region column.



with SREBF1 ⁄ siRNA1 or the NT siRNA. A general

observation is that the strongest effect on untargeted

gene expression was found at 24 h post-transfection,

and it decreased thereafter. Thus, according to our

data, the undesired effects of siRNA seem to appear in

a transient way.

Another key parameter to take into account could

be the length of the transfection period. Although 24 h

of transfection has been frequently reported in the lit-

erature [21,22], a longer transfection period could be

chosen in order to improve or prolong siRNA action.

Therefore, we next compared the effect of a 24 h or

48 h transfection period on the siRNA off-target sig-

nature in 143B cells, and found a higher number of

genes differentially expressed with statistical signifi-

cance after a 48 h transfection. Moreover, as shown

for IGFBP4, IGFBP5, JUN or SPARC, the upregula-

tion or downregulation of several genes already

observed for a 24 h transfection time was enhanced

after a 48 h transfection. These data suggest that the

shortest transfection time should be preferred in order

to minimize the extent of siRNA side effects.

It is known that siRNA side effects are concentration-

dependent [8], and the lowest efficient concentration of

siRNA is usually recommended to prevent saturation of

the RNAi machinery and off-targeting. We also

observed that, when the siRNA concentration was low-

ered from 100 nm to 20 nm, the number of genes differ-

entially expressed with statistical significance and the

magnitude of gene upregulation and downregulation in

143B cells transfected with SREBF1 ⁄ siRNA1 were

reduced (supplementary Table S1). It is therefore

important to keep the siRNA concentration as low as

possible. However, off-target silencing can still be

observed for siRNA concentrations as low as 4 nm [7].

Hence, this parameter alone does not seem to be suffi-

cient to completely prevent the siRNA nonspecific

activity. A promising alternative strategy to decrease

siRNA side effects is the use of chemically modified

molecules such as 2¢-O-methylated siRNAs [23].

In conclusion, we have shown that the signature of

siRNAs on gene expression depends not only on

siRNA sequence but also on the cell type of interest,

and that important parameters must be considered in

order to minimize siRNA undesired effects: transfec-

tion period, time between transfection and analysis,

and siRNA concentration. Interestingly, 35% of the

observed effects cannot be explained by complete seed

pairing.

When analyzed directly after the transfection period,

the number of mRNAs differentially expressed in

response to each siRNA and in each cell type ranges

between one and 12 out of 273 genes, according to the

condition (between 0.4% and 4.4% of the genes that

can be analyzed by the microarray). A more restrictive

calculation excluding the transcripts that were not

detected in each condition leads to a range of 0.5–

6.3% of unintended mRNA variations. These results

cannot be extended to a genome-wide scale, because

the microarray is not representative of the whole gen-

ome; instead, its design is focused on cellular responses

to siRNA, IFN, DNA damage and apoptotic stimuli.

However, 6.3% of misregulated genes represents an

important proportion that could reflect numerous

modifications in gene expression at the transcriptomic

level. If these unintended modifications observed at the

transcript level were reflected at the protein level, it

would become likely, as recently observed [24], that

siRNA off-target effects would result in uncontrolled

impairment of cell physiology.

Experimental procedures

siRNA transfection

siRNA transfection experiments were performed using

dsRNA synthesized by Dharmacon (Lafayette, CO, USA).

Four siRNAs were designed for the specific silencing of

SREBF1 (NM_004176) and LAMP2 (NM_002294) tran-

scripts. Sense sequences for SREBF1 ⁄ siRNA1, SREBF1 ⁄siRNA2, LAMP2 ⁄ siRNA1 and LAMP2 ⁄ siRNA2 are 5¢-UGACUUCCCUGGCCUAUUUUU-3¢, 5¢-ACAUUGAGC

UCCUCUCUUGUU-3¢, 5¢-GAUAAGGUUGCUUCAGU

UAUU-3¢ and 5¢-ACAGUACGCUAUGAAACUAUU-3¢,respectively. As a negative control, we used an NT siRNA

(5¢-UAGCGACUAAACACAUCAA-3¢) or a RISC-free

siRNA (proprietary sequence) from Dharmacon. Cells were

transfected with DF (T-2001; Dharmacon) at 1.5 lLÆlg)1

siRNA. The transfection efficiency in 143B cells plated on

coverslips was determined using fluorescein isothiocyanate-

labeled siRNA (Silencer siRNA Labeling kit; Ambion,

Austin, TX, USA) and evaluated as 90–95% after 24 h by

cell counting using a confocal microscope (Leica, Wetzlar,

Germany) (data not shown).

siRNA efficiency for SREBF1 and LAMP2 expression

was determined by either real-time PCR using specific prim-

ers or by western blotting analysis. 143B, IMR-90 and

A549 cells were seeded in culture plates (Corning, Lowell,

MA, USA) at 25 000 cellsÆcm)2 (143B and IMR-90) or

50 000 cellsÆcm)2 (A549) 24 h before being transfected with

DF for 24 h with 100, 50, 20 or 5 nm siRNA. Media were

replaced and gene silencing was verified 0, 24, 48 or 72 h

post-transfection. For DNA microarray experiments, 143B,

IMR-90 and A549 cells were seeded as above and then

transfected with DF for 24 or 48 h with 100 or 20 nm

siRNA. Total RNA was extracted 0, 24 or 48 h post-

transfection and then processed for microarray analysis.



Real-time PCR

After cell transfection with siRNAs, total RNA was

extracted using the Total RNAgent extraction kit (Pro-

mega, Madison, WI, USA). mRNA contained in 5 lg of

total RNA was reverse transcribed using SuperScript II

Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA)

according to the manufacturer’s instructions. Forward and

reverse primers for SREBF1 (forward, 5¢-GGCCCAG

GTGACTCAGCTATT-3¢; reverse, 5¢-AGGGCATCCGA

GAATTCCTT-3¢), LAMP2 (forward, 5¢-TCAGCATTGC

AAATAACAATCTCA-3¢; reverse, 5¢-CAGTCTGCTCT

TTGTTGCACATATAA-3¢), CTGF (forward, 5¢-CAAGCTGCCCGGGAAAT-3¢; reverse, 5¢-GGACCAGGCA

GTTGGCTCTA-3¢), JUN (forward, 5¢-GGATCAAGGC

GGAGAGGAA-3¢; reverse, 5¢-TCCAGCCGGGCGATT-3¢),PLAU (forward, 5¢-CTGTGACCAGCACTGTCT

CAGTTT-3¢; reverse, 5¢-CCCAGTGAGGATTGGATGA

ACTA-3¢), SPARC (forward, 5¢-GAGACCTGTGACCT

GGACAATG-3¢; reverse, 5¢-GGAAGGAGTGGATTTAG

ATCACAAGA-3¢), TGFBR2 (forward, 5¢-TGGACCCT

ACTCTGTCTGTGGAT-3¢; reverse, 5¢-TTCTGGAGC

CATGTATCTTGCA-3¢) and TBP ⁄TFIID (forward,

5¢-CCTCACAGGTCAAAGGTTTACAGTAC-3¢; reverse,

5¢-GCTGAGGTTGCAGGAATTGAA-3¢) were designed

using primer express 1.5 software (Applied Biosystems,

Foster City, CA, USA). Amplification reaction assays con-

tained SYBR Green PCR Mastermix (Applied Biosystems)

and primers (Applied Biosystems) at 300 nm. A hot start at

95 �C for 5 min was followed by 40 cycles at 95 �C for 15 s

and 65 �C for 1 min using an ABI PRISM 7000 SDS ther-

mal cycler (Applied Biosystems). TBP ⁄TFIID was used as

the reference gene for normalization and relative mRNA

steady-state level quantification. Melting curves were gener-

ated after amplification, and data were analyzed using the

thermal cycler software. Each sample was tested in

duplicate.

Clear cell lysate preparation and western blotting

analysis

143B cells were transfected in 12-well plates (Corning) as

described above. Cells were then rinsed with 1.5 mL of

NaCl ⁄Pi and lysed in 200 lL of cold lysis buffer (20 mm

Tris, pH 7.4, 150 mm NaCl, 1 mm EDTA, 1% Triton

X-100) containing protease inhibitors (Roche, Basel,

Switzerland). Clear cell lysates were prepared, and protein

contents were determined by the Bradford method (Pierce,

Rockford, IL, USA).

Samples corresponding to 25 lg of protein were prepared

in Laemmli SDS loading buffer, resolved on 10%

SDS ⁄PAGE, and transferred to poly(vinylidene difluoride)

membranes (Millipore, Billerica, MA, USA). For SREBF1,

LAMP2 and a-tubulin detection, membranes were blocked

for 2 h in NaCl ⁄Tris-T (20 mm Tris, pH 7.4, 150 mm NaCl,

0.1% Tween-20) containing 2% dry milk (Amersham, Pis-

cataway, NJ, USA) and incubated for 2 h (SREBF1) or 1 h

(a-tubulin) with either mouse anti-SREBF1 IgG (BD

Pharmingen, Mississauga, Canada) at a 1 : 5000 dilution or

mouse anti-a-tubulin IgG (Sigma, Saint Louis, MO, USA)

at a 1 : 30 000 dilution. The blots were washed and pro-

teins were visualized with horseradish peroxidase-conju-

gated anti-(mouse IgG) (Dako, Glostrup, Denmark) and an

ECL system (Amersham). Equal protein loading was

checked by the immunodetection of a-tubulin.

Low-density DNA microarray

Array design

We used a low-density DNA microarray (DualChip human

RNAi side effect; Eppendorf, Westbury, NY, USA) allow-

ing gene expression analysis for 273 genes, including genes

related to IFN response, apoptosis, proliferation, DNA

repair, metabolism, and intracellular signaling (see supple-

mentary Table S17 for the list of genes and supplementary

Fig. S1 for the array design). Results from reliable and vali-

dated low-density arrays were reported elsewhere [25–28].

The method is based on a system with two arrays on a

glass slide and three identical subarrays (triplicate spots)

per array. The reliability of hybridizations and experimental

data was evaluated using several positive and negative

hybridization controls, as well as detection controls spotted

on the microarray.

RNA reverse transcription and cDNA hybridiza-

tion

After cell transfection with siRNAs, total RNA was

extracted with the Total RNAgents extraction kit (Pro-

mega), quality was checked with a bioanalyzer (Agilent

Technologies, Santa Clara, CA, USA), and 10 lg (143B

cells) or 20 lg (IMR-90 and A549 cells) was used for

reverse transcription in the presence of biotin-11-dCTP,

biotin-11-dATP (Perkin-Elmer, Waltham, MA, USA) and

Superscript II Reverse Transcriptase (Invitrogen), as

described previously [25]. Six synthetic poly(A)-tailed RNA

standards (Eppendorf) were spiked into the purified RNA

in order to quantify the experimental variation introduced

during labeling and analysis. For each condition, three

independent experiments were performed in triplicate, pro-

viding hybridizations on nine arrays carried out as

described by the manufacturer and reported previously [25].

Detection was performed with cyanin 3-conjugated

anti-biotin IgG (Jackson Immuno Research Laboratories,

West Grove, PA, USA). Fluorescence of hybridized arrays

was scanned using the Packard ScanArray (Perkin-Elmer)

at a resolution of 10 lm. To maximize the dynamic range

of detection, the same arrays were scanned with different

photomultiplier gains in order to quantify both the high-



copy and low-copy expressed genes. The scanned 16-bit

images were imported into imagene 5.5 software (BioDis-

covery, El Segundo, CA, USA) to quantify signal intensi-

ties. The fluorescence intensity of each DNA spot (median

intensity of all pixels present within the spot) was calculated

using local mean background subtraction. A signal was

only accepted when the average intensity after background

subtraction was at least two times higher than the local

background around the spot. Intensity values of triplicate

fluorescent signals were averaged and used to calculate the

intensity ratio between the test and the reference.

Data normalization and statistical analysis

The data were normalized in a two-step procedure. First, a

correction was applied using a factor calculated from the

intensity ratios of internal standards in the test and refer-

ence samples. The presence of the internal standard probes

at different locations of the array allowed quantification of

the local background and evaluation of the array homoge-

neity, which is taken into account in the normalization.

Furthermore, in order to consider the purity and quality of

the mRNA, a second normalization step was performed on

the basis of the average of fluorescence intensities measured

for a set of housekeeping genes (between three and 10,

according to the experiment).

All experiments were carried out in triplicate (n = 3), and

ratios representing the relative transcript levels are presented

as the mean ± standard deviation. Statistical analyses were

performed using sigmastat 3.1 software, in order to test

the significance of the differences between relative transcript

levels in siRNA-transfected cells and in DF-treated cells.

anova1s with an a-level of 0.050 were performed by the

Holm–Sidak test after a systematic check of the normality

test and the equal variance test. Transcript level variations

were considered to be statistically significant for P < 0.05.

Hierarchical clustering of gene expression profiles was per-

formed using the online epclust software (http://www.

bioinf.ebc.ee/EP/EP/EPCLUST/). Genes were clustered

using average linkage with the Manhattan distance metric.

Sequence alignments

cDNA sequences from NCBI database and siRNA sense

sequences were aligned using fasta 3.4 [29] with the

settings described previously [6].

Acknowledgements

The authors are grateful to Eppendorf’s group (Ham-

burg, Germany) and staff members for careful reading,

comments and suggestions. This work was supported

by the ‘Region Wallonne’ (Ministry for Research and

New Technologies and International Relations,

Program ST4772-QUIV ⁄ML, Namur, Belgium). The

authors also acknowledge financial support through

the Belgian Program on Interuniversity Attraction

Poles (IAP 6 ⁄ 02) and the ‘Action de Recherche

Concertee’ (ARC) funded by the ‘Gouvernement de la

Communaute Wallonie-Bruxelles’.

References

1 Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE

& Mello CC (1998) Potent and specific genetic interfer-

ence by double-stranded RNA in Caenorhabditis

elegans. Nature 391, 806–811.

2 Chu CY & Rana TM (2007) Small RNAs: regulators and

guardians of the genome. J Cell Physiol 213, 412–419.

3 Martin SE & Caplen NJ (2007) Applications of RNA

interference in mammalian systems. Annu Rev Genomics

Hum Genet 8, 81–108.

4 Elbashir SM, Martinez J, Patkaniowska A, Lendeckel

W & Tuschl T (2001) Functional anatomy of siRNAs

for mediating efficient RNAi in Drosophila melanogaster

embryo lysate. EMBO J 20, 6877–6888.

5 Summerton JE (2007) Morpholino, siRNA, and

S-DNA compared: impact of structure and mechanism

of action on off-target effects and sequence specificity.

Curr Top Med Chem 7, 651–660.

6 Jackson AL, Burchard J, Schelter J, Chau BN, Cleary

M, Lim L & Linsley PS (2006) Widespread siRNA

‘off-target’ transcript silencing mediated by seed region

sequence complementarity. RNA 12, 1179–1187.

7 Jackson AL, Bartz SR, Schelter J, Kobayashi SV,

Burchard J, Mao M, Li B, Cavet G & Linsley PS

(2003) Expression profiling reveals off-target gene regu-

lation by RNAi. Nat Biotechnol 21, 635–637.

8 Persengiev SP, Zhu X & Green MR (2004) Nonspecific,

concentration-dependent stimulation and repression of

mammalian gene expression by small interfering RNAs

(siRNAs). RNA 10, 12–18.

9 Snove O & Holen T (2004) Many commonly used

siRNAs risk off-target activity. Biochem Biophys Res

Commun 319, 256–263.

10 Birmingham A, Anderson EM, Reynolds A, Ilsey-Tyree

D, Leake D, Fedorov Y, Baskerville S, Maksimova E,

Robinson K, Karpilow J et al. (2006) 3¢-UTR seed

matches, but not overall identity, are associated with

RNAi off-targets. Nat Methods 3, 199–204.

11 de Veer MJ, Sledz CA & Williams BR (2005) Detection

of foreign RNA: implications for RNAi. Immunol Cell

Biol 83, 224–228.

12 Klatt AR, Klinger G, Zech D, Paul-Klausch B, Renno

JH, Schmidt J, Malchau G & Wielckens K (2007)

RNAi in primary human chondrocytes: efficiencies,

kinetics, and non-specific effects of siRNA-mediated

gene suppression. Biologicals 35, 321–328.



13 Hou YF, Zhou YC, Zheng XX, Wang HY, Fu YL,

Fang ZM & He SH (2006) Modulation of expression

and function of Toll-like receptor 3 in A549 and H292

cells by histamine. Mol Immunol 43, 1982–1992.

14 Zeng Y, Yi R & Cullen BR (2003) MicroRNAs and

small interfering RNAs can inhibit mRNA expression

by similar mechanisms. Proc Natl Acad Sci USA 100,

9779–9784.

15 Maga G & Hubscher U (2003) Proliferating cell nuclear

antigen (PCNA): a dancer with many partners. J Cell

Sci 116, 3051–3060.

16 Bantounas I, Phylactou LA & Uney JB (2004) RNA

interference and the use of small interfering RNA to

study gene function in mammalian systems. J Mol

Endocrinol 33, 545–557.

17 Stojdl DF, Lichty B, Knowles S, Marius R, Atkins H,

Sonenberg N & Bell JC (2000) Exploiting tumor-specific

defects in the interferon pathway with a previously

unknown oncolytic virus. Nat Med 6, 821–825.

18 Sarov M & Stewart AF (2005) The best control for the

specificity of RNAi. Trends Biotechnol 23, 446–448.

19 Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schel-

ter JM, Castle J, Bartel DP, Linsley PS & Johnson JM

(2005) Microarray analysis shows that some microR-

NAs downregulate large numbers of target mRNAs.

Nature 433, 769–773.

20 Alexopoulou L, Holt AC, Medzhitov R & Flavell RA

(2001) Recognition of double-stranded RNA and acti-

vation of NF-kappaB by Toll-like receptor 3. Nature

413, 732–738.

21 Zerr-Fouineau M, Chataigneau M, Blot C & Schini-

Kerth VB (2007) Progestins overcome inhibition of

platelet aggregation by endothelial cells by down-regu-

lating endothelial NO synthase via glucocorticoid recep-

tors. FASEB J 21, 265–273.

22 Chung S, Brown JM, Provo JN, Hopkins R & McIn-

tosh MK (2005) Conjugated linoleic acid promotes

human adipocyte insulin resistance through NFkappaB-

dependent cytokine production. J Biol Chem 280,

38445–38456.

23 Snove O & Rossi JJ (2006) Chemical modifications

rescue off-target effects of RNAi. ACS Chem Biol 1,

274–276.

24 Fedorov Y, Anderson EM, Birmingham A, Reynolds

A, Karpilow J, Robinson K, Leake D, Marshall WS

& Khvorova A (2006) Off-target effects by siRNA can

induce toxic phenotype. RNA 12, 1188–1196.

25 de Longueville F, Surry D, Meneses-Lorente G,

Bertholet V, Talbot V, Evrard S, Chandelier N, Pike A,

Worboys P, Rasson JP et al. (2002) Gene expression

profiling of drug metabolism and toxicology markers

using a low-density DNA microarray. Biochem Pharma-

col 64, 137–149.

26 de Longueville F, Atienzar FA, Marcq L, Dufrane S,

Evrard S, Wouters L, Leroux F, Bertholet V, Gerin B,

Whomsley R et al. (2003) Use of a low-density micro-

array for studying gene expression patterns induced by

hepatotoxicants on primary cultures of rat hepatocytes.

Toxicol Sci 75, 378–392.

27 Debacq-Chainiaux F, Borlon C, Pascal T, Royer V,

Eliasers F, Ninane N, Carrard G, Friguet B, de Long-

ueville F, Boffe S et al. (2005) Repeated exposure of

human skin fibroblasts to UVB at subcytotoxic level

triggers premature senescence through the TGF-beta1

signaling pathway. J Cell Sci 118, 743–758.

28 Vankoningsloo S, Piens M, Lecocq C, Gilson A,

De Pauw A, Renard P, Demazy C, Houbion A, Raes

M, Arnould T et al. (2005) Mitochondrial dysfunction

induces triglyceride accumulation in 3T3-L1 cells: role of

fatty acid beta-oxidation and glucose. J Lipid Res 46,

1133–1149.

29 Pearson WR & Lipman DJ (1988) Improved tools for

biological sequence comparison. Proc Natl Acad Sci

USA 85, 2444–2448.

Supplementary material

The following supplementary material is available

online:

Fig. S1. DualChip Human RNAi side effect design.

Table S1. Effects of DharmaFECT1 (DF), SREBF1 ⁄siRNA1 at 100 nm (S100) or 20 nm (S20) and the non-

targeting (NT) siRNA at 100 nm (N100) or 20 nm

(N20) on gene expression in 143B cells analyzed 0 h

post-transfection (transfection duration of 24 h).

Table S2. Effects of DharmaFECT1 (DF), SREBF1 ⁄siRNA2 at 100 nm (S100) and the nontargeting (NT)

siRNA at 100 nm (N100) on gene expression in 143B

cells analyzed 0 h post-transfection (transfection dura-

tion of 24 h).

Table S3. Effects of DharmaFECT1 (DF), LAMP2 ⁄siRNA1 at 100 nm (L100) and the nontargeting (NT)



tion of 24 h).




tion of 24 h).


siRNA at 100 nm (N100) on gene expression in A549


tion of 24 h).






tion of 24 h).




tion of 24 h).




tion of 24 h).


siRNA at 100 nm (N100) on gene expression in

IMR90 cells analyzed 0 h post-transfection (transfec-

tion duration of 24 h).













Table S13. Validation of microarray data by real-time

PCR.

Table S14. Effects of DharmaFECT1 (DF), the

SREBF1-specific siRNA at 100 nm (S100) and the

nontargeting (NT) siRNA at 100 nm (N100) on gene

expression in 143B cells analyzed 24 h or 48 h post-

transfection (transfection duration of 24 h).

Table S15. Effects of DharmaFECT1 (DF), the

SREBF1-specific siRNA at 100 nm (S100) and the

nontargeting (NT) siRNA at 100 nm (N100) on gene

expression in 143B cells analyzed 0 h or 24 h post-

transfection (transfection duration of 48 h).

Table S16. List of high-copy mRNAs downregulated

in the presence of the siRNAs.

Table S17. List of genes analyzed with the DualChip

Human RNAi Side Effect microarray.

This material is available as part of the online article

from http://www.blackwell-synergy.com

Please note: Blackwell Publishing are not responsible

for the content or functionality of any supplementary

materials supplied by the authors. Any queries (other

than missing material) should be directed to the corre-

sponding author for the article.



Date post:	24-Nov-2023
Category:	Documents
Upload:	unamur
View:	0 times
Download:	0 times

Gene expression silencing with ‘specific’ small interfering RNA goes beyond specificity - a...

Documents