Gene expression silencing with ‘specific’ small interfering RNA goes beyond specificity – a study of key parameters to take into account in the onset of small interfering RNA off-target effects ´ ´ ´ Sebastien Vankoningsloo1, Francoise de Longueville2, Stephanie Evrard2, Pierre Rahier2, Andree ¸ ´ ´ Houbion1, Antoine Fattaccioli1, Melanie Gastellier1, Jose Remacle2, Martine Raes1, Patricia Renard1 and Thierry Arnould1 Laboratoire de Biochimie et Biologie Cellulaire, University of Namur (F.U.N.D.P), Belgium Eppendorf Array Technologies, Namur, Belgium 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: thierry.arnould@fundp.ac.be (Received 10 January 2008, revised 12 March 2008, accepted 19 March 2008) 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 easiest approach to loss-of-function experiments However, several recent studies 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 conditions 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 doi:10.1111/j.1742-4658.2008.06415.x RNA interference (RNAi) is a recently discovered gene-silencing pathway [1] triggered by dsRNAderived 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 abolish the siRNA-induced slicing activity [4] However, several studies using DNA microarray and ⁄ or computational 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 circumstances, and especially in immune cells, siRNAs are 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 2738 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS S Vankoningsloo et al 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 (TLR3), leading to a general repression of translation [11] An inflammatory response was also observed in primary human chondrocytes transfected with siRNAs [12] These considerations 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 microarray 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 (SREBF1) mRNA encoding a transcription factor, two different siRNAs targeting the lysosome-associated membrane protein (LAMP2) mRNA encoding a lysosomal glycoprotein, and a nontargeting (NT) siRNA Gene expression profiles were determined for each siRNA in transiently transfected human osteosarcoma 143B cells, lung adenocarcinoma 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 lysosomal glycoprotein LAMP2, which is not known to be directly involved in transcription events (LAMP2 ⁄ siRNA1 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 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 transformed or nontransformed cells expressing or not expressing the siRNA-responsive TLR3 receptor Indeed, 143B and A549 are tumor-derived cell lines, siRNA off-target effects in different cell types 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 IMR90 cells, respectively (Fig 1A) Under these conditions, the effect of the SREBF1-targeting siRNA was prolonged, at least, up to 72 h post-transfection, as demonstrated 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 investigated 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 transfection with SREBF1 ⁄ siRNA1 (100 nm), apparently not correlated with mRNA levels (Fig 1B), probably result from differences in exposure times during western blotting 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 experiment was performed on biological triplicates, and the complete lists of relative transcript level values and corresponding standard deviations are provided in supplementary Tables S1–S12 Several transcripts were FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS 2739 siRNA off-target effects in different cell types Relative SREBF1 mRNA abundance A 1.6 143B A549 IMR90 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 DF 100 20 100 20 100 20 DF 100 20 100 20 100 20 DF 100 20 100 20 100 20 SREBF1 SREBF1 NT siRNA SREBF1 SREBF1 NT siRNA SREBF1 SREBF1 NT siRNA siRNA1 siRNA2 (nM) siRNA1 siRNA2 (nM) siRNA1 siRNA2 (nM) (nM) (nM) (nM) (nM) (nM) (nM) B Relative SREBF1 mRNA abundance S Vankoningsloo et al 2.5 2.0 1.5 1.0 0.5 0.0 24 48 72 DF 24 48 72 SREBF1/siRNA1 (100 nM) 24 48 72 NT siRNA (100 nM) Time post-transfection (h) Relative LAMP2 mRNA abundance C 143B 1.6 A549 IMR90 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 DF 100 20 100 20 100 20 DF 100 20 100 20 100 20 DF 100 20 100 20 100 20 LAMP2 siRNA1 (nM) LAMP2 NT siRNA siRNA2 (nM) (nM) LAMP2 siRNA1 (nM) LAMP2 NT siRNA siRNA2 (nM) (nM) LAMP2 siRNA1 (nM) LAMP2 NT siRNA siRNA2 (nM) (nM) Fig 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 ⁄ siRNA1, 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 control cells (n = 1) 2740 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS S Vankoningsloo et al siRNA off-target effects in different cell types SREBF1 h posttransfection α-tubulin SREBF1 24 h posttransfection α-tubulin SREBF1 48 h posttransfection α-tubulin SREBF1 72 h posttransfection α-tubulin CTL DF 100 50 20 SREBF1 siRNA1 (nM) 100 50 20 NT siRNA (nM) Fig 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 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 factor-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 established gene expression profiles for the five siRNAs in the three cell types The number of genes differentially expressed in response to siRNAs with statistical significance 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 receptor 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 transcript not provide the same gene expression profiles (see Venn diagrams in Figs 3–5) rules out potential secondary effects due to target knockdown, and indicates 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 consistently 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 downregulation of SOD2 (superoxide dismutase 2) found exclusively in IMR-90 cells transfected with SREBF1 ⁄ siRNA2 A global analysis of all data crossing 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 ⁄ siRNA2 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-transfection were compared in experiments performed on biological triplicates (Fig 6) Again, we observed that the transfection reagent alone induced only small variations in the abundance of gene transcripts, no matter what the post-transfection time was (Fig 6, columns 1–3) In contrast, the relative abundance of several 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 and FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS 2741 A B DF LAMP2-siRNA1 LAMP2-siRNA2 NT_siRNA S Vankoningsloo et al DF SREBF1-siRNA1 SREBF1-siRNA2 NT_siRNA siRNA off-target effects in different cell types CCND3 IGFBP3 CCND1 DUSP1 TNFRSF10B YWHAZ SPARC MAP2K1 RAF1 PLAU CANX JUN EGFR PLAUR CTGF UNG CAV1 SPARC PLAU CCND3 CANX CAV1 MAP2K1 IGFBP3 RAF1 TNFRSF10B UNG YWHAZ CCND1 PLAUR EGFR DUSP1 CTGF JUN Fig 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 8) (see also supplementary Tables S1 and S14) Representative results are presented in Fig 7, which summarizes and illustrates each kind of kinetic profile that we obtained As shown in Fig 7A a moderate but sustained 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 upregulation 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 transcripts 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 (epidermal growth factor receptor) (Fig 7D), MAP2K1 (also known as MEK1, mitogen-activated protein kinase 2742 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 profiles 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 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 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS siRNA off-target effects in different cell types DF SREBF1-siRNA1 SREBF1-siRNA2 NT_siRNA S Vankoningsloo et al B DF LAMP2-siRNA1 LAMP2-siRNA2 NT_siRNA A GADD45A CSF1 JUN MYC BIN1 PLAU CDKN1A JUND IL8 PLAUR RRM1 CCND1 YWHAZ TGFBR2 CTNNB1 IGFBP3 MAP2K1 GPX1 TERF1 RAF1 EGFR 0.50 SREBF1 siRNA1 2.10 IL8 JUND CSF1 MYC JUN CDKN1A GADD45A YWHAZ PLAUR PLAU CCND1 BIN1 IGFBP3 RAF1 MAP2K1 EGFR CTNNB1 TGFBR2 GPX1 TERF1 RRM1 2.00 0.50 12 SREBF1 siRNA2 LAMP2 siRNA1 10 LAMP2 siRNA2 Fig 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 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 A549 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 and 8) than after a 24 h transfection (Fig 9, columns and 6) Similar conclusions can be drawn from data obtained for cells transfected with the NT siRNA (Fig 9, columns 11 and 12 versus and 10) Therefore, it seems that the longer the transfection 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 nucleotides 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 several 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 various experimental conditions Molecular signatures of siRNAs were determined with a commercial lowdensity microarray designed for siRNA side effect studies This microarray comprises 273 capture probes FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS 2743 S Vankoningsloo et al DF LAMP2-siRNA1 LAMP2-siRNA2 NT_siRNA DF SREBF1-siRNA1 SREBF1-siRNA2 NT_siRNA siRNA off-target effects in different cell types A B HIST1H3I SERPINE1 MYBL2 IGFBP3 TGFBR2 MAPK1 EGFR HSPCA SOD2 MADH3 JUND WARS BAD UNG ICAM1 MYBL2 BAD UNG MADH3 JUND WARS SERPINE1 ICAM1 IGFBP3 HIST1H3I TGFBR2 HSPCA MAPK1 SOD2 EGFR 0.35 SREBF1 siRNA1 0.35 1.50 SREBF1 siRNA2 LAMP2 siRNA1 2.40 LAMP2 siRNA2 Fig 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 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 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 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 incubated 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, IGFBP3, PCNA, PRKDC (protein kinase, DNA-activated, catalytic polypeptide), SERPINE1 ⁄ PAI-1 (serpin peptidase inhibitor ⁄ plasminogen activator inhibitor-1), TFDP1 (transcription factor Dp-1), TNFRSF10B (tumor necrosis factor receptor superfamily, member 10b) and TYMS (thymidylate synthetase) This transfection reagent might therefore alter some cellular processes in a cell type-dependent manner 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] 2744 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 illustrated 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 statistical significance by LAMP2 ⁄ siRNA1 (2.24 ± 0.82) in IMR-90 cells (supplementary Table S11) The four targeting siRNAs used in this study provide efficient knockdown of their respective targets at 100 nm This concentration might seem rather high, but was chosen in order to generate side effects allowing a comparative study of the importance of siRNA sequence, cell type, transfection period and post-transfection 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 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS S Vankoningsloo et al siRNA off-target effects in different cell types A Rel mRNA abundance DF_0h DF_24h DF_48h SREB1F_0h SREB1F_24h SREBF1_48h NT_0h NT_24h NT_48h PLAU 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 Rel mRNA abundance C Rel mRNA abundance D Fig 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 0h 24 h 48 h 0h 24 h 48 h 0h 24 h 48 h 0h 24 h 48 h 24 h 48 h 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 1.2 1.0 0.8 0.6 0.4 0.2 0.0 1.2 1.0 0.8 0.6 0.4 0.2 0.0 E Rel mRNA abundance the expression level of the RNAi pathway components in each cell type and ⁄ or by different transfection efficiencies 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 therapeutic approaches are considered Moreover, it was suggested that the cellular background could modify the degree of siRNA off-target effects elicited through 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0h B Rel mRNA abundance B A 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Fig Representative kinetic profiles of gene expression in 143B cells incubated with DF (circles) or transfected with SREBF1 ⁄ siRNA1 (squares) or the NT siRNA (triangles) Gene expression was analyzed by microarray 0, 24 and 48 h post-transfection, and profiles are illustrated for CDKN1B (A), PLAU (B), CCND2 (C), EGFR (D) and IGFBP3 (E) FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS 2745 siRNA off-target effects in different cell types S Vankoningsloo et al A Transfection 0h Extraction 24 h post-T 24 h Extraction 48 h post-T 48 h 72 h SREBF1/siRNA1 20 genes genes NT siRNA 10 genes genes B Transfection 0h Extraction h post-T 24 h Extraction 24 h post-T 48 h 72 h SREBF1/siRNA1 26 genes 12 genes NT siRNA 27 genes 15 genes Fig Effect of two different transfection periods on gene expression 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 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 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 associated with the siRNA-induced IFN response, such as IFITM2 (interferon-induced transmembrane protein 2), IFNAR1 (interferon receptor 1) or IRF1 (interferonresponsive 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 abun2746 dance can be considered as real siRNA off-target effects Similar conclusions can be drawn from experiments performed with two other siRNAs targeting the LAMP2 transcript Furthermore, we observed that an NT siRNA, used as a negative control in our experiments, 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 control, 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 difficulty 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 degradation [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 not show perfect sequence matching with the seed region of the corresponding siRNA, suggesting 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 predominant role [6,10] It is possible that these 35% of seedindependent variations represent a secondary effect resulting from the downregulation of the 65% seedmatching 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 generating 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 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS siRNA off-target effects in different cell types DF_T24_E24 DF_T24_E48 DF_T48_E0 DF_T48_E24 SREBF1_T24_E24 SREBF1_T24_E48 SREBF1_T48_E0 SREBF_T48_E24 NT_T24_E24 NT_T24_E48 NT_T48_E0 NT_T48_E24 S Vankoningsloo et al Fig 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 h (E0), 24 h (E24) or 48 h (E48) 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 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS IGFBP5 SPARC MMP2 BCL6 S100A4 FOS TFRC ENPP1 PLAU IGFBP4 CTGF CDH11 GSN HSPB1 ITGA5 MMP14 CDKN1B FGF2 PCNA DHFR UNG KIF23 HSPCB MLH1 IGFBP2 CAV1 EF21 BCL2L1 CDC42 PRAME MADH1 BAX CANX MAPK9 UBE2C RAD51 TFDP1 ADPRT BIN1 K-ALPHA-1 CKB ALDOA CDK2 CENPF TFDP2 TERT TGFBR2 FGFR1 CASP3 CTSL ABL1 UBE2V1 RAF1 BSG TIMP1 COL6A2 MAP2K1 EGFR CDH13 PLAUR JUN ITGA6 HPRT1 PLAT WARS TNFRSF10B CCND2 TK1 DUSP1 0.35 5.60 2747 siRNA off-target effects in different cell types S Vankoningsloo et al Fig 10 Sequence alignments between siRNA sense strands and mRNAs downregulated by these siRNAs after 24 h of transfection Identical nucleotides in mRNA and siRNA sequences are indicated on a gray background, and mismatched nucleotides on a white background The degree of sequence identity to siRNAs is indicated as number of contiguous identical nucleotides ⁄ 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 mRNAs abundant enough to be detected by the microarray 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 downregulated 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 2748 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 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS S Vankoningsloo et al 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 literature [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 signature in 143B cells, and found a higher number of genes differentially expressed with statistical significance after a 48 h transfection Moreover, as shown for IGFBP4, IGFBP5, JUN or SPARC, the upregulation 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 concentrationdependent [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 lowered from 100 nm to 20 nm, the number of genes differentially 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 nm [7] Hence, this parameter alone does not seem to be sufficient 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: transfection 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 siRNA off-target effects in different cell types 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 genome; 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) transcripts 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 lLỈlg)1 siRNA The transfection efficiency in 143B cells plated on coverslips was determined using fluorescein isothiocyanatelabeled 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 primers or by western blotting analysis 143B, IMR-90 and A549 cells were seeded in culture plates (Corning, Lowell, MA, USA) at 25 000 cellsỈcm)2 (143B and IMR-90) or 50 000 cellsỈcm)2 (A549) 24 h before being transfected with DF for 24 h with 100, 50, 20 or 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 posttransfection and then processed for microarray analysis FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS 2749 siRNA off-target effects in different cell types S Vankoningsloo et al Real-time PCR After cell transfection with siRNAs, total RNA was extracted using the Total RNAgent extraction kit (Promega, Madison, WI, USA) mRNA contained in 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¢-CA AGCTGCCCGGGAAAT-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 contained SYBR Green PCR Mastermix (Applied Biosystems) and primers (Applied Biosystems) at 300 nm A hot start at 95 °C for was followed by 40 cycles at 95 °C for 15 s and 65 °C for using an ABI PRISM 7000 SDS thermal cycler (Applied Biosystems) TBP ⁄ TFIID was used as the reference gene for normalization and relative mRNA steady-state level quantification Melting curves were generated 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, 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 h in NaCl ⁄ Tris-T (20 mm Tris, pH 7.4, 150 mm NaCl, 2750 0.1% Tween-20) containing 2% dry milk (Amersham, Piscataway, NJ, USA) and incubated for h (SREBF1) or h (a-tubulin) with either mouse anti-SREBF1 IgG (BD Pharmingen, Mississauga, Canada) at a : 5000 dilution or mouse anti-a-tubulin IgG (Sigma, Saint Louis, MO, USA) at a : 30 000 dilution The blots were washed and proteins were visualized with horseradish peroxidase-conjugated 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) allowing gene expression analysis for 273 genes, including genes related to IFN response, apoptosis, proliferation, DNA repair, metabolism, and intracellular signaling (see supplementary Table S17 for the list of genes and supplementary Fig S1 for the array design) Results from reliable and validated 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 hybridization After cell transfection with siRNAs, total RNA was extracted with the Total RNAgents extraction kit (Promega), 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, providing 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- FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS S Vankoningsloo et al copy and low-copy expressed genes The scanned 16-bit images were imported into imagene 5.5 software (BioDiscovery, El Segundo, CA, USA) to quantify signal intensities 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 reference 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 homogeneity, 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 performed 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 (Hamburg, 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, siRNA off-target effects in different cell types Program ST4772-QUIV ⁄ ML, Namur, Belgium) The authors also acknowledge financial support through the Belgian Program on Interuniversity Attraction Poles (IAP ⁄ 02) and the ‘Action de Recherche ´ Concertee’ (ARC) funded by the ‘Gouvernement de la ´ Communaute Wallonie-Bruxelles’ References Fire A, Xu S, Montgomery MK, Kostas SA, Driver SE & Mello CC (1998) Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans Nature 391, 806–811 Chu CY & Rana TM (2007) Small RNAs: regulators and guardians of the genome J Cell Physiol 213, 412–419 Martin SE & Caplen NJ (2007) Applications of RNA interference in 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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 nontargeting (NT) siRNA at 100 nm (N100) or 20 nm (N20) on gene expression in 143B cells analyzed 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 h post-transfection (transfection duration of 24 h) Table S3 Effects of DharmaFECT1 (DF), LAMP2 ⁄ siRNA1 at 100 nm (L100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in 143B cells analyzed h post-transfection (transfection duration of 24 h) Table S4 Effects of DharmaFECT1 (DF), LAMP2 ⁄ siRNA2 at 100 nm (L100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in 143B cells analyzed h post-transfection (transfection duration of 24 h) Table S5 Effects of DharmaFECT1 (DF), SREBF1 ⁄ siRNA1 at 100 nm (S100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in A549 cells analyzed h post-transfection (transfection duration of 24 h) Table S6 Effects of DharmaFECT1 (DF), SREBF1 ⁄ siRNA2 at 100 nm (S100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in A549 FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS S Vankoningsloo et al cells analyzed h post-transfection (transfection duration of 24 h) Table S7 Effects of DharmaFECT1 (DF), LAMP2 ⁄ siRNA1 at 100 nm (L100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in A549 cells analyzed h post-transfection (transfection duration of 24 h) Table S8 Effects of DharmaFECT1 (DF), LAMP2 ⁄ siRNA2 at 100 nm (L100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in A549 cells analyzed h post-transfection (transfection duration of 24 h) Table S9 Effects of DharmaFECT1 (DF), SREBF1 ⁄ siRNA1 at 100 nm (S100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in IMR90 cells analyzed h post-transfection (transfection duration of 24 h) Table S10 Effects of DharmaFECT1 (DF), SREBF1 ⁄ siRNA2 at 100 nm (S100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in IMR90 cells analyzed h post-transfection (transfection duration of 24 h) Table S11 Effects of DharmaFECT1 (DF), LAMP2 ⁄ siRNA1 at 100 nm (L100) and the nontargeting (NT) siRNA at 100 nm (N100) on gene expression in IMR90 cells analyzed h post-transfection (transfection duration of 24 h) Table S12 Effects of DharmaFECT1 (DF), LAMP2 ⁄ siRNA2 at 100 nm (L100) and the nontargeting (NT) siRNA off-target effects in different cell types siRNA at 100 nm (N100) on gene expression in IMR90 cells analyzed h post-transfection (transfection 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 posttransfection (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 h or 24 h posttransfection (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 corresponding author for the article FEBS Journal 275 (2008) 2738–2753 ª 2008 The Authors Journal compilation ª 2008 FEBS 2753 ... 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... locations of the array allowed quantification of the local background and evaluation of the array homogeneity, which is taken into account in the normalization Furthermore, in order to consider the. .. 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