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Experimental identification of microRNA targets
Gene 451 (2010) 1–5
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Gene j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e n e
Review
Experimental identification of microRNA targets Ulf Andersson Ørom 1, Anders H. Lund ⁎ Biotech Research and Innovation Centre and Centre for Epigenetics, University of Copenhagen, Copenhagen, Denmark
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Article history: Received 8 October 2009 Received in revised form 10 November 2009 Accepted 16 November 2009 Available online 24 November 2009 Received by A. J. Van Wijren Keywords: microRNA Target identification
a b s t r a c t microRNAs are small RNAs that regulate protein synthesis post-transcriptionally. Animal microRNAs recognize their targets by incomplete base pairing to sequence motifs most often present in the 3′ untranslated region of their target mRNAs. This partial complementarity vastly expands the repertoire of potential targets and constitutes a problem for computational target prediction. Although computational analyses have shed light on important aspects of microRNA target recognition, several questions remain regarding how microRNAs can recognize and regulate their targets. Forward experimental approaches allow for an unbiased study of microRNA target recognition and may unveil novel, rare or uncommon target binding patterns. In this review we focus on animal microRNAs and the experimental approaches that have been described for identification of their targets. © 2009 Elsevier B.V. All rights reserved.
1. Introduction microRNAs (miRNAs) are uncapped, unpolyadenylated small RNAs that are processed from primary transcripts in sequential steps by the RNase III endonucleases Drosha in the nucleus (Lee et al., 2003) and Dicer in the cytoplasm (Hutvagner, 2005). Mature miRNA are incorporated into the RNA-induced silencing complex (RISC; Meister et al., 2004b) where they are bound by members of the Argonaute (Ago) family of proteins and constitute the target recognition module of RISC (Carthew and Sontheimer, 2009). Extensive research has revealed the existence of more than 700 different human miRNAs (Griffiths-Jones et al., 2008) and numerous reports have demonstrated the importance of miRNA-mediated regulation in key processes, such as proliferation, apoptosis, differentiation and development, cellular identity and pathogen–host interactions (He et al., 2007; Parker and Sheth, 2007; Pillai et al., 2007; Carthew and Sontheimer, 2009). Despite of this, the mechanisms by which miRNAs act are still not resolved. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has proven to be quite challenging in animals primarily due to the incomplete complementarity between miRNA and target mRNAs. Some key principles have emerged on the pattern of miRNA target recognition and these have been applied to computationally predict targets of miRNA regulation (Bartel, 2009). Examples of commonly
Abbreviations: UTR, untranslated region; miRNA, microRNA; RISC, RNA-induced silencing complex; SILAC, stable isotope labeling by amino acids in cell culture; HITS-CLIP, high-throughput sequencing of RNAs isolated by cross-linking immunoprecipitation. ⁎ Corresponding author. E-mail address: [email protected] (A.H. Lund). 1 Present address: The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, USA. 0378-1119/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.gene.2009.11.008
used algorithms are miRanda (John et al., 2004), TargetScan (Lewis et al., 2003, 2005) and PicTar (Krek et al., 2005). The most general feature of miRNA regulation described is the recognition of sequence motifs complementary to the seed region (nucleotides 2–7 of the miRNA) in the 3′ UTR of target mRNAs (Lewis et al., 2003), which together with criteria such as target sequence conservation make up the basis for most target prediction algorithms. It is currently unknown which proportion of miRNA interactions follow these rules and functional recognition motifs outside of the 3′ UTRs, not following the seed rule and target sequences that are not conserved between species, have been reported (Ha et al., 1996; Reinhart and Bartel, 2002; Vella et al., 2004b; Jopling et al., 2005; Krek et al., 2005; Didiano and Hobert, 2006; Easow et al., 2007; Orom et al., 2008; Tay et al., 2008; Tsai et al., 2009). Computational approaches to miRNA target identification are strong tools to narrow down the list of putative targets of miRNA regulation and have contributed significantly to the development of the miRNA field. However, a limitation of target predictions is that they rely on few established principles and as such cannot help in revealing novel aspects of miRNA target recognition. While several reports document the validity of predicted targets for miRNA regulation, many predicted targets do not recapitulate regulation in validation experiments (Nakamoto et al., 2005; Vinther et al., 2006; Frankel et al., 2008; Baek et al., 2008; Didiano and Hobert, 2008; Selbach et al., 2008; Jiang et al., 2009). A thorough study of miRNAs predicted to target CyclinD1 has addressed this using luciferase reporter assays (Jiang et al., 2009). Out of 45 miRNAs predicted to target the CyclinD1 3′ UTR only 7 could be confirmed by the authors (16%). While false positive predictions can be eliminated by experimental validation studies, the number of false negative predictions remains unknown. An unbiased approach to study miRNA interactions with their targets would provide much insight
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into additional recognition patterns and help as well to exclude false negative predictions. In this review, we describe the reported experimental approaches to identify the mRNA targets associated with specific miRNAs in animals (for overview, see Fig. 1). 2. Experimental target identification 2.1. Transcriptome analyses The realization that animal miRNAs down-regulate the level of a number of their target mRNAs (Bagga et al., 2005; Lim et al., 2005) paved the way for a series of overexpression and miRNA inhibition studies where miRNA targets were sought identified on a transcriptome-wide scale (Krutzfeldt et al., 2005; Christoffersen et al., 2007; Frankel et al., 2008; Grimson et al., 2007; Elmen et al., 2008a). Initial studies transiently transfected the tissue specific miRNAs miR-1 (muscle specific) and miR-124a (brain specific) into HeLa cells where they are normally not expressed and used microarray analyses to identify the cohort of mRNAs down-regulated as a consequence of miRNA overexpression (Lim et al., 2005). Subsequent analysis showed that target mRNA down-regulation is highly significantly associated with the presence of an miRNA seed complementary site in the mRNA 3′ UTR sequence. In addition, correlations between the mRNA targets and the miRNAs are shown: identified targets are primarily expressed at low levels in the tissues with high expression of the miRNAs (Farh et al., 2005; Lim et al., 2005). Furthermore, introducing the tissue specific miRNAs into HeLa cells shifted the mRNA expression profile toward that of the tissue normally expressing the miRNA, suggesting a very important role for miRNAs in tissue development and maintenance (Lim et al., 2005). The option to identify a large set of miRNA targets using microarrays has prompted other groups to take similar approaches to unravel miRNA functions both in cell culture and in vivo. A modified approach, in part trying to avoid offtarget effects resulting from miRNA overexpression, is to inhibit the miRNA of interest with oligonucleotides complementary to the
Fig. 1. Overview of approaches for experimentally identifying microRNA targets. microRNA regulation of translation is a multi-facetted process that allows several entrances for experimentally identifying the targets regulated by a specific microRNA. Reports address this issue through: (1) Analysis of mRNAs degraded as a consequence of overexpressing the microRNA and subsequent analysis of sequence motifs, (2) immunoprecipitation of tagged or endogenous RISC complex and analysis of associated mRNAs, (3) Affinity purification of tagged microRNAs and microarray analysis of associated mRNAs, (4) by using the observation that some microRNA targets move in the polysomal distribution upon microRNA targeting and analyzing differences in polysomal associated mRNAs with and without the microRNA, (5) analyzing protein production following labeling of proteins and mass spectrometry.
miRNA (Hutvagner et al., 2004; Meister et al., 2004a; Orom et al., 2006) and analyze mRNA levels on microarrays. When inhibiting the miRNA a subset of its targets will increase at both the protein and mRNA levels and potential targets can thus be readily identified (Krutzfeldt et al., 2005; Frankel et al., 2008ff; Elmen et al., 2008b; Christoffersen et al., 2009). Two reports apply both overexpression and inhibition of miRNAs (Nicolas et al., 2008; Ziegelbauer et al., 2009). By analyzing the overlap between these two series of experiments the list of putative direct target is significantly reduced. When miR-140 was either overexpressed or inhibited (Nicolas et al., 2008) a list of 1236 and 466 genes were reported as differentially expressed, while the overlap between the two experiments was only 49 transcripts. Twenty-one of these 49 mRNAs contain miR-140 seed complementary sites, yet none of them are predicted by commonly used miRNA target prediction algorithms, suggesting a significant number of false negative predictions by these algorithms. While these approaches can identify a subset of miRNA targets, they are limited to the mRNAs that are degraded to a certain extent by their targeting miRNAs, and the applications of such approaches have been highly dependent on computational analyses based on sequence complementarity. Such an approach yields many candidate target mRNAs that are differentially expressed upon exogenous introduction of miRNAs and most likely many false positive candidates are included due to downstream effects of the affected true miRNA mRNA targets. An approach to limit the number of false positives is to rely on seed site complementarity in the detected candidates. It is evident from these experiments that destabilization of target mRNAs is an important mechanism for miRNA function, on top of the strict translational repression without effects on mRNA levels. 2.2. Biochemical approaches Several known miRNA targets have been identified using bioinformatic analyses for seed complementarity and subsequent experimental and functional validation of the interaction. A more challenging task is to identify those targets regulated primarily at the level of translation, or recognized through non-seed base pairing interactions. Toward this, several groups have reported progress using different experimental approaches. Three reports address experimental miRNA target identification by immunoprecipitation of Ago proteins, either tagged or endogenous, to analyze the associated mRNAs as candidate miRNA targets. Karginov et al. used an epitope-tagged Ago2 in HEK293 to isolate targets of mir-124a, an miRNA not endogenously expressed in HEK293 cells (Karginov et al., 2007). Initial validation of the approach showed significant enrichment of three previously characterized targets of miR-124a, Ctdsp1, Plod3 and Vamp3, whereas a panel of housekeeping mRNAs was not enriched after immunoprecipitation of the myc-tagged Ago2. To identify a comprehensive set of miR-124a targets the myc-Ago2 immunoprecipitates were hybridized to microarrays along with determination of total mRNA levels. Both mRNA targets that are down-regulated in total mRNA and targets that are unaffected at the mRNA level by the miRNA were identified in the immunoprecipitates. Four of 4 down-regulated mRNA targets and 21 of 30 tested mRNAs that were not affected at total mRNA level were validated in luciferase reporter 3′ UTR assays, but a further characterization of the translationally regulated targets was not pursued. The paper shows that miRNA targets can be isolated and identified using Ago immunoprecipitation, identifying primarily those targets that are translationally repressed. Similar findings were demonstrated for miR-1 in a Drosophila system (Easow et al., 2007). Using immunoprecipitation of HA-tagged Ago1 proteins in S2 cells and subsequent microarray analysis, enrichments for mRNAs containing miR-1 miRNA seed complementary sites in their 3′ UTRs were demonstrated to correlate with the expression level of the specific
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miRNAs. The study shows as well the applicability of Ago immunoprecipitation for miRNA target identification, but lacks a thorough analysis of the identified targets. Rather the report focuses on the presence of miR-1 seeds in a subset of the identified potential targets of miR-1 regulation. Beitzinger et al. (2007) isolated endogenous Ago proteins from HEK293 cells using highly specific monoclonal antibodies against either human Ago1 or human Ago2 (Beitzinger et al., 2007). By purifying RNAs associated with either of the Ago proteins, cDNA synthesis and cloning, the associated mRNAs were identified. Analysis of the putative miRNA targets shows little overlap between Ago1- and Ago2-associated miRNA targets in human HEK293 cells, suggesting that specific pools of miRNAs or miRNA targets are associated to the different Ago proteins. About half of the suggested targets were predicted by at least one of the three applied target prediction methods: MiRanda (John et al., 2004), TargetScan (Lewis et al., 2003, 2005) or Pictar (Krek et al., 2005). For validation, 6 mRNAs predicted to be targets of miRNA regulation were selected. Cloning of their 3′ UTRs into a luciferase reporter vector and reporter assays with both miRNA overexpression or miRNA inhibition confirmed that these targets are regulated by the predicted miRNA through their 3′ UTRs. While all three studies report the identification of miRNA targets using experimental approaches, none of them address miRNA target recognition directly but tend to rely on miRNA seed site interaction for validation. The three papers show the potential of Ago immunoprecipitation as a means of identifying miRNA targets but at the same time they demonstrate the inherited difficulties in experimental miRNA target identification. While several thousands of mRNAs are hypothesized to be regulated by miRNAs, only a few are identified using these approaches. Tagging of the miRNA is another approach that has been employed to identify targets of miRNA regulation. By transfecting cells with miRNAs labeled with biotin and subsequently isolating the associated mRNAs, this method has been described for the well-characterized bantam/hid interaction in Drosophila both in reporter assays in HEK293 cells and for endogenous hid in S2 cells, where the hid 3′ UTR could be affinity purified using a biotin-tagged bantam miRNA (Orom and Lund, 2007). The method has been used to validate individual miRNA:target interactions (Kedde et al., 2007; Christoffersen et al., 2009) and to identify targets and suggest a novel function of the miRNA miR-10a (Orom et al., 2008). Surprisingly, it was found that miRNA-10a can target mRNAs encoding ribosomal proteins through their 5′ UTRs via non-seed interactions to enhance their translation, as well as modulate mRNA targets through their 3′ UTRs and repress their translation (Orom et al., 2008). Using this method, it was shown by cross-linking followed by primer extension mapping of the miRNA binding site that the non-canonical interaction is direct, which is also validated by mutating the miRNA target sequence and the corresponding bases in the miRNA to recover the enhancing effect observed of the miRNA. An in vitro procedure using digoxigenin-labeled miRNA precursors has also been employed (Hsu et al., 2009). By incubation with antiDIG antiserum known miRNA targets from C. elegans and zebrafish were confirmed using qPCR. Additionally the approach identified hand2 as a miR-1 target. Controversy exists about miRNA target association to polysomes. mRNAs targeted by miRNAs are both reported associated to polysomes while bound by miRNAs and reported to shuttle in the polysomal spectrum as a consequence of miRNA regulation (Olsen and Ambros 1999; Nelson et al., 2004; Nakamoto et al., 2005; Pillai et al., 2005; Petersen et al., 2006; Thermann and Hentze, 2007). Nakamoto et al. have used the assumption that the position of a transcript in a polysome profile reflects, in part, the degree of its translation. Hence, shifts into heavier polysome fractions would reflect increased translation (Nakamoto et al., 2005). Using knockdown of endogenous miR-30a-3p and isolating polysomal and sub-
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polysomal fractions and comparing associated mRNAs on microarrays, 8 mRNAs translationally induced upon miR-30a-3p knockdown were identified and validated as being targets of miR-30a-3p regulation. Despite that all 8 mRNAs contain seed sites (including G:U wobble pairs), none of them were predicted to be targets of miR-30a3p by the applied algorithms with a score above threshold. This study clearly demonstrates the applicability of forward approaches to identify miRNA targets. Even though only a few target candidates are identified, none of them were previously predicted to be targets of miR-30a-3p. A recent report using purification of cross-linked RNA-binding proteins has shed more light on miRNA target recognition (Chi et al., 2009). This approach, termed HITS-CLIP, uses ultraviolet light to cross-link Ago proteins to associated RNA and miRNA. Ago protein complexes were immunoprecipitated and purified from mouse brains and the associated RNA identified by sequencing. Clusters of Ago binding sites were then identified, which provided not only the bound transcript but also the position of Ago binding. The study identifies 1463 Ago clusters mapping to 829 transcripts. The identity of the miRNA bound to each target is not known with this approach. The authors use bioinformatics prediction to account for their presumption that the 20 most expressed miRNAs account for the majority of bound targets, however 27% of identified targets do not contain sequences corresponding to the 20 most expressed miRNAs. miRNAs are shown to bind mostly to 3′ UTRs but also to a large degree to the open reading frames of the identified targets, although it is unclear if these binding sites are functional. The brain specific miRNA miR-124 was used to compare to bioinformatics predictions for miR-124 targets. Interestingly, there is a substantial overlap between targets identified for miR-124 using HITS-CLIP and computationally predicted transcripts, although the experimental approach identifies fewer binding sites for Agos in each transcript. This study provides insight on miRNA target recognition and can potentially assist in unraveling as yet uncharacterized patterns of miRNA target recognition, as the approach not only can help identify the targets of miRNA regulation but also define the region within which the interaction takes place. The option of studying single miRNAs with this approach would give even more insightful knowledge on the target recognition properties of a single miRNA without having to guess some of the interactions or make assumptions of which miRNAs are binding the identified target mRNA. Currently, further development of this method is ongoing in several laboratories. 2.3. Proteome analyses Several proteomic approaches for studying miRNA target regulation using stable isotope labeling by amino acids in cell culture (SILAC) have been reported (Vinther et al., 2006; Baek et al., 2008; Selbach et al., 2008). This experimental approach is appealing as it may identify targets regulated both by transcript destabilization and translational repression. With SILAC, proteins are metabolically labeled by growing cells in medium containing heavy isotopes of essential amino acids typically lysine and arginine. Using mass spectrometry, differences in protein synthesis can be determined by the ratio of peptide peak intensities from the light and heavy isotopes. The first study to apply SILAC for miRNA target identification found 12 targets for the miRNA miR-1 in HeLa cells (Vinther et al., 2006). Eight of the 12 identified targets contain seed complementary sites in their 3′ UTRs. A comparison with mRNA microarray analysis studies of miR-1 targets in HeLa cells (Lim et al., 2005) showed that four of these targets overlap between the two studies using different approaches to address the same question. Luciferase reporter validation of 3′ UTRs of the identified target genes supported 6 of the putative target mRNAs identified, underlining the applicability of the method for miRNA target identification. Following this report, two large-scale proteomics studies to identify miRNA targets have been published (Baek et al.,
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2008; Selbach et al., 2008). Baek et al. studied the miRNAs miR-1, miR124 and miR-181 in HeLa cells and the effect of removing miR-223 in mouse neutrophils. Selbach et al. used a slightly modified SILAC procedure where cells were pulse-labeled to incorporate the isotopes primarily into newly synthesized proteins, and studied the miRNAs miR-1, miR-30, miR-155, miR-16 and let-7b and knock-down of let-7b in HeLa cells. While one large-scale study reports primarily effects at the level of mRNA stability (Baek et al., 2008), another observes more instances of specific translational inhibition (Selbach et al., 2008). Common to the two reports is that they show effects of single miRNAs on hundreds of proteins, albeit with a bias toward the detection of proteins expressed at a higher level. Most of these effects are modest, making it hard to distinguish primary miRNA effects from secondary effects. Analyses for predicted binding sites in the 3′ UTRs show enrichment for the presence of seed sites. The small effects observed lead the authors to suggest that an important role of miRNAs might be the fine-tuning of the expression of many proteins. In addition, several putative targets show up-regulation of protein synthesis, suggesting a general enhancing effect of miRNAs (Selbach et al., 2008), either indirect or direct, on a large number of proteins. An example of a clinically applicable small-scale proteomics approach using reverse-phase protein miRNA analysis has been described (Iliopoulos et al., 2008). Comparison of miRNA expression and reverse-phase protein arrays probed with 214 antibodies in combination with miRNA target prediction identified a number of putative targets of miRNA regulation involved in the pathogenesis of osteoarthritis. The study identified and validated the regulation by miR-22 of BMP7 and PPARa. While the approach relies completely on target prediction algorithms, it is advantageous for analysis of clinical samples where the amount of sample is limited. 3. Discussion When considering the several approaches reported to successfully identify mRNA targets of miRNA regulation only few experimentally identified and functionally validated miRNA targets exist. This likely reflects the challenge of miRNA target identification and subsequent useful functional validation. miRNA target validation focusing on computationally predicted targets has been discussed recently (Kuhn et al., 2008; Bartel, 2009). For experimentally identified targets, functional validation is more relevant than computational analyses. Approaches such as calculation of −ΔG values are mostly useful to narrow down the number of putative candidate target mRNAs from bioinformatics analyses and may also exclude true targets. Effects on endogenous target protein levels serve as good indicators for valid miRNA target interactions, although indirect effects cannot be excluded from these experiments. A more direct validation, although not in its natural context, can be obtained by cloning a sequence of the mRNA of interest into a luciferase reporter and do co-transfection reporter assays. By mutating the identified target site and subsequently introducing complementary mutations into the miRNA sequence, abrogation and restoration of the translational effect on the reporter should be observed for a true miRNA target. This approach suffers from the limitation that both target and miRNA are present at artificially high concentrations, which may affect the effect observed (Doench and Sharp, 2004). Furthermore, direct evidence that an mRNA is endogenously bound by an miRNA can be obtained by using either formaldehyde cross-linking of the miRNA to its targets (Vasudevan et al., 2007) or 4-thiouridine-modified miRNAs (Orom et al., 2008), that allows for subsequent mapping of the exact site of binding using primer extension. The data obtained from experimental approaches to identify miRNA targets should, in addition to identifying targets involved in the processes studied, be used to characterize miRNA binding patterns
further. Most of the approaches described in this review resort to using the proposed seed pattern of miRNA recognition of their targets as a validation criterion for the success of their approach, rather than asking which patterns of recognition can be deduced from their data. Flanking sequences outside of the miRNA recognition site have been suggested to have important regulatory functions for a number of miRNAs (Vella et al., 2004a; Didiano and Hobert, 2006; Grimson et al., 2007; Kertesz et al., 2007; Didiano and Hobert, 2008), but very little has been done so far toward identifying additional mRNA determinants for miRNA binding and function. A major problem with an unbiased forward approach in target site analysis is the rather limited number of experimentally identified and validated targets each approach has revealed. With the recent, largescale proteomic approaches, together with genome-wide mapping of miRNA binding regions coming from techniques such as HITS-CLIPS, this may no longer be a limitation. 4. Conclusion Identifying targets of miRNA regulation remains a fundamental challenge and the lack of knowledge concerning the different mechanisms by which miRNAs work constitutes a major problem for experimental target identification. Hence, a combination of target identification methods may turn out to be necessary to reveal the full spectrum of miRNA target regulation. While the approaches applying Ago tagging and immunoprecipitation will likely miss degraded mRNAs, these are readily picked up by transfection and microarray approaches, which in turn cannot be used to identify targets that are exclusively regulated at the level of translation. The most comprehensive approach described so far for miRNA target identification is the proteomics approach reported by three different groups (Vinther et al., 2006; Baek et al., 2008; Selbach et al., 2008), and such an approach should be able to pick up all kinds of repression by the miRNA, as the output is protein levels. While it remains problematic to distinguish primary and secondary effects without relying on extensive experimental validation or on computational predictions, global proteomics approaches could reveal new aspects of miRNA target site recognition and function. While repression is by far the most commonly reported effect of miRNA targeting of an mRNA, enhancement of translation by miRNAs has been observed by a handful of groups so far (Vasudevan et al., 2007; Henke et al., 2008; Orom et al., 2008; Selbach et al., 2008; Iwasaki and Tomari, 2009; Tsai et al., 2009), two of which are based on experimental target identification. This could be a consequence of different miRNA recognition motifs, of mRNA sequence context, or as recently suggested due to cell cycle-dependent differences in miRNA functions (Vasudevan et al., 2007). In summary, experimental identification of miRNA targets should to a higher extent be used to expand the current knowledge of miRNA target recognition and broadening of the spectrum of miRNA targets. Acknowledgments Work in the authors' laboratory is supported by EC FP7 funding (ONCOMIRS, Grant Agreement Number 201102. This publication reflects only the authors' views. The commission is not liable for any use that may be made of the information herein), the Novo Nordisk Foundation, the Danish National Research Foundation, the Danish Medical Research Council, the Danish Cancer Society and the Danish National Advanced Technology Foundation. UAØ is supported by a personal grant from the Danish Medical Research Council. References Baek, D., Villen, J., Shin, C., Camargo, F.D., Gygi, S.P., Bartel, D.P., 2008. The impact of microRNAs on protein output. Nature 455, 64–71.
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Gene j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e n e
Review
Experimental identification of microRNA targets Ulf Andersson Ørom 1, Anders H. Lund ⁎ Biotech Research and Innovation Centre and Centre for Epigenetics, University of Copenhagen, Copenhagen, Denmark
a r t i c l e
i n f o
Article history: Received 8 October 2009 Received in revised form 10 November 2009 Accepted 16 November 2009 Available online 24 November 2009 Received by A. J. Van Wijren Keywords: microRNA Target identification
a b s t r a c t microRNAs are small RNAs that regulate protein synthesis post-transcriptionally. Animal microRNAs recognize their targets by incomplete base pairing to sequence motifs most often present in the 3′ untranslated region of their target mRNAs. This partial complementarity vastly expands the repertoire of potential targets and constitutes a problem for computational target prediction. Although computational analyses have shed light on important aspects of microRNA target recognition, several questions remain regarding how microRNAs can recognize and regulate their targets. Forward experimental approaches allow for an unbiased study of microRNA target recognition and may unveil novel, rare or uncommon target binding patterns. In this review we focus on animal microRNAs and the experimental approaches that have been described for identification of their targets. © 2009 Elsevier B.V. All rights reserved.
1. Introduction microRNAs (miRNAs) are uncapped, unpolyadenylated small RNAs that are processed from primary transcripts in sequential steps by the RNase III endonucleases Drosha in the nucleus (Lee et al., 2003) and Dicer in the cytoplasm (Hutvagner, 2005). Mature miRNA are incorporated into the RNA-induced silencing complex (RISC; Meister et al., 2004b) where they are bound by members of the Argonaute (Ago) family of proteins and constitute the target recognition module of RISC (Carthew and Sontheimer, 2009). Extensive research has revealed the existence of more than 700 different human miRNAs (Griffiths-Jones et al., 2008) and numerous reports have demonstrated the importance of miRNA-mediated regulation in key processes, such as proliferation, apoptosis, differentiation and development, cellular identity and pathogen–host interactions (He et al., 2007; Parker and Sheth, 2007; Pillai et al., 2007; Carthew and Sontheimer, 2009). Despite of this, the mechanisms by which miRNAs act are still not resolved. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has proven to be quite challenging in animals primarily due to the incomplete complementarity between miRNA and target mRNAs. Some key principles have emerged on the pattern of miRNA target recognition and these have been applied to computationally predict targets of miRNA regulation (Bartel, 2009). Examples of commonly
Abbreviations: UTR, untranslated region; miRNA, microRNA; RISC, RNA-induced silencing complex; SILAC, stable isotope labeling by amino acids in cell culture; HITS-CLIP, high-throughput sequencing of RNAs isolated by cross-linking immunoprecipitation. ⁎ Corresponding author. E-mail address: [email protected] (A.H. Lund). 1 Present address: The Wistar Institute, 3601 Spruce Street, Philadelphia, PA, USA. 0378-1119/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.gene.2009.11.008
used algorithms are miRanda (John et al., 2004), TargetScan (Lewis et al., 2003, 2005) and PicTar (Krek et al., 2005). The most general feature of miRNA regulation described is the recognition of sequence motifs complementary to the seed region (nucleotides 2–7 of the miRNA) in the 3′ UTR of target mRNAs (Lewis et al., 2003), which together with criteria such as target sequence conservation make up the basis for most target prediction algorithms. It is currently unknown which proportion of miRNA interactions follow these rules and functional recognition motifs outside of the 3′ UTRs, not following the seed rule and target sequences that are not conserved between species, have been reported (Ha et al., 1996; Reinhart and Bartel, 2002; Vella et al., 2004b; Jopling et al., 2005; Krek et al., 2005; Didiano and Hobert, 2006; Easow et al., 2007; Orom et al., 2008; Tay et al., 2008; Tsai et al., 2009). Computational approaches to miRNA target identification are strong tools to narrow down the list of putative targets of miRNA regulation and have contributed significantly to the development of the miRNA field. However, a limitation of target predictions is that they rely on few established principles and as such cannot help in revealing novel aspects of miRNA target recognition. While several reports document the validity of predicted targets for miRNA regulation, many predicted targets do not recapitulate regulation in validation experiments (Nakamoto et al., 2005; Vinther et al., 2006; Frankel et al., 2008; Baek et al., 2008; Didiano and Hobert, 2008; Selbach et al., 2008; Jiang et al., 2009). A thorough study of miRNAs predicted to target CyclinD1 has addressed this using luciferase reporter assays (Jiang et al., 2009). Out of 45 miRNAs predicted to target the CyclinD1 3′ UTR only 7 could be confirmed by the authors (16%). While false positive predictions can be eliminated by experimental validation studies, the number of false negative predictions remains unknown. An unbiased approach to study miRNA interactions with their targets would provide much insight
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into additional recognition patterns and help as well to exclude false negative predictions. In this review, we describe the reported experimental approaches to identify the mRNA targets associated with specific miRNAs in animals (for overview, see Fig. 1). 2. Experimental target identification 2.1. Transcriptome analyses The realization that animal miRNAs down-regulate the level of a number of their target mRNAs (Bagga et al., 2005; Lim et al., 2005) paved the way for a series of overexpression and miRNA inhibition studies where miRNA targets were sought identified on a transcriptome-wide scale (Krutzfeldt et al., 2005; Christoffersen et al., 2007; Frankel et al., 2008; Grimson et al., 2007; Elmen et al., 2008a). Initial studies transiently transfected the tissue specific miRNAs miR-1 (muscle specific) and miR-124a (brain specific) into HeLa cells where they are normally not expressed and used microarray analyses to identify the cohort of mRNAs down-regulated as a consequence of miRNA overexpression (Lim et al., 2005). Subsequent analysis showed that target mRNA down-regulation is highly significantly associated with the presence of an miRNA seed complementary site in the mRNA 3′ UTR sequence. In addition, correlations between the mRNA targets and the miRNAs are shown: identified targets are primarily expressed at low levels in the tissues with high expression of the miRNAs (Farh et al., 2005; Lim et al., 2005). Furthermore, introducing the tissue specific miRNAs into HeLa cells shifted the mRNA expression profile toward that of the tissue normally expressing the miRNA, suggesting a very important role for miRNAs in tissue development and maintenance (Lim et al., 2005). The option to identify a large set of miRNA targets using microarrays has prompted other groups to take similar approaches to unravel miRNA functions both in cell culture and in vivo. A modified approach, in part trying to avoid offtarget effects resulting from miRNA overexpression, is to inhibit the miRNA of interest with oligonucleotides complementary to the
Fig. 1. Overview of approaches for experimentally identifying microRNA targets. microRNA regulation of translation is a multi-facetted process that allows several entrances for experimentally identifying the targets regulated by a specific microRNA. Reports address this issue through: (1) Analysis of mRNAs degraded as a consequence of overexpressing the microRNA and subsequent analysis of sequence motifs, (2) immunoprecipitation of tagged or endogenous RISC complex and analysis of associated mRNAs, (3) Affinity purification of tagged microRNAs and microarray analysis of associated mRNAs, (4) by using the observation that some microRNA targets move in the polysomal distribution upon microRNA targeting and analyzing differences in polysomal associated mRNAs with and without the microRNA, (5) analyzing protein production following labeling of proteins and mass spectrometry.
miRNA (Hutvagner et al., 2004; Meister et al., 2004a; Orom et al., 2006) and analyze mRNA levels on microarrays. When inhibiting the miRNA a subset of its targets will increase at both the protein and mRNA levels and potential targets can thus be readily identified (Krutzfeldt et al., 2005; Frankel et al., 2008ff; Elmen et al., 2008b; Christoffersen et al., 2009). Two reports apply both overexpression and inhibition of miRNAs (Nicolas et al., 2008; Ziegelbauer et al., 2009). By analyzing the overlap between these two series of experiments the list of putative direct target is significantly reduced. When miR-140 was either overexpressed or inhibited (Nicolas et al., 2008) a list of 1236 and 466 genes were reported as differentially expressed, while the overlap between the two experiments was only 49 transcripts. Twenty-one of these 49 mRNAs contain miR-140 seed complementary sites, yet none of them are predicted by commonly used miRNA target prediction algorithms, suggesting a significant number of false negative predictions by these algorithms. While these approaches can identify a subset of miRNA targets, they are limited to the mRNAs that are degraded to a certain extent by their targeting miRNAs, and the applications of such approaches have been highly dependent on computational analyses based on sequence complementarity. Such an approach yields many candidate target mRNAs that are differentially expressed upon exogenous introduction of miRNAs and most likely many false positive candidates are included due to downstream effects of the affected true miRNA mRNA targets. An approach to limit the number of false positives is to rely on seed site complementarity in the detected candidates. It is evident from these experiments that destabilization of target mRNAs is an important mechanism for miRNA function, on top of the strict translational repression without effects on mRNA levels. 2.2. Biochemical approaches Several known miRNA targets have been identified using bioinformatic analyses for seed complementarity and subsequent experimental and functional validation of the interaction. A more challenging task is to identify those targets regulated primarily at the level of translation, or recognized through non-seed base pairing interactions. Toward this, several groups have reported progress using different experimental approaches. Three reports address experimental miRNA target identification by immunoprecipitation of Ago proteins, either tagged or endogenous, to analyze the associated mRNAs as candidate miRNA targets. Karginov et al. used an epitope-tagged Ago2 in HEK293 to isolate targets of mir-124a, an miRNA not endogenously expressed in HEK293 cells (Karginov et al., 2007). Initial validation of the approach showed significant enrichment of three previously characterized targets of miR-124a, Ctdsp1, Plod3 and Vamp3, whereas a panel of housekeeping mRNAs was not enriched after immunoprecipitation of the myc-tagged Ago2. To identify a comprehensive set of miR-124a targets the myc-Ago2 immunoprecipitates were hybridized to microarrays along with determination of total mRNA levels. Both mRNA targets that are down-regulated in total mRNA and targets that are unaffected at the mRNA level by the miRNA were identified in the immunoprecipitates. Four of 4 down-regulated mRNA targets and 21 of 30 tested mRNAs that were not affected at total mRNA level were validated in luciferase reporter 3′ UTR assays, but a further characterization of the translationally regulated targets was not pursued. The paper shows that miRNA targets can be isolated and identified using Ago immunoprecipitation, identifying primarily those targets that are translationally repressed. Similar findings were demonstrated for miR-1 in a Drosophila system (Easow et al., 2007). Using immunoprecipitation of HA-tagged Ago1 proteins in S2 cells and subsequent microarray analysis, enrichments for mRNAs containing miR-1 miRNA seed complementary sites in their 3′ UTRs were demonstrated to correlate with the expression level of the specific
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miRNAs. The study shows as well the applicability of Ago immunoprecipitation for miRNA target identification, but lacks a thorough analysis of the identified targets. Rather the report focuses on the presence of miR-1 seeds in a subset of the identified potential targets of miR-1 regulation. Beitzinger et al. (2007) isolated endogenous Ago proteins from HEK293 cells using highly specific monoclonal antibodies against either human Ago1 or human Ago2 (Beitzinger et al., 2007). By purifying RNAs associated with either of the Ago proteins, cDNA synthesis and cloning, the associated mRNAs were identified. Analysis of the putative miRNA targets shows little overlap between Ago1- and Ago2-associated miRNA targets in human HEK293 cells, suggesting that specific pools of miRNAs or miRNA targets are associated to the different Ago proteins. About half of the suggested targets were predicted by at least one of the three applied target prediction methods: MiRanda (John et al., 2004), TargetScan (Lewis et al., 2003, 2005) or Pictar (Krek et al., 2005). For validation, 6 mRNAs predicted to be targets of miRNA regulation were selected. Cloning of their 3′ UTRs into a luciferase reporter vector and reporter assays with both miRNA overexpression or miRNA inhibition confirmed that these targets are regulated by the predicted miRNA through their 3′ UTRs. While all three studies report the identification of miRNA targets using experimental approaches, none of them address miRNA target recognition directly but tend to rely on miRNA seed site interaction for validation. The three papers show the potential of Ago immunoprecipitation as a means of identifying miRNA targets but at the same time they demonstrate the inherited difficulties in experimental miRNA target identification. While several thousands of mRNAs are hypothesized to be regulated by miRNAs, only a few are identified using these approaches. Tagging of the miRNA is another approach that has been employed to identify targets of miRNA regulation. By transfecting cells with miRNAs labeled with biotin and subsequently isolating the associated mRNAs, this method has been described for the well-characterized bantam/hid interaction in Drosophila both in reporter assays in HEK293 cells and for endogenous hid in S2 cells, where the hid 3′ UTR could be affinity purified using a biotin-tagged bantam miRNA (Orom and Lund, 2007). The method has been used to validate individual miRNA:target interactions (Kedde et al., 2007; Christoffersen et al., 2009) and to identify targets and suggest a novel function of the miRNA miR-10a (Orom et al., 2008). Surprisingly, it was found that miRNA-10a can target mRNAs encoding ribosomal proteins through their 5′ UTRs via non-seed interactions to enhance their translation, as well as modulate mRNA targets through their 3′ UTRs and repress their translation (Orom et al., 2008). Using this method, it was shown by cross-linking followed by primer extension mapping of the miRNA binding site that the non-canonical interaction is direct, which is also validated by mutating the miRNA target sequence and the corresponding bases in the miRNA to recover the enhancing effect observed of the miRNA. An in vitro procedure using digoxigenin-labeled miRNA precursors has also been employed (Hsu et al., 2009). By incubation with antiDIG antiserum known miRNA targets from C. elegans and zebrafish were confirmed using qPCR. Additionally the approach identified hand2 as a miR-1 target. Controversy exists about miRNA target association to polysomes. mRNAs targeted by miRNAs are both reported associated to polysomes while bound by miRNAs and reported to shuttle in the polysomal spectrum as a consequence of miRNA regulation (Olsen and Ambros 1999; Nelson et al., 2004; Nakamoto et al., 2005; Pillai et al., 2005; Petersen et al., 2006; Thermann and Hentze, 2007). Nakamoto et al. have used the assumption that the position of a transcript in a polysome profile reflects, in part, the degree of its translation. Hence, shifts into heavier polysome fractions would reflect increased translation (Nakamoto et al., 2005). Using knockdown of endogenous miR-30a-3p and isolating polysomal and sub-
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polysomal fractions and comparing associated mRNAs on microarrays, 8 mRNAs translationally induced upon miR-30a-3p knockdown were identified and validated as being targets of miR-30a-3p regulation. Despite that all 8 mRNAs contain seed sites (including G:U wobble pairs), none of them were predicted to be targets of miR-30a3p by the applied algorithms with a score above threshold. This study clearly demonstrates the applicability of forward approaches to identify miRNA targets. Even though only a few target candidates are identified, none of them were previously predicted to be targets of miR-30a-3p. A recent report using purification of cross-linked RNA-binding proteins has shed more light on miRNA target recognition (Chi et al., 2009). This approach, termed HITS-CLIP, uses ultraviolet light to cross-link Ago proteins to associated RNA and miRNA. Ago protein complexes were immunoprecipitated and purified from mouse brains and the associated RNA identified by sequencing. Clusters of Ago binding sites were then identified, which provided not only the bound transcript but also the position of Ago binding. The study identifies 1463 Ago clusters mapping to 829 transcripts. The identity of the miRNA bound to each target is not known with this approach. The authors use bioinformatics prediction to account for their presumption that the 20 most expressed miRNAs account for the majority of bound targets, however 27% of identified targets do not contain sequences corresponding to the 20 most expressed miRNAs. miRNAs are shown to bind mostly to 3′ UTRs but also to a large degree to the open reading frames of the identified targets, although it is unclear if these binding sites are functional. The brain specific miRNA miR-124 was used to compare to bioinformatics predictions for miR-124 targets. Interestingly, there is a substantial overlap between targets identified for miR-124 using HITS-CLIP and computationally predicted transcripts, although the experimental approach identifies fewer binding sites for Agos in each transcript. This study provides insight on miRNA target recognition and can potentially assist in unraveling as yet uncharacterized patterns of miRNA target recognition, as the approach not only can help identify the targets of miRNA regulation but also define the region within which the interaction takes place. The option of studying single miRNAs with this approach would give even more insightful knowledge on the target recognition properties of a single miRNA without having to guess some of the interactions or make assumptions of which miRNAs are binding the identified target mRNA. Currently, further development of this method is ongoing in several laboratories. 2.3. Proteome analyses Several proteomic approaches for studying miRNA target regulation using stable isotope labeling by amino acids in cell culture (SILAC) have been reported (Vinther et al., 2006; Baek et al., 2008; Selbach et al., 2008). This experimental approach is appealing as it may identify targets regulated both by transcript destabilization and translational repression. With SILAC, proteins are metabolically labeled by growing cells in medium containing heavy isotopes of essential amino acids typically lysine and arginine. Using mass spectrometry, differences in protein synthesis can be determined by the ratio of peptide peak intensities from the light and heavy isotopes. The first study to apply SILAC for miRNA target identification found 12 targets for the miRNA miR-1 in HeLa cells (Vinther et al., 2006). Eight of the 12 identified targets contain seed complementary sites in their 3′ UTRs. A comparison with mRNA microarray analysis studies of miR-1 targets in HeLa cells (Lim et al., 2005) showed that four of these targets overlap between the two studies using different approaches to address the same question. Luciferase reporter validation of 3′ UTRs of the identified target genes supported 6 of the putative target mRNAs identified, underlining the applicability of the method for miRNA target identification. Following this report, two large-scale proteomics studies to identify miRNA targets have been published (Baek et al.,
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2008; Selbach et al., 2008). Baek et al. studied the miRNAs miR-1, miR124 and miR-181 in HeLa cells and the effect of removing miR-223 in mouse neutrophils. Selbach et al. used a slightly modified SILAC procedure where cells were pulse-labeled to incorporate the isotopes primarily into newly synthesized proteins, and studied the miRNAs miR-1, miR-30, miR-155, miR-16 and let-7b and knock-down of let-7b in HeLa cells. While one large-scale study reports primarily effects at the level of mRNA stability (Baek et al., 2008), another observes more instances of specific translational inhibition (Selbach et al., 2008). Common to the two reports is that they show effects of single miRNAs on hundreds of proteins, albeit with a bias toward the detection of proteins expressed at a higher level. Most of these effects are modest, making it hard to distinguish primary miRNA effects from secondary effects. Analyses for predicted binding sites in the 3′ UTRs show enrichment for the presence of seed sites. The small effects observed lead the authors to suggest that an important role of miRNAs might be the fine-tuning of the expression of many proteins. In addition, several putative targets show up-regulation of protein synthesis, suggesting a general enhancing effect of miRNAs (Selbach et al., 2008), either indirect or direct, on a large number of proteins. An example of a clinically applicable small-scale proteomics approach using reverse-phase protein miRNA analysis has been described (Iliopoulos et al., 2008). Comparison of miRNA expression and reverse-phase protein arrays probed with 214 antibodies in combination with miRNA target prediction identified a number of putative targets of miRNA regulation involved in the pathogenesis of osteoarthritis. The study identified and validated the regulation by miR-22 of BMP7 and PPARa. While the approach relies completely on target prediction algorithms, it is advantageous for analysis of clinical samples where the amount of sample is limited. 3. Discussion When considering the several approaches reported to successfully identify mRNA targets of miRNA regulation only few experimentally identified and functionally validated miRNA targets exist. This likely reflects the challenge of miRNA target identification and subsequent useful functional validation. miRNA target validation focusing on computationally predicted targets has been discussed recently (Kuhn et al., 2008; Bartel, 2009). For experimentally identified targets, functional validation is more relevant than computational analyses. Approaches such as calculation of −ΔG values are mostly useful to narrow down the number of putative candidate target mRNAs from bioinformatics analyses and may also exclude true targets. Effects on endogenous target protein levels serve as good indicators for valid miRNA target interactions, although indirect effects cannot be excluded from these experiments. A more direct validation, although not in its natural context, can be obtained by cloning a sequence of the mRNA of interest into a luciferase reporter and do co-transfection reporter assays. By mutating the identified target site and subsequently introducing complementary mutations into the miRNA sequence, abrogation and restoration of the translational effect on the reporter should be observed for a true miRNA target. This approach suffers from the limitation that both target and miRNA are present at artificially high concentrations, which may affect the effect observed (Doench and Sharp, 2004). Furthermore, direct evidence that an mRNA is endogenously bound by an miRNA can be obtained by using either formaldehyde cross-linking of the miRNA to its targets (Vasudevan et al., 2007) or 4-thiouridine-modified miRNAs (Orom et al., 2008), that allows for subsequent mapping of the exact site of binding using primer extension. The data obtained from experimental approaches to identify miRNA targets should, in addition to identifying targets involved in the processes studied, be used to characterize miRNA binding patterns
further. Most of the approaches described in this review resort to using the proposed seed pattern of miRNA recognition of their targets as a validation criterion for the success of their approach, rather than asking which patterns of recognition can be deduced from their data. Flanking sequences outside of the miRNA recognition site have been suggested to have important regulatory functions for a number of miRNAs (Vella et al., 2004a; Didiano and Hobert, 2006; Grimson et al., 2007; Kertesz et al., 2007; Didiano and Hobert, 2008), but very little has been done so far toward identifying additional mRNA determinants for miRNA binding and function. A major problem with an unbiased forward approach in target site analysis is the rather limited number of experimentally identified and validated targets each approach has revealed. With the recent, largescale proteomic approaches, together with genome-wide mapping of miRNA binding regions coming from techniques such as HITS-CLIPS, this may no longer be a limitation. 4. Conclusion Identifying targets of miRNA regulation remains a fundamental challenge and the lack of knowledge concerning the different mechanisms by which miRNAs work constitutes a major problem for experimental target identification. Hence, a combination of target identification methods may turn out to be necessary to reveal the full spectrum of miRNA target regulation. While the approaches applying Ago tagging and immunoprecipitation will likely miss degraded mRNAs, these are readily picked up by transfection and microarray approaches, which in turn cannot be used to identify targets that are exclusively regulated at the level of translation. The most comprehensive approach described so far for miRNA target identification is the proteomics approach reported by three different groups (Vinther et al., 2006; Baek et al., 2008; Selbach et al., 2008), and such an approach should be able to pick up all kinds of repression by the miRNA, as the output is protein levels. While it remains problematic to distinguish primary and secondary effects without relying on extensive experimental validation or on computational predictions, global proteomics approaches could reveal new aspects of miRNA target site recognition and function. While repression is by far the most commonly reported effect of miRNA targeting of an mRNA, enhancement of translation by miRNAs has been observed by a handful of groups so far (Vasudevan et al., 2007; Henke et al., 2008; Orom et al., 2008; Selbach et al., 2008; Iwasaki and Tomari, 2009; Tsai et al., 2009), two of which are based on experimental target identification. This could be a consequence of different miRNA recognition motifs, of mRNA sequence context, or as recently suggested due to cell cycle-dependent differences in miRNA functions (Vasudevan et al., 2007). In summary, experimental identification of miRNA targets should to a higher extent be used to expand the current knowledge of miRNA target recognition and broadening of the spectrum of miRNA targets. Acknowledgments Work in the authors' laboratory is supported by EC FP7 funding (ONCOMIRS, Grant Agreement Number 201102. This publication reflects only the authors' views. The commission is not liable for any use that may be made of the information herein), the Novo Nordisk Foundation, the Danish National Research Foundation, the Danish Medical Research Council, the Danish Cancer Society and the Danish National Advanced Technology Foundation. UAØ is supported by a personal grant from the Danish Medical Research Council. References Baek, D., Villen, J., Shin, C., Camargo, F.D., Gygi, S.P., Bartel, D.P., 2008. The impact of microRNAs on protein output. Nature 455, 64–71.
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