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Review

Experimental identification of microRNA targets

Ulf Andersson rom 1, Anders H. Lund

Biotech Research and Innovation Centre and Centre for Epigenetics, University of Copenhagen, Copenhagen, Denmark

a b s t r a c ta 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

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 computationalanalyses 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 miRNAare 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, differen-

tiation and development, cellular identity and pathogenhost inter-

actions (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

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 27 of the

miRNA) in the 3 UTR of target mRNAs (Lewis et al., 2003), which

together with criteria such as target sequence conservation make upthe 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

Gene 451 (2010) 15

Abbreviations: UTR, untranslated region; miRNA, microRNA; RISC, RNA-induced

silencing complex; SILAC,stableisotope labeling by amino acids in cell culture; HITS-CLIP,

high-throughput sequencing of RNAs isolated by cross-linking immunoprecipitation.

Corresponding author.

E-mail address: anders.lund@bric.ku.dk (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

Contents lists available at ScienceDirect

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

mailto:anders.lund@bric.ku.dkhttp://dx.doi.org/10.1016/j.gene.2009.11.008http://www.sciencedirect.com/science/journal/03781119http://www.sciencedirect.com/science/journal/03781119http://dx.doi.org/10.1016/j.gene.2009.11.008mailto:anders.lund@bric.ku.dk

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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 transcrip-

tome-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 off-

target effects resulting from miRNA overexpression, is to inhibit the

miRNA of interest with oligonucleotides complementary to the

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 signi-ficantly 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 andmost likelymany false positive candidates areincluded

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 bioinfor-

matic analyses for seed complementarity and subsequent experi-

mental 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 pairinginteractions. Toward this, several groups have reported progress using

different experimental approaches. Three reports address experimen-

tal 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. BothmRNA 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 contain-

ing miR-1 miRNA seed complementary sites in their 3 UTRs were

demonstrated to correlate with the expression level of the specific

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.

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miRNAs. The study shows as well the applicability of Ago immuno-

precipitation 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 wereidentified. 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 immunopre-

cipitation 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 individualmiRNA: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 anti-DIG 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 knock-

down of endogenous miR-30a-3p and isolating polysomal and sub-

polysomal fractions and comparing associated mRNAs on micro-

arrays, 8 mRNAs translationally induced upon miR-30a-3p knock-

down 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-30a-

3p 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 identifi

ed, none of them were previously predicted to be targets ofmiR-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 thebound

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 presump-

tion 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 propertiesof 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 regula-

tion 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 ofessential 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.

Thefirststudy to apply SILAC formiRNAtarget 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 themiRNAs miR-1,miR-

124 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-16and let-7band 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 instancesofspecific 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 theauthors 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 ofBMP7and 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 subsequentuseful 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

ofG 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 introducingcomplementary 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 processesstudied, 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 determi-nants 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, large-

scale 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 compre-

hensive 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 theoutput is protein levels. Whileit 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 themost 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. UA is supported by a

personal grant from the Danish Medical Research Council.

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