A P P L I C AT I O N N O T E

Reciprocal
microRNA
modulation
identifies genetargets in abiological context.

Abstract
Through their effect in down-regulation of gene transcripts,
microRNAs play an important role in a range of biologicalprocesses such as development, differentiation, and disease.
Although many methods have been proposed to discovermessenger RNA (mRNA)-microRNA interactions, most areeither time consuming or lack accuracy. Here, we present anempirical method for identifying direct microRNA targets using
reciprocal mRNA expression response to mimic and inhibitor
treatments. The method is supported through analysis of theenriched presence of microRNA seed complement sequences.
Using reverse transcription quantitative real-time PCR (RT-qPCR)
for confirmation, empirically determined miR-122 targets werecompared to targets predicted by widely used microRNA target

Authors
Maren M. Gross
Emily M. Anderson
Kevin Sullivan
Barbara Robertson
Amanda Birmingham
Devin Leake
Annaleen Vermeulen
Revvity, Inc.

prediction programs. Overall, we conclude empirical detection isa more time efficient way to identify mRNA-microRNA interactions
than selecting targets from lists generated by microRNA-target
prediction programs.

Reciprocal microRNA modulation identifies gene targets in a biological context.

Introduction

Since high throughput gene expression microarray analysis

microRNAs are a class of small non-coding RNAs, 22

we used this as a basis for a robust method to efficiently

nucleotides (nt) in length, involved in post-transcriptional gene

identify microRNA targets. Expression changes of a direct

regulation of up to 50% of protein-coding genes . Mammaliangene target should be anti-correlated when treated withmature microRNAs down-regulate gene expression primarily

mimics and inhibitors independently. Mimics typically haveby binding to the 3’ UTR . The mRNA-microRNA binding

stronger, more robust effects while inhibitors have more

occurs through the seed region of the microRNA (minimallysubtle effects26; therefore, when using inhibitors alone,

positions 2-7 from the 5’ end of the mature strand) . It ismany researchers have been unable to distinguish primaryestimated that each microRNA family member may regulate

effects from background noise in small experiments.

hundreds of gene targets . Even though some rules for mRNA-

Previous studies using gene expression microarrays havemicroRNA target recognition have been elucidated, prediction

focused on use of microRNA mimics alone12, 27-29 with few

programs do not yet identify all targets

exceptions where both mimics and inhibitors were used inremains a widely used, fast, and cost-effective approach,

1-3

4-7

1

8

.

9-11

Current methods for microRNA target discovery utilizea specific context30, 31.

one or more of the following approaches: microRNA target

Here, we investigate whether combining the use of miRIDIAN™

prediction algorithms , the anti-correlation of microRNA

microRNA Mimics and Hairpin Inhibitors to modulate microRNAand gene target expression within large datasets12 orlevels would allow for identification of a smaller and moreimmunoprecipitation of RISC

biologically relevant set of candidate targets. As a model1. Techniques involving

13-15

cell-based crosslinking and immunoprecipitation of thesystem we utilize miR-122, a prominent microRNA in liver cells

Ago complex have also been developed, which are analyzed

that is involved in cholesterol metabolism and Hepatitis C viralby deep sequencing and are intended to not only identify

replication32, 33. Empirically measured microRNA targets aretargets, but also to isolate specific regions of mRNA-bioinformatically probed for potential direct mRNA-microRNA

microRNA binding16, 17. While it has been demonstrated

interactions by determining enrichment of seed complementthat novel targets can be identified with such enrichment

sites1 in their 3’ UTR regions. Finally, we perform individual

techniques, they present technical challenges and are stillevaluation of proposed targets by RT-qPCR, comparing thesebeing adopted by the broader community. Each method has

empirically detected targets to those predicted by common

limitations with regard to accuracy and specificity as well as

microRNA targeting algorithms. Overall, we conclude thatthe long time commitment and technical expertise required

empirical detection using modulation of microRNA by miRIDIAN

to perform each experiment.

Mimics and Hairpin Inhibitors leads to identification of highTarget prediction algorithms search for seed complements
in target mRNA 3’ UTRs and use sequence conservation
to predict microRNA binding sites. These predictions are

confidence targets in a more efficient manner than one-by-one
follow up of targets from publicly available microRNA target
prediction algorithms.

confirmed experimentally by modulating the levels of a
microRNA using mimics and inhibitors18, 19 and measuring
effects on the predicted targets. A common approach is to

Results and conclusions

systematically examine genes from target prediction lists

Validation of miRIDIAN miR-122 mimic and hairpin

based on the biology of interest20 where only 30-60% of theinhibitor effects

predictions are confirmed experimentally. This indicates

21-25

that there are additional parameters affecting microRNAtarget regulation which limit the accuracy of current
prediction algorithms. Additional experimentally determinedtargets are also missed by predictions that do not accountIn order to optimize experimental parameters such as
dose, time point, and transfection method for use withwhole genome microarray analysis, we tested the effectsof miRIDIAN Mimics and Hairpin Inhibitors on a previously

for the biological context of each system1, 19.

reported endogenous target of miR-122, ALDOA34, 35. Huh-7

An empirical approach allows one to discover new targets

expressed at a moderate level and can be both up- andand new biology in addition to confirming predicted targets.

down-regulated in the same cell line. Message levels of

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cells were selected because the endogenous microRNA is2

Reciprocal microRNA modulation identifies gene targets in a biological context.

ALDOA are reduced by introduction of a miRIDIAN miR-122

number of genes that can be more thoroughly studied given

Mimic and are conversely increased by introduction of athe time and resource constraints of any given project. For

miR-122 Hairpin Inhibitor in a dose- and time-dependent

example, the intersection of the significant anti-correlatedmanner (Figure 1). The mimic effect is up to two-fold stronger

genes from two of the four miR-122 mimic-treated samples

than the inhibitor effect, while the inhibitor effect takes

and one of the four inhibitor-treated samples narrowed

longer to appear, becoming significant between 3 and 7

down the potential target list from approximately 750 ordays. Optimal conditions for observation of ALDOA gene

1,750 genes in either experiment alone (Table 1) to a much

modulation were 50 nM and 3 days for mimics and 20 nM

smaller list of 22 in this more stringent intersection set

and 6 days for inhibitors.

(Table 2 and Figure 3A). Due to the subtle effects of inhibitors

Discovery of additional miR-122 gene targets by wholegenome expression microarray analysis
Using the optimal transfection conditions determined above,
genome-wide expression profiling was applied to determine
regulation of miR-122 gene targets (Figure 2). To increase
confidence in the data, we performed four biologicaltransfection replicates of both miR-122 mimic and inhibitor.
The microarray ratio data was limited to transcripts that
correspond to records in RefSeq 39, as hits are required tohave an annotated 3’ UTR for subsequent analysis. For each
set of transfections, transcript sequences were selected
as significantly modulated, relative to the lipid-treatedcontrol, if they had a p-value of ≤ 0.01 (Rosetta error model)
in a specified number of arrays in that group. Since theexpression of directly-targeted transcripts are expected to be

and the longer time points required for their action, false
negatives are a great concern due to the overall fluctuationin gene expression over the course of the experiment.
Taking this into account, we relaxed the criteria for inhibitors
to require significant modulation in only one or two outof the four replicates while maintaining more stringent
reproducibility criteria for mimics. This yields a slightly larger
and yet still high-confidence target list as evidenced by
RT-qPCR assessment of target modulation.
Table 1: Transcripts significantly modulated by miR-122 pernumber of biological replicates passing threshold. The numberof transcripts down-regulated by miRIDIAN miR-122 Mimic(50 nM, 3 day time point) or up-regulated following treatment
with the miRIDIAN Hairpin Inhibitor (20 nM, 6 day time point)
all passing a p-value ≤ 0.01 versus number of biologicalreplicates passing threshold.

anti-correlated with microRNA expression, transcripts were

Replicates

Mimics down

Inhibitors up

removed if the direction of their expression change was the

1 of 41617

1757

2 of 4753

747

3 of 4401

346

4 of 4

183

65

same as microRNA modulation. Intersections of these lists
were generated at various stringencies and using increasing
numbers of replicates (Tables 1 and 2). No transcripts passed
this analysis workflow when negative control mimic andinhibitor molecules were analyzed (Figure 1).
We observed that when using only a fold-change cutoff, thesubtle effect of inhibitors was not discerned, indicating alarge number of false negatives (data not shown), while useof a p-value threshold resulted in enrichment for targets that
contain seed complement sites. Examining the intersection

Table 2: Number of transcripts both down-regulated by
mimic and up-regulated by inhibitor combined with the numberof biological replicates passing threshold. The intersectionof up-and down-regulated transcripts as a function of thestringency of replicate threshold. Number of replicatesdesignated in bold headings.

# of mimicreplicatesof mimic-and inhibitor-affected genes is important forseveral reasons. The number of genes in the target list forbe thoroughly validated using lower throughput methods(Table 1), whereas an intersection list results in a smaller
list for validation (Table 2). Transfection replicates are used
to narrow this list even further and allow one to select a

www.revvity.com# of inhibitor
replicateseither experiment alone is prohibitively large and cannot

4

3

2

1

4

2

3

3

6

3

4

6

7

12

2

8

13

15

35

1

10

15

22

60

3

Reciprocal microRNA modulation identifies gene targets in a biological context.

Seed complement occurrence analysis of potential

Comparing the members of the two array-based target lists

microRNA targets(of 22 and 60 targets, Supplemental Tables 1 and 2) to their

A bioinformatic method to assess whether differentiallyexpressed genes are likely to be direct targets of
microRNAs involves determining the occurrence of seed
complement sequences in their 3’ UTRs1. We looked at
miR-122 seed complement enrichment in the 3’ UTRsof the 22 most strongly regulated transcripts for threesets of data: treatment with miRIDIAN mimic-only alone,inhibitor-only alone, and an intersection of mimic-only
and inhibitor-only treatment (Figure 3). This analysis
demonstrates that the intersection set has the highest seed
complement enrichment (compared to the expected seed
complement enrichment of a random length-matched set ofthe same number of 3’ UTRs); there is at least one 6mer 3’
UTR seed complement in 20 of the 22 transcripts (Table 3).
Although both the mimic-only and inhibitor-only datasets
have some 3’ UTR seed complement enrichment (1.6-foldtarget ranking in prediction programs demonstrates thatmany high-confidence targets in the array-based list are
not predicted by any program. The few that are predicted
are relatively far down in their respective ranking, so thatsystematic, one-by-one testing of a list of predicted targets
does not appear to be an efficient target confirmation strategy.
In the highest confidence set of 16 targets determined by
microarray and confirmed by RT-qPCR, only four targets werepredicted by TargetScan (specifically C9 or F86 #33, GPR172A#44, LMNB2 #6, and PKM2 #40), two targets were predicted
by Pictar (C9 or F86 #51 and GPR172A #22), and four targets
were predicted by Miranda (GALNT3, LMNB2, PRTFDC1, andSH3BGRL3; Table 1). The remaining nine high confidence targets
were not predicted by any of these three programs, illustratingthat many novel microRNA targets can be discovered using thisempirical anticorrelative method.

and 1.2-fold, respectively), the magnitudes of enrichment
are smaller than for the intersection set which indicates3.75-fold enrichment (Figure 3B).

A

RT-qPCR confirmation of targets and comparison toprediction programs
RT-qPCR was used as a method to support array-detected
targets as well as to compare this discovery strategy toBa list of targets generated by commonly used prediction
programs. Two empirical gene lists were selected to testwith RT-qPCR: 22 transcripts down-regulated in two offour mimic experiments and up-regulated in one of four
inhibitor experiments, and 60 transcripts that were directly
targeted in one of four mimic experiments and up-regulated
in one of four inhibitor experiments (Table 2, Supplemental
Tables 1, 2). We compared RT-qPCR results of these arraytargets to a list of miR-122 targets generated by the overlapof widely used microRNA target prediction programs;
TargetScan, Pictar, and Miranda, which contained 24 genes(Supplemental Table 3).
Confirmation criteria for RT-qPCR experiments included an
expression change in the appropriate direction (negative formimic and positive for inhibitor) and a multiple test correctedp-value of less than or equal to 0.05. For the smaller, more
stringent set of empirical targets (22) we found 16 (73%)
transcripts confirmed response to both mimic and inhibitor,
four (18%) confirmed with mimic treatment only, two (9%)
confirmed with inhibitor treatment only, and no transcripts
were unresponsive (Figure 4A, Supplemental Table 4).
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Figure 3: Analysis of potential microRNA targets. A. The Venn
diagram of the data analysis performed in Rosetta Resolver yielding
potential microRNA target lists. The repressed group is the setof messages significantly down-regulated in any 2 of 4 biologicalreplicates of Huh-7 cells at the 50 nM mimic dose (72 hours).
The derepressed group is the set of messages significantly
up-regulated in any 1 of 4 biological replicates of Huh-7 cells at the
20 nM inhibitor dose (144 hours). B. miR-122 seed enrichment inproposed intersection workflow compared to targets identified by
mimic and inhibitor effects alone. The top 22 most down-regulated
targets (mimic-only targets) as well as the top most up-regulated
targets (inhibitor-only targets) were identified among four
microarray replicates and compared to 22 intersection workflow
targets described in A. The number of seed complements were
calculated in the target 3’ UTRs (RefSeq 39) for each seed type1.
Expected background counts were calculated by averaging seedcounts in 150 trials of randomly chosen 3’ UTR length-matchedsets (sampled with replacement, excluding actual targets). Actualand expected seed counts are listed above each bar, followed bya p-value category for significance of enrichment.
4

Reciprocal microRNA modulation identifies gene targets in a biological context.

Table 3: Top 22 miR-122 targets determined by microarray experiment and the number of seed complements counted in their 3’ UTRs.

Gene Symbol

Accession

6mer-2-7

7mer-2-8

7mer-1A-2-7

8mer-1A-2-8

1

AADACL1

NM_020792.4

2

1

1

1

2

ABR

NM_021962.2

2

1

2

1

3

C10orf118

NM_018017.2

4

3

3

2

4

C9orf86

NM_024718.3

3

1

1

1

5

FBXO21

NM_033624.2

2

0

2

0

6

FGF18

NM_003862.1

1

1

0

0

7

GALNT3

NM_004482.3

1

1

1

1

8

GBA3

NM_020973.3

0

0

0

0

9

GPR172A

NM_024531.3

1

1

1

1

10

KIAA0101

NM_014736.4

1

0

0

0

11

LCT

NM_002299.2

0

0

0

0

12

LMNB2

NM_032737.2

4

2

1

1

13

MASP1

NM_139125.2

5

2

1

1

14

MTHFD2

NM_006636.3

1

0

1

0

15

NPPB

NM_002521.2

0

0

0

0

16

PKM2

NM_182470.1

1

1

1

1

17

PLEKHB2

NM_017958.2

2

2

2

2

18

PRTFDC1

NM_020200.5

2

2

0

0

19

RAB42

NM_152304.1

1

0

1

0

20

SFT2D1

NM_145169.1

1

1

1

1

21

SH3BGRL3

NM_031286.3

1

1

0

0

22

SH3MD2

NM_020870.3

1

0

0

0

There are several strengths of this microRNA target

here is reliable, straightforward, and more efficient than aidentification workflow. In silico prediction models give no

gene-by-gene RT-qPCR query of predicted target messages.

information as to expression level of predicted targets in

We narrow down the list of microRNA targets for follow

a particular cell line or system of interest and they do not

up to a reasonable size, in this case 22 high confidence

account for biological features or context (transcription/

targets. Starting with a bioinformatic prediction tool, thedegradation rates, etc.). Using the whole genome expression

list of targets to individually test would be at least twice

approach, one can assay both predicted and novel targets

as long. While some may be concerned that this method

in an individual context. While many studies look at mimic

surveys transcript levels alone, recent studies using masseffects exclusively because they are generally of higher

spectrometry and ribosome profiling have shown excellent

magnitude, we use the intersection of modulation in bothcorrelation between message and protein changes due to

directions to address false positive and false negative

microRNA modulation21, 22, making this a good starting point

discovery. Modulation by mimics alone will produce afor follow up with a protein detection method. Reciprocal

large yet artificial target list, since the cells are overloaded

modulation can also be applied using other large scalewith a microRNA beyond its native expression level; many

expression readouts such as Stable Isotope Labeling ofof the observed effects will be biological false positives.

Amino Acids in Cell Culture (SILAC) or Next Generation

However, background variation in gene expression over time

Sequencing (NGS).

can obscure specific inhibitor effects upon the microRNAand thus produce false negatives. The method presented
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5Reciprocal microRNA modulation identifies gene targets in a biological context.

Methods

A Top 22 Array Targets

B Top 24 Prediction Targets

Cell culture, cytotoxicity analysis, quantitation of mRNA bybranched DNA assay
Huh-7 and HeLa cell lines were plated at 10,000 cells perwell without antibiotics in 96-well plates and transfected
after 24 hours using Dharmacon™ DharmaFECT™
1 (0.2 μL/well; (Cat #T-2001-02) miR-122 miRIDIAN Mimic(Cat #C-300591-05-0010) and Hairpin Inhibitor

C All 60 Array Targets(Cat #IH-300591-06-0010) were transfected at 50 and
20 nM, respectively. Five biological replicates of thesetransfections were completed over the course of 28 months.
Cells treated with miRIDIAN Mimics were harvested at threedays post-transfection; cells treated with miRIDIAN Hairpin
Inhibitors were harvested at six days post-transfection with amedium change at day three. Cellular toxicity was measured
by the resazurin metabolism assay36; all transfected
wells showed ≥ 85% viability relative to untreated wells.
ALDOA were measured using the Panomics™ Quantigene™
Branched DNA Assay.
Gene expression microarrays
Total RNA was purified using the RNeasy kit including
on-column Qiagen™ DNase™ digestion, and concentration
was measured with a spectrophotometer. All samples werediluted to a standard concentration of 100 ng/μL. Total
RNA (650 ng) from each sample was amplified and labeledusing the Low Input RNA Fluorescent Linear Amplification

Figure 4: Diagrams illustrating RT-qPCR confirmation of
microarray identified and bioinformatically predicted targets.
A. Results from RT-qPCR of the top 22 microarray targets. B.
Results from RT-qPCR of the top 24 prediction algorithm targets.
C. Results from RT-qPCR with an expanded, less stringent list of 60
microarray targets.

replicates (for example 3 out of 4) were retained. Unlessotherwise noted, a p-value threshold of 0.01 was used.
Only probes annotated with NM-prefixed accession numbers
in RefSeq 39 were considered (23,558 sequences).
microRNA seed complement enrichment analysis

kit (Agilent). Samples were hybridized to the Agilent™ WholeFor each set of targets, complementary matches to

Human™ Genome Microarray 4 × 44K. Experimental samples

miR-122 seeds (6mer, 7mer-m8, 7mer-1A, 8mer37) were

were Revvity Cy5™ labeled and reference samples (lipid-

counted using mRNA sequence data in RefSeq 39, limited

only transfectants) were Cy3 labeled. Slides were processed

to a region of the sequence (ORF or 3’ UTR). Backgroundaccording to the Agilent protocol and scanned (model

counts were obtained by counting seed complements in

G2505B) at 5 micron resolution with XDR, data extracted

corresponding length-matched sets, also limited by mRNA

(Feature Extraction 9.5.1), and further analyzed using theregion. Random length-matched sets were constructed

Rosetta Resolver™ Gene Expression Analysis software.

by ordering all possible sequences by region length, thenFor each transfection condition, sets of consistently andfor each actual target sequence (the anchor) randomly

significantly responsive probes among replicate arrays werechoosing a sequence within a defined neighborhood window

determined by applying two filters. First, probes showing

around the anchor (sampled with replacement, excludinginconsistent response directionality among replicates were

the anchor). The expected value of background counts wasremoved; for mimic transfection conditions, only probes

estimated by repeating counts of random length-matchedwith negative response (Cy5/Cy3 < 1) were retained whilesets for a number of trials. We used a neighborhood window

for inhibitor conditions, only probes with positive response

size of 600 with 150 trials. Set-wise seed enrichment was(Cy5/Cy3 > 1) were retained. Second, probes with significant

calculated as the ratio of actual counts to the expected

response (p-value threshold < 0.01) in some fraction of thevalue of background counts. Set-wise seed complement
counts were modeled as a Poisson distribution, using the
average counts over each random trial as an estimate ofwww.revvity.com

6

Reciprocal microRNA modulation identifies gene targets in a biological context.

the Poisson parameter λ. Significance of set-wise seed4.

R. Lee, R.L. Feinbaum, The C. elegans heterochronic genelin-4 encodes small RNAs with complementarity to lin-14.
Cell. 75, 843-854 (1993).

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B. Reinhart, F.J. Slack, The 21-nucleotide let-7 RNA
regulates developmental timing in Caenorhabditis
elegans. Nature. 403, 901-906 (2000).

6.

D.P. Bartel, MicroRNAs: genomics, biogenesis,
mechanism, and function. Cell 116, 281-297 (2004).

7.

R.S. Pillai, C.G. Artus, Tethering of human Ago proteins tomRNA mimics the microRNA-mediated repression ofprotein synthesis. RNA 10, 1518- 1525 (2004).

8.

J.M. Friedman, P.A. Jones, MicroRNAs: critical
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Swiss Med. Wkly. 139, 466-472 (2009).

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Y. Watanabe, M. Tomita, Computational methods for
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enrichment was calculated by evaluating the cumulative
distribution function for the actual counts.
Target prediction using publicly available tools
A list of predicted miR-122 targets was created from theoverlap of three microRNA target prediction algorithms(TargetScan Release 5.1, Pictar 2006, and Miranda Sept 2008).
The TargetScan database (targetscan.org) contained 124
conserved targets for hsa-miR-122. The Pictar database
based on Lall et al. 2006 was accessed and queried forvertebrates with the dataset of predictions for all humanmicroRNAs based on conservation in mammals (human,
chimp, mouse, rat, dog) for hsa-miR-122a resulting in a list of
169 targets – 155 excluding replicates (pictar.mdc-berlin.de/).
The Miranda database (microRNA.org, September 2008
release) contained a list of 1091 unique gene transcripts formiR-122. The overlap of all three lists is 24 transcripts.
High throughput RT-qPCR methods
RNA was isolated using Promega SV 96 Total RNA Isolation
System (Promega). Total RNA from four replicate plates waspooled and 10 µL used for reverse-transcription utilizing the
Verso cDNA Synthesis Kit with 3:1 (volume:volume) oligo dT
to random hexamer primers in a 20 µL reaction. Gene
expression analysis was performed using Thermo Scientific™
Solaris™ qPCR Gene Expression Assays (Supplemental
Table 5) and Master Mix; 4 µL cDNA(3-fold diluted) in a
16 µL reaction. Six replicate qPCR reactions were preparedmanually in 96-well plates then condensed robotically into
384-well white plates and run on an Applied Biosystems™
Prism™ 7900HT system with the following cycling program:
15 minute activation at 95 °C, 40 cycles of 15 seconds
denaturation at 95 °C and 60 seconds of annealing andextension at 60 °C. Relative expression was calculated usinga ΔΔCq method, normalizing to GAPDH expression and thento the lipid-only control samples and reported as a ratio.

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