Nielsen H. RNA: Methods and Protocols

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304 Mašek, Valášek, and Pospíšek

10. Dissolve dried RNA pellets in 350 μL of RNase-free water

and precipitate by adding 35 μL of sodium acetate and

1 mL of 96% ethanol. Incubate samples for at least 1 h

at –20

◦

C, followed by centrifugation for 20 min at

21,700×g at 4

◦

11. Wash the puriﬁed RNA by repeating the Steps 8 and 9, but

decrease the volume of ethanol to 500 μL.

12. Dissolve RNA in an appropriate volume of RNase-free

water (e.g., 25 μL) to a recommended concentration of 1–

5 μg/μL. Measure concentration of RNA spectrophoto-

metrically and check the integrity (see Section 3.5, Option

3). RNA has to be stored at –80

◦

3.5. Electrophoresis

of RNA in

TAE/Formamide

Agarose Gels

1. Dissolve an appr opriate amount of agarose to obtain 1.2–

1.5% gel in 1× TAE buffer by heating in a microwave oven.

Cool the agar ose to approximately 50

◦

C and cast the gel.

2. Place the gel tray with the solidiﬁed agarose gel into an elec-

trophoresis tank and ﬁll the tank with 1× TAE buffer.

3. Add a loading buffer to your samples (see Options 1–3

below), denature RNA at 65

◦

C for 10 min and chill on ice

for 5 min.

4. Load the samples into the wells, connect power supply and

run electrophoresis at a voltage of 5 V/cm.

Option 1: Analysis of the quality of cell lysates by RNA elec-

trophoresis.

Mix 10 μL of your lysate with 17 μL of deionized for-

mamide. Add 1 μL of ethidium bromide and 2 μLof10×

loading dye supplemented with 1% SDS.

Option 2: Rapid determination of the content of polysome

proﬁle fractions by RNA electrophoresis.

We routinely collect 0.5-mL fractions from SW41 sucrose

gradients. Rough puriﬁcation of RNA is carried out by

adding 0.5 mL of GuITC, vortexing, and precipitation with

1 mL of 96% ethanol. After two washing steps, each with 1

mL of 75% ethanol and air-drying the pellets, RNA is dis-

solved in 60 μL of formamide. For RNA electrophoresis, 30-

μL aliquots are mixed with 1 μL of ethidium bromide and 3

μL of a loading dye (or see Note 17).

Option 3: Electrophoresis of highly puriﬁed RNA isolated

from sucrose gradients for Northern blotting, RT-PCR, and

microarrays analyses.

Generally, we load 5–15 μg of RNA on a 1–1.5% agarose

gel and prepare samples to meet the following criteria: for-

mamide, 60–90% (v/v); ethidium bromide, 1–5 μg; and 1×

loading dye.

Polysome Analysis and RNA Puriﬁcation from Sucrose Gradients 305

4. Notes

1. Because of a high consumption of RNase-free water for

polysome pr oﬁle analysis, we tend to prepare it in bigger

volumes; e.g., 5 L of ddH

O in one large Pyrex bottle is

stirred on magnetic stirrer with 5 mL of diethyl pyrocar-

bonate (DEPC) overnight, then boiled in the open Pyrex

bottle for 4 h, and ﬁnally autoclaved for 40 min to com-

pletely remove r esidual DEPC. DEPC is a hazardous chem-

ical; both mixing and boiling must be performed in a fume

hood.

2. As a precaution against degradation of ribosomal com-

plexes or puriﬁed RNA it is imperative to avoid contamina-

tion by RNases. We strongly recommend to heat-sterilize

all glassware at 160

◦

C for 2 h, to use all disposable plas-

ticware (e.g., pipette tips and Eppendorf tubes) directly

from dedicated bags, to submerge all non-disposable or

non-sterile plasticware (for example, Beckman centrifuga-

tion tubes) into 1% (v/v) peroxide for 6 h followed by

thorough rinsing in DEPC-treated water. We p repare all

solutions by directly dissolving chemicals in RNase-free

water without subsequent treatment with DEPC. Gloves

should be worn throughout.

3. RNase-free glass beads are either commercially available

(for e xample from Sigma-Aldrich) or can be prepared by an

overnight wash with hydrochloric acid, followed by a thor-

ough rinsing on a Büchner funnel and heat-sterilization at

180

◦

Cfor3h.

4. We recommend preparation of gradient solutions 1, 2, and

3 a day in advance, to let them slowly cool to 4

◦

5. Solution 3 has to be equilibrated to room temperature for

several hours before use. We normally evacuate the solution

to prevent the formation of air bubbles when running the

gradient through the ISCO UV detector cell.

6. Generally, rapidly growing cultures cultivated in rich media

give very nice polysomal proﬁles. Translation is extremely

sensitive to the physiological status of cells. To analyze

effects of mutations, chemical agents or stress treatment on

translation, it is essential ﬁrst to add cycloheximide to the

cell cultures, then to rapidly harvest and chill them on ice.

All cultures to be compared should be handled in the same

way during the entire procedure. The results can vary, for

instance, by longer incubation on ice leading to slow disso-

ciation of polysomes.

306 Mašek, Valášek, and Pospíšek

7. To make whole-cell extracts from mammalian cells the fol-

lowing protocol is recommended (43). 1 × 10

HeLa cells

give enough material for one SW41 gradient. First, cells

are washed twice with PBS buffer containing cyclohex-

imide at a concentration of 100 μg/mL. After scraping

cells into 1 mL of cycloheximide supplemented PBS, pel-

let them by centrifugation at 1,000×g for 2 min at 4

◦

Lyse the cell pellet for 10 min on ice by adding 450 μLof

lysis buffer containing 20 mM HEPES, pH 7.5, 125 mM

KCl, 5 mM MgCl

, 2 mM DTT, 0.5% (v/v) NP-40, and

100 μg/mL of cycloheximide; alternatively supplemented

with 100 U/mL RNase inhibitor, 1× complete protein

inhibitor cocktail (Roche), or 1 mM PMSF. Clear the lysate

by centrifugation at 16,000×g for 10 min at 4

◦

8. Alternatively, PEB without TritonX-100 can be used for

washing the cells. This would prevent formation of bubbles

during vortexing.

9. If no breaking apparatus is available, three pulses of vigor-

ous vortexing at the maximum speed for 40 s alternated by

2-min breaks on ice disrupt yeast cells sufﬁciently.

10. For analysis of the content of polysome fractions, for

instance by microarrays, the whole procedure can be eas-

ily scaled-up by a factor of 3 in SW28 centrifugation tubes

(38 mL). In this case 30–45 OD

260

units should be

loaded on each gradient, optimally in ﬁnal volume of 1–

1.5 mL but not exceeding 2.5 mL; centrifugation is set to

28,000 rpm for 5 h at 4

◦

C; and ﬁnally a ﬂow rate of the

peristaltic pump should be adjusted to 2.5–2.8 mL/min.

11. There are several additional protocols describing how to

make sucr ose gradients. The very elegant, simple, and

reproducible freeze-thawing method was introduced by

Luthe (44). First, prepar e 17.5, 25.6, 33.8, 41.9, and

50% (w/v) sucrose gradient solutions. Then pour 2 mL

of each solution into 1 4 × 89 mm Beckman centrifuga-

tion tube, starting with the most concentrated sucrose.

Each sucrose layer is frozen at –80

◦

C for 15 min before

applying the next solution. Before use, thaw the tubes at

◦

C overnight to form continuous sucrose gradient. This

method is suitable for preparation of multiple sucrose gra-

dients in advance, because preformed gradients can be

stored for several months.

12. If areas of different peaks between multiple proﬁles are

going to be compared, it is advantageous to even the gradi-

ent volumes before loading the samples. To normalize the

polysome proﬁle data, it is also advantageous to run one

Polysome Analysis and RNA Puriﬁcation from Sucrose Gradients 307

or two blank tubes containing the same volume of PEB as

that of the samples.

13. Overnight pre-cooling of the desired rotor at 4

◦

Cis

strongly recommended as it prevents its potential damage

due to temperature heterogeneity of the rotor body.

14. Most commonly, two fractions are collected. The ﬁrst frac-

tion usually contains ribosomal subunits and monosomes

representing a non-translated pool of transcripts (note that

an inclusion of monosomes into this fraction might not

be suitable for all mRNAs). The second fraction, contain-

ing polysomes, corresponds to a pool of actively translated

mRNAs. For accurate collection of corresponding frac-

tions, we recommend to determine the dead volume of the

detector/collector system and a corresponding time delay

between particular peak detection and its elution from the

ISCO instrument. Fractionation of the polysome proﬁle

into multiple fractions of a ﬁxed volume is particularly suit-

able for Northern and Western blot analyses.

15. The Steps 5 and 6 can be substituted by a Trizol reagent

isolation procedure.

16. The LiCl precipitation procedure is applied in order to

remove heparin from the RNA sample. However, lithium

ions interfere with reverse transcriptase activity. Hence the

protocol has to be extended by a second round of ethanol

precipitation if the RNA is to be used as a substrate for this

enzyme.

17. For the simplest and the most rapid electrophoretic analysis

of RNA from polysome proﬁle fractions, it is possible to

take directly 15 μL of a collected fraction, mix it with 15

μL of formamide, 1 μL of ethidium bromide and 3 μLof

10× loading dye, and run the agarose gel. This setup is not

recommended for subsequent northern analysis.

Acknowledgment

This work was supported by Czech Science Foundation grant No.

301/07/0607, Ministry of Education, Youth and Sports of the

Czech Republic grant No. LC06066 (both to MP). LV was sup-

ported by The Wellcome Trusts grant No. 076456/Z/05/Z,

Fellowship of Jan E. Purkyne from Academy of Sciences of the

Czech Republic, and Inst. Research Concept AV0Z50200510.

308 Mašek, Valášek, and Pospíšek

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Chapter 21

Prediction of Targets for MicroRNAs

Morten Lindow

Abstract

MicroRNAs (miRNAs) are small 20–22 nt long RNAs which function as post-transcriptional regulators

altering the expression of genes either by blocking translation or by destabilizing mRNAs (for recent

reviews see, e.g., Zhang et al. (J Cell Physiol, 210:279–289) and Engels and Hutvagner (Oncogene,

25:6163–6169)). A central problem in miRNA biology is to identify the mRNAs regulated by miRNAs –

the miRNA targets. A large number (>10) of bioinformatics methods have been developed to address this

question, but unfortunately the scarcity of experimentally validated targets makes it hard to objectively

judge the performance of the methods (for an attempt see Sethupathy et al. (Nat Methods, 3:881–886).

Nevertheless, here I will give some guidelines on how to use the existing tools to ﬁnd miRNA targets.

Key words: MicroRNA, target prediction, post-transcriptional regulation, RNA-RNA interaction.

1. The Empirical

Basis for

microRNA Target

Prediction

Since miRNA target prediction is by no means perfect yet, it is

important to understand the empirical basis on which the algo-

rithms are built. For a detailed reviews of the principles of target

prediction refer to, e.g., (1–3).

1.1. Biochemical and

Structural Evidence

Target ﬁnding for plant miRNAs was an early success in miRNA-

bioinformatics: It has been shown that plant miRNA targets can

be found much above noise simply by searching for sequences

highly complementary to the miRNA in mRNA coding or

untranslated sequences (4). The validity of predictions with high

complementary to the miRNA is high and has been shown

numerous times experimentally to lead to endonucleolytic cleav-

ageofthetarget(5).

H. Nielsen (ed.), RNA, Methods in Molecular Biology 703,

311

312 Lindow

Contrary to that, animal miRNAs targets are generally

not completely complementary to their targets over the whole

sequence of the miRNA and they do not generally lead to cleav-

age of their targets. Instead the animal miRNA:target interaction

emphasizes base pairing between the 5



end of the miRNA and

the 3



UTR of the target (6). The critical importance of bases

2–7 from the miRNA 5



end, often called the seed or nucleus

region, has been established through comparative genomic as well

as experimental studies (7–9). However, detailed in vivo studies

have also demonstrated several examples of unregulated mRNAs

that have perfect seed sites and conversely regulated mRNAs that

lack perfect seed sites (10).

1.2. Statistical

Correlation from

High-Through-Put

Experiments

The statistically strongest argument for the “seed-emphasis” or

“seed-only” model comes from expression array measurements.

Lim et al. (11) has shown that 3



UTRs for mRNAs that sig-

niﬁcantly drop in expression upon transfection of cell lines with

miRNAs are highly enriched for a motif complementary to base

2–7 of the transfected miRNA (the seed region).

Similarly, in vivo, Krützfeld (12) has shown that blocking a

miRNA with an antisense molecule leads to increased concentra-

tions of a population of mRNA matching the seed sequences of

the blocked miRNA.

While the statistics are strong for the dominating role of the

seed in such microarray studies of alterations in mRNA concen-

trations, it should be kept in mind that the effect on translation is

not captured. So far only one study has attempted to address this

in a high throughput fashion. Vinther et al. (13) used proteomics

to measure the concentration of 504 highly expressed proteins

in a cell line transfected with an miRNA and found that more

complex matching models (miRanda) had a more signiﬁcant over-

lap with the experimental observation than the Seed-only model

(TargetScanS (S for seed)).

Summing up from the current data we can conclude that

while perfect seed matching is a good way to predict miRNA tar-

gets, it is neither universally sufﬁcient nor necessary.

2. Guidelines for

Accessing Target

Predictions

The questions that bioinformatics microRNA target prediction

can help answer can be grouped into four types:

1. miRNA as query: “I have a known miRNA Y – what does it

target?”

2. mRNA as query: “I have an mRNA X – which known

miRNA(s) target it?”

Prediction of Targets for MicroRNAs 313

3. Both mRNA and miRNA as query: “I have a miRNA Y that

I think regulate a speciﬁc mRNA X, where could the target

site be?”

4. I have found a new miRNA. What does it target?

Depending on the question asked, microRNA target predic-

tions can be accessed in two fundamentally different ways: For

questions of type 1 and 2 (and to some degree type 3) precom-

puted data available on the web provide easy access to predictions

of known miRNAs on known mRNAs. In most cases such pre-

computed datasets are presented in user friendly web interfaces

with hyperlinks to additional information and come with addi-

tional analysis of the conservation of the target sites, which pre-

sumably pinpoints the sites most likely to be physiological impor-

tant. Precomputed targets ar e available for many but not all model

organisms.

However, in some cases the precomputed target sites can-

not be used: Perhaps the miRNA is newly discovered or from

an organism for which precomputed targets are not available, or

the researcher has a collection of target sequences not used in

the precomputed target sets. In these cases de novo prediction of

target sites is necessary. If the number of sequence combinations

(miRNA and possible target sequence) to search is small, web ser-

vices allowing the user to upload sequence data can be used. If

on the other hand there are many sequences to search it can be

advantageous to run the algorithm locally on the user’s own com-

puter. Presently, only a few methods (miRanda and RNAhybrid)

are available as stand-alone-programs that can be downloaded and

installed. The drawbacks of running predictions locally is that

results are just displayed or saved as plain text, which can be

harder to interpret and summarize than results fr om precomputed

databases. Moreover, analysis of the phylogenetic conservation of

the sites is not performed by any of the current downloadable

programs.

Before moving on to the list of recommended prediction ser-

vices, I want to emphasize that microRNA target prediction is a

ﬁeld in rapid development, new methods and new useful websites

are continuously appearing, and this guide cannot be exhaustive.

It is advisable always to check for new developments using Google

and PubMed.

3. Precomputed

Predictions

Databases of precomputed predictions are typically compiled and

set up as supplementary websites to publications of speciﬁc target

prediction algorithms.