Disruptive mRNA folding increases translational
efficiency of catechol-O-methyltransferase variant
Douglas Tsao1,2, Svetlana A. Shabalina3, Jose ´e Gauthier2, Nikolay V. Dokholyan2,4,* and
1Department of Chemistry,2Center for Neurosensory Disorders, School of Dentistry, University of North
Carolina, Chapel Hill, NC 27599,3National Center for Biotechnology Information, National Library of Medicine,
National Institutes of Health, Bethesda, MD 20894 and4Department of Biochemistry and Biophysics, School of
Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
Received December 21, 2010; Revised March 4, 2011; Accepted March 7, 2011
Catechol-O-methyltransferase (COMT) is a major
enzyme controlling catecholamine levels that plays
a central role in cognition, affective mood and pain
perception. There are three common COMT haplo-
types in the human population reported to have
functional effects, divergent in two synonymous
and one nonsynonymous position. We demonstrate
that one of the haplotypes, carrying the non-
synonymous variation known to code for a less
stable protein, exhibits increased protein expres-
sion in vitro. This increased protein expression,
which would compensate for lower protein stability,
is solely produced by a synonymous variation
(C166T) situated within the haplotype and located in
the 50region of the RNA transcript. Based on mRNA
secondary structure predictions, we suggest that
structural destabilization near the start codon
caused by the T allele could be related to the obser-
ved increase in COMT expression. Our folding simu-
lations of the tertiary mRNA structures demonstrate
that destabilization by the T allele lowers the folding
transition barrier, thus decreasing the probability of
occupying its native state. These data suggest a
novel structural mechanism whereby functional syn-
onymous variations near the translation initiation
codon affect the translation efficiency via entropy-
driven changes in mRNA dynamics and present
another example of stable compensatory genetic
variations in the human population.
Catechol-O-methyltransferase (COMT) deactivates neuro-
transmitters and metabolizes catechol-containing structures
by methylation of a hydroxyl group (1). The implications
of COMT activity are broad and can influence factors
such as general cognitive function (2–4), addiction (5),
stress response (5) and pain sensitivity (6). Three genetic
variants of COMT have been identified in the human
population corresponding to low, average and high pain
sensitivity haplotypes (LPS, APS and HPS) (6). Higher
COMT activity corresponds to lower pain sensitivity and
vice versa. A silent mutation differentiates between low
(LPS) and high (HPS) pain-sensitive phenotypes via
reduced HPS protein levels (7), while APS is characterized
by a valine to methionine substitution at amino acid pos-
ition 108 that reduces its intrinsic activity through lowering
protein stability (1,8) (Figure 1a). These haplotypes have
also been associated with risk of fibromyalgia (9), tem-
poromandibular joint disorder (TMJD) (6), postsurgical
pain (10,11), responses to drugs (12) and development of
brain white matter (13).
The ability of highly structured regions of mRNA to
inhibit protein expression was recognized for a long time
(14–16). However, the exact mechanisms of this inhibition
and its relative contributions to regulation of translation
efficiency in live cells have only limited examples (17,18).
Thus, several in vitro studies have shown that RNA tran-
scripts containing extremely stable stems with melting
temperatures higher than 70?C can decrease protein ex-
pression at the level of ribosomal translocation (19). The
underlying factor preventing translation at highly stable
regions is thought to be the ribosome itself. It has been
shown that the ribosome contains an intrinsic helicase
activity, allowing it to read the individual bases (19).
Thus, RNA motifs that are too difficult to unwind cause
the ribosome to stall on the transcript.
Protein synthesis is highly regulated at the initiation
stage, enabling rapid, reversible and spatial control of
gene expression (20–23). Prokaryotic translation of
mRNA is regulated at both the 50and 30ends of a tran-
script during initiation (24). For eukaryotes, initiation of
*To whom correspondence should be addressed. Tel: +(919) 843 2549; Fax: +(919) 966 5339; Email: firstname.lastname@example.org
Correspondence may also be addressed to Nikolay V. Dokholyan. Tel: +(919) 843 2513; Fax: +(919) 966 2852; Email: email@example.com
Published online 12 April 2011 Nucleic Acids Research, 2011, Vol. 39, No. 14 6201–6212
? The Author(s) 2011. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
translation proceeds by the ribosome scanning from the 50
end of the transcript to the initial start codon (15,25).
Scanning through the transcript is facilitated by the eIF4
factor unwinding structured RNA regions through an
ATP-dependent process (14), and because of the scanning
mechanism ribosomes cannot bind circular mRNA tran-
scripts (26). Earlier work has demonstrated that gene ex-
pression can be repressed by increasing the stability of 50
end mRNA secondary structures (27). Recent experiments
with green fluorescent protein (GFP) constructs have also
shown that the folding free energy of the 50end of an
mRNA transcript is most correlated with protein expres-
sion, as opposed to a codon bias (28). Furthermore,
reduced stability of the mRNA at the translation-
initiation site was found to be a common feature for
most species (29).
To uncover the translation mechanisms that allelic
variants of common COMT haplotypes contribute to vari-
ation in COMT activity, we performed a set of molecular
and computational studies. We first conducted in vitro
translation studies of three haplotypes in rabbit reticulo-
cyte lysates. Unlike the in vivo expression system, we did
not observe a difference in an amount of translated
COMT protein between LPS and HPS haplotypes, sug-
gesting that rs4818-dependent stem–loop structure (7)
requires additional cellular chaperons to affect transla-
tion efficiency. However, we observed robust increase in
amount of protein of APS haplotype-coded mRNA. Here,
we show how APS haplotype-specific T allele of the
single-nucleotide polymorphism (SNP) rs4633 located at
the 50end of mRNA near the ribosomal binding site,
rather than non-synonymous met158variation, modulates
protein expression in vitro. We also conduct secondary
structural analysis and perform simulations at the 50end
of each haplotype using discrete molecular dynamics
(DMD) to determine the mechanism by which the T
allele at rs4633 alters translational efficiency (20,30,31).
Our results reveal a novel mechanism by which the dynam-
ics of mRNA structures near the initial start codon may
influence efficiency of translation initiation.
MATERIALS AND METHODS
In vitro translation
COMT cDNA coding for three haplotypes and LPS-T166
mutant were cloned into a pCMV-Sport6 vector as descr-
ibed previously (7). The mRNA templates used for trans-
lation were generated by first restriction enzyme digestion
using HindIII to create a linear plasmid. Digested
plasmids were subsequently cleaned up using a PCR puri-
fication kit (Qiagen). In vitro transcription was performed
by adding SP6 RNA polymerase (Promega) along with
rNTPs and incubated in a reaction buffer under condi-
tions provided by the manufacturer. RNA was purified
from the mixture using Trizol (Invitrogen) and subse-
quently dissolved in water. The RNA integrity was eval-
uated by running the samples on the Bioanalyzer 2100
The in vitro translation reaction was carried out using
1mg RNA template, 17.5ml rabbit reticulolysate, 0.5ml
amino acid mixture (-Met), 1ml
(1200Ci/mmol), 0.5ml RNasin and diluted to a total
reaction volume of 25ml. To denature the RNA we heat
up the samples for 3min at 70?C and immediately place on
Figure 1. Haplotypes of COMT and corresponding expression levels. (a) Organization of SNPs for the three haplotypes along COMT gene. Percent
frequency of each haplotype from a cohort of 202 healthy Caucasian females is indicated on the right (6). The LPS designed mutant used for later
experiments is also included for comparison. (b) In vitro translation in rabbit reticulocyte lysates. Both LPS and HPS share the same expression
levels, while APS exhibits higher expression levels. To test whether this is due to the rs4633 SNP near the start codon, we mutated the LPS variant
from166C to166T. The resultant LPS-T166mutant displays expression levels on par with APS.
6202 Nucleic Acids Research, 2011,Vol.39, No. 14
ice. For RNA secondary structure formation, we heat de-
nature then subsequently add 5mM MgCl2and cool at a
rate 0.1?C/s to a final temperature of 15?C. Once the RNA
template is added to the rabbit lysate mix, we incubate for
1.5h at 30?C. The reaction is stopped by adding 1?
Laemmli buffer and heating for 4min at 80?C.
We quantified the amount of protein product by sep-
arating via sodium dodecyl sulfate–polyacrylamide gel
electrophoresis (SDS–PAGE). The gel is initially placed
in fixing solution (50% methanol, 40% water, 10%
acetic acid) for 30min under gentle rotation. Afterwards,
the gel is soaked in a rinsing solution (85% water, 7%
methanol, 7% acetic acid, 1% glycerol) for 5min with
gentle rotation. The gel is then placed in a drier with
vacuum pump for 1.5h at 80?C. The gel is then placed
in a cassette with PhosphorImager screen and later quan-
tified using Storm PhosphorImaging System (Molecular
To verify that our radiolabeled protein product is
COMT, we performed immunoprecipitations on several
lysate reactions. After in vitro translation reaction, an
equal amount of NET buffer (150mM NaCl, 5mM
EDTA, 50mM Tris–HCl, pH 7.4) is added. We use
Ultralink Protein A/G agarose beads and equilibrated
them by washing with 0.5ml NET buffer twice per
100ml beads and resuspending in 100ml NET buffer. For
each lysate reaction, 5ml of primary COMT antibody was
added and incubated overnight with rotating at 4?C.
Then, 50ml of equilibrated Protein A/G agarose beads
are added and incubated at 4?C for 4h. Samples are
then centrifuged for 5min to remove the supernatant.
The supernatantis saved
analysis. The beads are then subsequently washed with
50ml NET buffer twice by rotating for 5min in 4?C. The
proteins are removed from the beads by dissolving them
with 25ml of Novex Tris–Glycine SDS solution and boiled
for 4min at 80?C. The supernatant from the boiling
reaction contains our immunoprecipitated protein and is
analyzed via SDS–PAGE.
Transfection and western blotting
The cDNA clones coding for three COMT haplotypes
were transfected into mammalian cell lines as described
previously (7). COS-1, Hek-293, HepG2 and MCF-7 cell
lines were purchased from ATCC and maintained in
media [Dulbecco’s Modified Eagle Medium (DMEM)
with 10% fetal bovine serum (FBS), 4.5g/l glucose,
L-glutamine and sodium pyruvate for COS-1, Hek-293
and HepG2; RPMI 1640 with 5% FBS and L-glutamine
for MCF-7] in accordance with manufacturer’s recom-
mendations. The western blotting was performed as des-
cribed previously (7) using anti-hCOMT antibody derived
from rabbit (Chemicon, ab5873).
Secondary structure analysis of COMT allelic variants
COMT allelic variants and randomly generated sequences
were computationally ‘folded’ and the predicted minimum
free energy of the secondary structure was calculated for
different window sizes, using our implementation of the
algorithm described by Zuker (31). Energy minimization
was performed by dynamic programming method using an
improved algorithm for evaluation of internal loops (32).
We estimated the free-energy penalty associated with
breaking (opening) of the target’s local secondary struc-
ture (target structure opening, ?G kcal/mol), considering
local disruption of secondary structure in windows with
different lengths. Free-energy changes were approximated
with nearest-neighbor free-energy parameters using the
program OligoWalk (33). Here, we consider local struc-
ture for a set of suboptimal structures (Figure 2a). Each
structure contributes to the free-energy penalty for disrup-
tion of structure in proportion to a Boltzmann weight; and
the summations over all suboptimal structures were
provided. Thus, this difference between the free energy
of each suboptimal structure and the free energy of the
corresponding suboptimal structure without base pairs in
the region of complementarity in 30-nt window length is
defined as the energy required for target structure opening
(Figure 2a). Monte Carlo simulation and analysis of
randomized sequences (21,34) was used for estimation of
the significant difference between target structure opening,
?G, of two COMT allelic variants. One-thousand unique
random sequences for each allelic variant were generated
by shuffling the first 210nt of the COMT mRNA sequence
and iteratively mutating two positions randomly along the
sequence (except position 166). Each generated sequence is
then checked to verify that the %GC and %AU content
remain identical to the original COMT gene. Once the
sequences are crosschecked with one another to ensure
there are no duplicates, we create two sets of sequences
where position 166 is occupied by either a C or U. The
free-energy penalty associated with opening of the target’s
local secondary structure (?G kcal/mol) for all random
sequences, considering local disruption of secondary struc-
ture in windows with 30-nt length was calculated. P-values
for randomizations and for difference between the C and
T alleles were determined by paired t-tests.
Three-bead RNA model and DMD
Traditional molecular dynamics simulate the motions of
particles by solving Newton’s equations of motion for a
defined system using an integration algorithm. In DMD,
simulations proceed according to the conservation laws of
energy, momentum and angular momentum and are
evaluated as a series of two-body interactions. The effi-
ciency of the engine is based on an algorithm that searches
through an event table, where velocities are only modified
as necessary. Here, we classify an event as the instance in
which two particles are within a defined interaction range
as defined by their potential. The potentials used in DMD
are discretized to accommodate the discontinuous nature
of the simulations. Further details of the DMD algorithm
can be found elsewhere (35,36).
We perform the RNA folding simulations using a
simplified three-bead model (freely available on the web
at http://ifoldrna.dokhlab.org/) (30). For each nucleotide,
each bead represents a phosphate, sugar and base.
Interactions contained in the model include standard
Watson–Crick base pairing, G-U base pairing, base
Nucleic Acids Research, 2011,Vol.39, No. 146203
stacking, phosphate–phosphate repulsion, hydrophobic
interactions and loop entropy.
Replica-exchange simulations and analysis
To ensure adequate sampling of the conformational space
of each haplotype, we utilize replica exchange where R
replicas are simulated each at a temperature Tiwhere i
represents the index of that particular replica (37). A
random walk in temperature space is performed by
exchanging the temperatures between two replicas i and
j under the probability
for ? ? 0
for ? > 0exp ??
and Eiis the total energy of the system within the ith
replica. Thus, as the macromolecule explores conform-
ational space, the energy changes accordingly. Swapping
the temperatures between replicas allows conformations
that are stuck in local energy minima to escape and
resume exploring other conformations. For our RNA
folding simulations, we performed replica-exchange simu-
lations with nine replicas (T=0.1, 0.15, 0.2, 0.225, 0.25,
0.275, 0.35, 0.4 and 0.5 e/kB) per allelic variant for 2?106
time steps. Energiesof eachRNA conformation
Figure 2. Secondary structure analysis of166C and166T-allelic variants of COMT by Mfold. (a) Free energy profile (?G) for target structure opening
in the vicinity of the rs4633 SNP and the start codon for166C and166T-allelic variants determined using OligoWalk. (b) The most probable RNA
secondary structural predictions for166C and166T-allelic variants near start codon. We zoom in toward the regional differences between the allelic
variants. The start codon is colored in green and the varying SNP is colored in red. The regions of difference are within the motifs labeled Loop I,
Stem I and Stem II. The two stems leading to the Loop I and Stem II (capped at base pairs 46–110 and 29–178, respectively) are identical as are the
rest of the nucleotides within the 210-nt window (Supplementary Data, Figures S1–S3). Both DMD and Mfold predict identical secondary structures
for the166C-allelic variant and166T-allelic variant.
6204 Nucleic Acids Research, 2011,Vol.39, No. 14
throughout the simulation are evaluated according to par-
ameters published previously (30).
Simulations can be analyzed using the weighted histo-
gram analysis method (WHAM) to determine various
function using the trajectories (38). We compute the
specific heat of folding using
where < E > is the average potential energy and < E2> is
the average squared potential energy of the system for
each specific temperature (35).
Contact maps provide a useful measure of gauging the
frequency of certain conformations over the simulation.
For our purposes, we are interested in the frequency of
base-pair formations and thus have limited our contacts to
the base beads (as opposed to including the sugar and
phosphate beads). We define a base pair between two
atoms i and j>i+3 as <6.5A˚. From the contact maps,
we can compare structures derived from simulations to
secondary structure prediction programs by comparing
contact maps of the former to dot plots of the latter.
We can also deduce the secondary structures from the
tertiary structures by calculating base pairs formed for
each trajectory according to the parameters in the force
field and determine which secondary structures are most
We have clustered conformations using the OC hier-
archical clustering package (available at http://www
.compbio.dundee.ac.uk/downloads/oc). RNA structures
derived from our simulation trajectories were clustered
according to RMSD. The lower the RMSD between two
RNA structures, the less distant (in terms of clustering)
they are from one another. The OC algorithm works by
first taking two structures that have the minimum distance
and assigning them as a cluster, and then search across
all other structures and comparing their distances among
other structures. Structures that have been clustered to-
gether are considered a single entity. Representative
tertiary structures were derived from three clustering
methods: single (where the minimum distance between
the two clusters is taken as the distance), complete
(where the maximum distance between the clusters is
taken as the distance) and means (where the average
distance between clusters is taken as the distance).
Structures that were found to be most dominant for all
three clustering methodologies were considered most
by derivinga partition
In vitro translation of COMT haplotypes
To study the precise molecular mechanism(s) whereby the
mRNA of COMT haplotypes (Figure 1) produce different
protein levels, we first employed an in vitro translation
approach that is very effective in isolating putative mech-
anisms involving differential effects at the ribosomal level.
We performed an endpoint kinetics assay using rabbit
reticulocyte lysates. The advantage of this in vitro system
is that external biological factors that regulate protein syn-
thesis are absent. We found that the APS haplotype dem-
onstrates higher protein expression levels compared to
both the LPS and HPS haplotypes while LPS and HPS
haplotypes have equivalent expression levels in vitro
There are two unique alleles an APS haplotype carries
within its transcribed region – a T allele of the SNP rs4633
at the 50end of the mRNA (+32nt downstream from the
start codon) and an A allele of the SNP rs4680 within
the second exon of the gene (Figure 1a). As we previously
showed, the structural mRNA differences within the
second exon are the most pronounced between LPS
and HPS haplotypes (7), not the APS haplotype.
Consequently, we concluded that it is unlikely that SNP
rs4680 contributes significantly to the protein levels in the
in vitro translation experiment. In contrast, SNP rs4633 is
situated at the 50end of the COMT mRNA near the start
codon; a region which showed the strongest association
between stability of mRNA folding and the rates of trans-
lation initiation expression levels of individual genes
(28,39). To test the individual contribution of SNP
rs4633 in the increase in protein levels observed for APS
haplotypes, we created a C to T mutant at position 166 for
mutating LPS at position 166 from C to T recapitulates
high expression levels characteristic for APS (Figure 1b).
Thus, the determining factor for translation efficiency of
COMT resides in the SNP rs4633 alone.
Secondary structure prediction of 50region
We then studied local RNA secondary structures contri-
buting to effect of SNP rs4633. It was shown that free-
energy stability of the 50region of an mRNA transcript is
correlated with translational efficiency (21,22,28,39–41).
Transcripts that have less stable RNA structural elements
near the 50end have higher translation rates (28), presum-
ably because tight binding to the initial start codon
becomes difficult for the ribosome initiation machinery
(20,23,25,28). To test if the T allele of rs4633 specific for
APS haplotype results in a change in free energy, we ini-
tially utilize secondary structure prediction programs to
calculate the free energy of the 50end for different respect-
We predicted mRNA local secondary structures in the
vicinity of the SNP rs4633 and the start codon by employ-
ing different algorithms for both variants (30,32,42).
Comparison of the optimal structures for both C and
T-allelic variants shows that the main structural differ-
ences due to the SNP rs4633 lies within the structural
regions of Loop I, Stem I and Stem II (Figure 2a;
Supplementary Data, Figures S1–S3). Loop I of the166T
variant (nt 119–126) is more flexible with an additional
2nt (nucleotides 126 and 127) comprising this single-
stranded region. The two nucleotides present in Loop I
Consideration of these two regions alone, the T allele-
carrying mRNA is less stable. However, both free-energy
166T variant causes Stem I to lose two Watson–
Nucleic Acids Research, 2011,Vol.39, No. 146205
calculations predict that its structure at Stem II down-
stream from the start codon is ?1kcal/mol more stable
than the wild type. This is primarily due to an additional
A-U base pair formed unique to the T allele-carrying
mRNA in Stem II between nucleotides 156 and 165 that
increases its stability from an enthalpic standpoint. From
visual inspection, it can also be seen that the additional
unpaired bases found in the hairpin loop of Stem II for
166C variant are entropically disfavorable due to the
number of single-stranded nucleotides comprising the
terminal loop compared to only four base pairs within
To verify whether this structural region is stable inde-
pendent of the surrounding sequence, we truncated the
sequence near the neighboring junctions (nucleotides 119
through 171) and refolded the structures using Mfold,
predict the same optimal local structures as the 210-nt
length transcripts (Figure 2a). Furthermore, we predicted
all suboptimal structures for both C and T allelic variants
when percent suboptimality is set to 30 (when only folding
within 30% from the minimum free energy will be
computed). Identical Stem I loops surrounding AUG
codons were found in two most stable RNA local struc-
tures with the total free energies (?G) of ?14.8 and
8.63kcal/mol in the
variant produced four structures with the energies ranging
from ?G=?16.6kcal/mol to ?G=?10.05kcal/mol, only
one of those identical at Stem I loop (Supplementary Data,
Figure S5). Thus, there are not only differences in the sec-
ondary structures between166C and166T allelic variants,
but also166T-allelic variant also produces a higher diver-
sity of suboptimal structures.
We then estimated the level of pairing and free energy of
target breaking (opening) for the
mRNA and the166T variant using full-length transcripts
and truncated transcript sequences of different lengths
starting from 210nt, where approximately a half of the
sequence length is located in the 50UTR and the second
half is in the coding region of the COMT gene. We
modeled the dynamic process of transcript folding and tar-
get breaking using 30-nt windows (33,43) (see ‘Materials
and Methods’ section). Profiles of the free energy of target
breaking for166C and166T variants (Figure 2b) show that
mRNA secondary structures in the vicinity of the start
codon (30-nt length window) are less stable and the free
energy of target breaking is significantly (P=0.0016)
higher for the
haplotype) relative to the166C variant of rs4633 (specific
for LPS and HPS haplotypes). On the other hand, Monte
Carlo simulation of the sequences in the vicinity of
the start codon showed that the differences in free
energy of mRNA secondary structure target opening
for the166T variant and the166C variant of rs4633 were
not random (P<0.05). Consistent with these findings,
our secondary structural analysis using Mfold, Afold
and RNAstructure (31,32,42) also demonstrates that the
allelic variation of rs4633 directly affects the RNA struc-
ture surrounding the start codon (Figure 2b). Thus,
our secondary structure predictions therefore provide
166C-allelic variant, respectively
166T variant of rs4633 (specific for APS
an important insight that there is an independent motif
in which there are structural differences for each
Simulations predict different folding barriers
Our folding prediction and analysis of mRNA local sec-
ondary structures revealed that the166T allele promotes
base pair disruption near the vicinity of the start codon
(Figure 2b). The lower free-energy stability of the
166T-allelic variant implies that there exists a lower energy
barrier separating the folded and unfolded states. The eu-
karyotic initiation factor eIF4a facilitates translation in
an ATP-dependent manner by unwinding RNA secondary
structure to enable ribosomal translocation (14). Thus,
if there are differences in the energetic barriers between
the folded and unfolded states for the haplotypes, then
structures with a lower energy barrier height would
undergo more efficient translation since there would be a
higher probability for the structure to exist in an unfolded
conformation (44,45). To test this hypothesis, we generate
tertiary structures of the RNA motifs by simulating the
dynamics (Figure 3a and b) of each allelic variant using
discrete molecular dynamics (DMD) (30).
166T-allelic variations of rs4633 of COMT transcripts at
the 50region between nucleotides 119 through 171 using
an RNA three-bead model (30). Since we observe multiple
transitions between the folded and unfolded states, there is
adequate sampling to enable determination of the thermo-
dynamics of the folding transition using the weighted
histogram analysis method. A comparison between the
the peak denoting the folding transition temperature is
slightly higher for the
increase in thermodynamic stability (Figure 3c).
We find that both alleles adopt a native conformation
(Figure 3a and b) that is in line with secondary structural
predictions (Figure 2a). Both the C and T alleles fold into
their respective native conformations at ?25.1kcal/mol
and lower (Figure 3d). Notably, the
higher probability in existing in this low energy state.
The most stable structures that are unique to the
allele are formed due to transient base pair formations in
the loops of Stem I and Stem II (Figure 3d). In contrast,
conformations at higher energies along its folding pathway
(Figure 3d), a consequence of its lower folding transition
temperature (Figure 3c).
Since we know that the T allele is responsible for dis-
rupting the local secondary structure near the start codon
(Figure 2b), we wanted to deduce the destabilizing effects
on the overall tertiary structure of that region. From our
simulations, we determined the flexibility of each allelic
variant’s tertiary structure by calculating the root mean
square fluctuation (RMSF) of the native ensemble (as
highlighted in Figure 3d). We find that the dynamics of
166T allele-carrying mRNA are highly entropy driven
(Figure 3b; right most structure) with RMSFs up to
11A˚ for a single nucleotide (Supplementary Data,
Table S1). In contrast, the
166T alleles of mRNA transcripts shows that
166C allele, demonstrating its
166C allele has a
166T allele-carrying mRNA is more likely to adopt
166C allele-carrying mRNA
6206 Nucleic Acids Research, 2011,Vol.39, No. 14
has an RMSF <6.5A˚
Supplementary Data, Table S1). The contact map high-
lights the attempts made by166T allele-carrying mRNA
to fold into its native structure (Figure 3e and f).
for all nucleotides (Figure 3a;
The plethora of contacts demonstrates the compet-
ing states that lead to a rugged energy landscape
(Figure 3d). Thus, examining the dynamics of
allele-carrying mRNA suggests that the free-energy
Figure 3. Tertiary structure analysis by DMD simulations indicates that the unfolding energy barrier is lower for166T allele, thus enhancing its
conformational flexibility to explore higher energy states. (a and b) Structures of variants as predicted using DMD. Leftmost structures represent the
native tertiary structure for each allelic variant. The middle structures are the secondary structural elements as predicted by DMD. The rightmost
structures are represented in sausage format where thicker backbones represent larger root mean squared fluctuations within the native ensemble (see
‘Materials and Methods’ section). Regions of the RNA are color-coded to correspond to different motifs: Loop I (blue), Stem I (green) and Stem II
(red). (a) Structures of166C-allelic variant. (B) Structures of166T-allelic variant. (c) The folding transition temperature for166T is lower than166C, as
denoted by the peak in specific heat of folding. Specific heats were determined using weighted histogram analysis method. The vertical droplines
represent the standard deviation for each plot point. (d) Histogram of free energies derived from replica-exchange simulations. The native ensembles
that correspond to the structural motifs as predicted by Mfold are denoted on the plot. (e and f) Maps illustrating frequency of contacts at a
temperature of 0.10 e/kB. (e) Frequency of contacts for166C-allelic variant. The most frequent contacts shown are representative of the native state
alone, where the first lower left diagonal line is representative of Stem I (nucleotides 5 through 32 of the simulation correspond to nucleotides 124
through 151 on the transcript). The second smaller diagonal line on the upper right of the plot represents the contacts formed from the Stem II
(nucleotides 34 through 51). (f) Frequency of contacts for166T-allelic variant. Even at the lowest simulation temperature, the166T SNP creates an
ensemble of states where many different enthalpic contacts are made throughout its folding simulation but are entropically unfavorable.
Nucleic Acids Research, 2011,Vol.39, No. 146207
barrier height between folded and unfolded states is
smaller (Figure 3c) and therefore likely to adopt higher
energy states. Consequently, the
unfolded intermediate state more frequently than the
166C allele-carrying mRNA (Figure 3d) (44–46).
166T allele exists in an
Haplotype-dependent COMT protein expression in
mammalian cell lines
To examine if the higher efficiency of protein translation
contributed by the166T allele plays a substantial role at the
cellular level, we carried out a series of transfection
studies. As reported previously, in PC12 rat pheochrom-
acytoma cells, COMT protein expression levels is reduced
25-fold in the HPS haplotype; however, LPS and APS
haplotypes display comparable protein levels (7). To de-
termine whether this effect is a general feature of COMT
protein expression in mammalian cells or specific to the
PC12 cell line, we transfected expression vectors with the
three COMT haplotypes in a number of different cell line
with divergent tissue origin: COS-1 monkey kidney cells,
HEK-293 human embryonic kidney cells, HepG2 human
liver cells and MCF-7 human breast cancer cells. We find
that all transfected cell types consistently exhibited the
same qualitative trend in protein expression (Figure 4),
where LPS exhibits higher protein expression than HPS,
and HPS shows the most reduced protein expression.
Notably, APS showed comparable protein levels to LPS
in COS-1 and HepG2 cell lines but not in HEK-293 and
MCF-7 where APS showed the highest protein level
(Figure 4). Thus, it appears that an increase in translation
efficiency of the APS haplotype does contribute to cellular
protein levels and this contribution is tissue specific.
166T mutation contributes to higher translation rates
Our in vitro translation data demonstrate that the APS
haplotype of COMT has a higher rate of translation com-
pared to LPS and HPS, while LPS and HPS haplotypes
show similar level of expression (Figure 1b). Furthermore,
our results suggested that the upstream 50-end structure in
allelic-dependent manner largely controls the in vitro
translation rate. The APS-specific
was hypothesized to drive the difference in protein trans-
lation efficiency. The
strong candidate for the translational ‘switch’ because of
its unique location near the start codon, the RNA area
known to contribute the most to the RNA structure-
dependent translation initiation rate (28). To test this hy-
pothesis, we created a LPS mutant carrying the166T allele
of rs4633 (LPS-T166) specific for APS haplotype. This
mutant has similar protein expression levels in comparison
to APS (Figure 1b), suggesting that only the SNP rs4633 is
necessary for determining expression rates in vitro.
Therefore, in the case of translation in vitro, we can rule
out the possibility that the other downstream SNPs of
three major COMT haplotypes play a role in determining
efficiency of in vitro translation.
166T allele of rs4633
166T allele of rs4633 was also a
Computational analysis reveals structural differences
To investigate the structural mechanism in which SNP
rs4633 affects in vitro translation of COMT RNA, we
employed an array of computational approaches. We ini-
tially utilize Mfold to generate secondary structures of
each haplotype. By examining the folds of COMT
mRNA at various sequence lengths, we observe which
type of structures predominate at the 50end. We find
that much of the structure toward the 50end is identical
for all haplotypes with the exception near the vicinity of
the SNP rs4633 and start codon (Figure 2b). The obser-
vation that a single SNP affects the 50end structure in the
vicinity of the start codon supports the view that this
region might regulate COMT translation.
LPS and HPS haplotypes exhibit equivalent in vitro
protein expression levels, which can be attributed to iden-
tical secondary structures near the start codon due to
sharing the166C allele. However, the166T allele structure
(carried by the APS haplotype) has some unique structural
rearrangements in comparison with the166C-allelic variant
that influences structure stability in the vicinity of the start
codon (Figure 2a). Furthermore, consistent with these ob-
servations, thermodynamic analysis from OligoWalk
suggests that the166T allele structure is less stable near
the vicinity of the start codon compared to the
allele (Figure 2b). Local secondary structures are extreme-
ly conserved in the vicinity of the start codon for the166C
allelic variant, exemplified in the optimal and suboptimal
structures predicted by Mfold where Stem I is identical for
both (Supplementary Data, Figure S4). Contrarily, local
secondary structures in the vicinity of the start codons
differ between optimal and suboptimal structures in
T-allele and display more structural diversity.
It is suggestive that the region in the vicinity of the start
codon may potentially play a more significant role with
regard to translational efficiency (28,40). Since we know
that the alternative166T allele yields a unique structure in
the vicinity of the start codon, it is possible to fold this
sequence using DMD simulations and seek whether
tertiary interactions may play an integral role. We find
Figure 4. Protein expression levels of COMT constructs that code for
three COMT haplotypes in the range of mammalian cell lines. The
constructs were transiently transfected into indicated cell lines and its
protein expression were analyzed via western blotting. Top band:
b-actin. Lower band: COMT. Cell lines: COS-1 monkey kidney cells,
HEK-293 human embryonic kidney cells, HepG2 human liver cells, and
MCF-7 human breast cells. Among these cell lines, APS protein expres-
sion is equivalent or higher than LPS, while HPS exhibits significantly
lower levels compared to the two haplotypes.
6208Nucleic Acids Research, 2011,Vol.39, No. 14
that both166C allele and166T allele predicted structures
derived from DMD simulations are identical to second-
ary structures predicted by Mfold and RNAstructure
(Figures 2a and 3a and b). However, the native
ensemble of166T allele is less probable in comparison to
the166C allele. The conformational entropy for the166T
allele is higher than the166C allele, resulting in high flexi-
bility and exploration of higher energy states (Figure 3d).
Consequently, this may enable facilitated initiation of
translation as displayed by the higher expression levels
by this haplotype. Our thermodynamic analysis reveals
that this is a strong possibility given that the folding tran-
sition temperature is lower for
166T allele’s structure
Codon bias and mRNA structure as factors in
Two main mechanisms are thought to be important
factors in determining the efficiency of mRNA translation:
(i) the ease of unwinding the mRNA structure at the 50end
and (ii) codon usage. The extent to which one mechanism
plays a dominating factor over the other is dependent
upon the individual genes in question (27,28,47,48). Our
simulations in this work have focused on the structural
contributions that can lead to increased protein expression
from the166T allele. Here, we consider the possibility of
codon bias and its effect on translational regulation of
COMT. Both the
code for histidine through the synonymous codons CAC
and CAT, respectively. It has been reported that the CAC
allele is nearly twice as efficient compared to the CAT
allele (49). Within this context, it is unlikely that the
CAT codon is the contributing factor for increased
protein expression. However, it has also been reported
that use of low-efficiency codons near the initiation site
can aid the efficiency of translation as this scenario
prevents ribosomal traffic jams near the initiation site
(50). It is uncertain to what extent the CAT codon in-
creases efficiency of translation given these two competing
scenarios. These explanations are in agreement with pre-
viously published data on the role of mRNA structure and
codon usage in the vicinity of the start codon for transla-
tion efficiency (41). Nevertheless, reduced ribosomal
traffic may also play a role in the enhancement of166T
allele protein expression.
166C allele and
166T allele at rs4633
Role of mRNA stability and dynamics in translation
Previous reports have only suggested a correlation of free-
energy stability and translational efficiency (15,28). Since
it is the stability of the 50end that is the most determining
factor for translation efficiency, it is presumed that the
limiting factors are unwinding by the eIF4a factor and
ribosomal binding to the start codon. The relationship
between free energy and translation initiation is not im-
mediately apparent. The stability of a particular region
alone would not necessarily render a recognition site in-
accessible. Thus, we proposed that the folding pathway
might play an essential role in regulating initiation
factor access. Specifically, if the energetic barriers
between conformational states of an RNA are low, then
the RNA can easily explore conformations that are
outside its native ensemble (46). These conformations
can have a reduced number of structural elements and
therefore this flexibility can facilitate sequence recognition
by translational machinery.
The resultspresented here
Substitution of166C to166T at rs4633 in COMT mRNA
increases the number of favorable isoenergetic conform-
ational states for its mRNA transcript. The dynamics
that the native conformation becomes less populated as
the RNA explores conformations at higher energies.
Consequently, there are large fluctuations in the positions
of each nucleotide, thereby enhancing its flexibility.
Further exploration of this model by studying the
dynamics for a wide variety of RNA structures would be
required to prove its fundamental significance.
166T-carrying allele become entropy driven such
Implications for cellular expression levels
Our current results in several transfected mammalian cell
lines re-enforce the conclusion from our previous studies
(7) that the in vivo expression of COMT is strongly
dictated by RNA structures formed by SNPs rs4818 and
rs4680 (Figure 4) yielding the lowest expression levels for
haplotype HPS. However, the in vitro translation rate
seems to be independent from rs4818 and rs4680 inter-
actions and driven solely by local structures near the
start codon that is dependent on allelic variants of
rs4633 (Figure 1b). Furthermore, in two out of four cell
lines we also observed that the APS haplotype produced
the highest protein level, consistent with in vitro transla-
tion results that are rs4633-dependent. Thus, we observed
differential input of three SNPs into two distinct structural
mechanisms, apparently contributing to translation regu-
lation at different levels.
These results are in line with the observed mRNA
structure-dependent differences in efficiency and rate of
translation in vivo and in vitro reported previously (18).
Since the rate of translation is much slower than the rate
of RNA folding, it is thought that RNA begins to fold
locally during the translation process and that the final
structure oftentimes is the metastable product of local
folding. Thus, the upstream structures dominate folding
outcomes in vitro, suggesting that folding occurs sequen-
tially. However, when studied in vivo, upstream and down-
stream structures are presented equally and folding
outcomes reflect the relative stability of alternative struc-
tures, probably facilitated by cellular chaperone proteins
associated with nascent RNAs (18).
The variation in COMT expression levels across differ-
ent systems could potentially be explained by the experi-
structures in cells due to rapid exchanging of states
facilitated by proteins bound to nascent RNAs (18) in
contrast to in vitro translation conditions. Because the
rate of RNA folding is on the scale of microseconds and
thus much faster than the rate of transcription, there is a
preference for local folds as opposed to long-ranged base
pairs. This preference may be diminished in cells by
Nucleic Acids Research, 2011,Vol.39, No. 146209
specific RNA-binding proteins that allow exchange of sec-
ondary structures through branch migration (18). Our
results suggest that the contribution of these cellular
proteins is tissue specific, such that in some cell lines the
overall cellular protein expression is almost exclusively
controlled by these factors, while other cell lines recapitu-
late the results found in vitro using rabbit reticulocyte
lysates (Figure 4).
It is also plausible that in some cell lines, other factors
regulating translation are more strongly contributing to
protein expression. The abundance of transfer RNAs
(tRNAs) with synonymous codons are known to vary in
the cell up to 10-fold across different human tissues (51).
The availability of tRNAs during translation could also
contribute to the relative speed at which the protein is
Alternatively, it is possible that structural modulation
of RNA itself is not the sole explanation for differences in
protein expression, and there may be additional mechan-
isms contributing to translational regulation. These down-
stream structural motifs may potentially be recognized by
external biological factors and subject to further regula-
tion. For example, the Fragile X Mental Retardation
Protein (FMRP), an established regulator of translation,
is known to bind to specific structural RNA motifs (52,53)
and can downregulate their expression by association
with the RNA-induced silencing complex (54,55). This
cascade of structural and cellular mechanisms at the
mRNA level is likely to be defined by other specific
cellular components and thus contribute to differences in
COMT protein expression levels in a tissue-dependent
From a broader perspective, since the APS haplotype
carries a nonsynonymous met158variation known to
create a thermolabile mutant and thus display lower en-
zymatic activity (1,8) in comparison with wild-type val158,
it is remarkable that its protein expression level can be
significantly higher than wild-type LPS haplotype. Thus,
our results represent a potential compensatory mechanism
of APS haplotypes to overcome lower enzymatic activity
via overexpression in specific cell lines.
The results presented here demonstrate a new molecular
mechanism, thereby synonymous substitution of a known
functional human COMT haplotype contributes to trans-
lation efficiency, thus representing an exciting example of
evolutionary selection of an RNA-structure destabilizing
allele to compensate for a destabilizing amino acid substi-
tution within a mutant protein structure. Importantly, this
change did not only affect the stability of RNA structure
but rather its dynamics, suggesting that increased con-
formational flexibility enhances translational efficiency.
This mechanism by which the destabilizing allele facilitates
translation provides a new perspective in functional
genomics and requires further investigation to determine
the extent of its fundamental applicability for common
genetic variations in human population.
Supplementary Data are available at NAR Online.
We would like to thank Dr Sergei Romanov for his aid in
developing the in vitro translation assay.
(R01GM080742 to N.V.D.); American Recovery and
Reinvestment Act supplements
GM066940-06S1 to N.V.D.);
Dental and Craniofacial Research and National Institute
of Neurological Disorders and Stroke grants (RO1-
DE16558, UO1-DE017018, PO1 NS045685 to L.D.);
and Intramural Research Programs of National Center
Medicine (to S.A.S.). Funding for open access charge:
National Institutes of Dental and Craniofacial Research
and National Institute of Neurological Disorders and
Stroke grants (5-U01-DE017018-04-06
NS045685-06A1 to L.D.).
US National Institutesof Healthgrant
aNational Library of
Conflict of interest statement. None declared.
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