REVIEWS

Synonymous but not the same:the causes and consequences ofcodon bias
Joshua B. Plotkin*and Grzegorz Kudla‡

Abstract | Despite their name, synonymous mutations have significant consequences
for cellular processes in all taxa. As a result, an understanding of codon bias is central tofields as diverse as molecular evolution and biotechnology. Although recent advances insequencing and synthetic biology have helped to resolve longstanding questions aboutcodon bias, they have also uncovered striking patterns that suggest new hypotheses
about protein synthesis. Ongoing work to quantify the dynamics of initiation andelongation is as important for understanding natural synonymous variation as it is fordesigning transgenes in applied contexts.

*Department of Biology
and Program in Applied
Mathematics andComputational Science,
University of Pennsylvania,
433 South University Avenue,
Philadelphia, Pennsylvania
19104, USA.
‡
Wellcome Trust Centre for
Cell Biology, University of
Edinburgh, Michael Swann
Building, Kings Buildings,
Mayfield Road, Edinburgh
EH9 3JR, UK.
Correspondence to J.B.P.
e-mail:jplotkin@sas.upenn.edu
doi:10.1038/nrg2899
Published online
23 November 2010

When the inherent redundancy of the genetic code
was discovered, scientists were rightly puzzled by therole of synonymous mutations1. The central dogma of
molecular biology suggests that synonymous mutations — those that do not alter the encoded aminoacid — will have no effect on the resulting protein
sequence and, therefore, no effect on cellular function, organismal fitness or evolution. Nonetheless, inmost sequenced genomes, synonymous codons are notused in equal frequencies. This phenomenon, termed
codon-usage bias (FIG. 1), is now recognized as crucial in shaping gene expression and cellular function
through its effects on diverse processes, ranging fromRNA processing to protein translation and protein
folding. Naturally occurring codon biases are pervasive, and they can be extremely strong. Some species
such as Thermus thermophilus avoid certain codons
almost entirely. Synonymous mutations are important in applied settings as well — the use of particularcodons can increase the expression of a transgene bymore than 1,000-fold2.
We already enjoy a broad array of often conflicting
hypotheses for the mechanisms that induce codonusage biases in nature, and for their effects on protein
synthesis and cellular fitness. Until recently, we havebeen unable to systematically interrogate these hypotheses through large-scale experimentation. As a result,despite decades of interest and substantial progress inunderstanding codon-usage biases, there is an overabundance of plausible explanatory models whose relative, quantitative contributions are seldom compared.

Advances in synthetic biology, mass spectrometry
and sequencing now provide tools for systematically elucidating the molecular and cellular consequences of synonymous nucleotide variation. Such studies have refined
our understanding of the relative roles of initiation, elongation, degradation and misfolding in determining protein expression levels of individual genes and the overall
fitness of a cell. This information, in turn, is helping
researchers to distinguish among the forces that shapenaturally occurring patterns of codon usage. Researchers
can also leverage high-throughput studies in applied
settings that require controlled, heterologous gene
expression, for example, to improve design principles
for vaccine development and gene therapy.
Here we review the causes, consequences, and practical use of codon-usage biases. Because we alreadybenefit from several outstanding reviews on naturally
occurring codon biases3–7, we focus here on those classical hypotheses that remain unresolved and the recent
developments arising from high-throughput studies.
We begin by summarizing the empirical patterns ofcodon usage that are observed across species, acrossgenomes and across individual genes. We describe
the diverse array of mechanistic hypotheses for thecauses of such variation and the sequence signaturesthat support them. Against this backdrop of hypotheses and sequence analysis, we describe experimental
work that relates codon usage to endogenous gene
expression and cellular fitness. From this, we turn toexperimental studies on heterologous gene expression and their implications both for understanding

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Figure 1 | Codon bias within and between genomes. The relative synonymous codon usage (RSCU)127 is plotted
0CVWTG4GXKGYU^)GPGVKEU
for 50 randomly selected genes from each of nine species. RSCU ranges from 0 (when the codon
is absent), through 1(when there is no bias) to 6 (when a single codon is used in a six-codon family). Methionine, tryptophan and stopcodons are omitted. Genes are in rows and codons are in columns, with C- and G-ending codons on the left side ofeach panel. Note the extensive heterogeneity of codon usage among human genes. Other measures of a gene’s
codon bias include the codon adaptation index (CAI; the similarity of codon usage to a reference set of highly
expressed genes)35, the frequency of ‘optimal’ codons (FOP)28 and the tRNA adaptation index (tAI; the similarity ofcodon usage to the relative copy numbers of tRNA genes)128.

natural synonymous variation and for engineering new
constructs in applied settings.

Mechanistic hypotheses
Significant deviations from uniform codon choice havebeen observed in species from all taxa, including bacteria, archaea, yeast, fruitflies, worms and mammals. The
overall codon usage in a genome can differ dramatically between species, although seldom between closely
related species6.
Mutational versus selective hypotheses. explanations forpatterns of codon usage, within or between species, fall
into two distinct categories that are associated with two
independent forces in molecular evolution: mutationand natural selection3–5.

A mutational explanation posits that codon bias arises
from the properties of underlying mutational processes— for example, biases in nucleotides that are producedby point mutations8, contextual biases in the point
mutation rates or biases in repair. mutational explanations are neutral because they do not require any fitness
advantage or detriment to be associated with alternativesynonymous codons. mutational mechanisms are typically invoked to explain interspecific variation in codon
usage, especially among unicellular organisms.
explanations involving natural selection posit thatsynonymous mutations somehow influence the fitnessof an organism, and they can therefore be promoted
or repressed throughout evolution. Selective mechanisms are typically invoked to explain variation incodon usage across a genome or across a gene, although

NATURe ReVieWS | GenetiCs

VolUme 12 | jANUARy 2011 | 33
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REVIEWSC'NQPICVKQPNKOKVGF(TGGTKDQUQOGU

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E+PKVKCVKQPNKOKVGFJKIJEQFQPCFCRVCVKQPF+PKVKCVKQPNKOKVGF XGT[UNQY JKIJEQFQPCFCRVCVKQP

Figure 2 | Relationships between initiation rate, elongation rate, ribosome
density and the rate of protein synthesis for endogenous genes. The steady-state
0CVWTG4GXKGYU^)GPGVKEU
rate of protein synthesis and density of ribosomes bound on an mRNA both dependon the rates of initiation and elongation. When elongation is the rate-limiting step ina gene’s translation (case A), the message will be covered as densely as possible byribosomes and faster elongation will tend to increase the rate of protein synthesis.
However, most endogenous genes are believed to be initiation limited (cases B, C
and D), so that their transcripts are not completely covered by ribosomes. This isevidenced by extensive variability in ribosome densities across endogenous mRNAs67.
For two initiation-limited genes with the same initiation rate, the mRNA with fasterelongation (afforded by, say, higher codon adaptation to tRNA pools) will have a lower
density of translating ribosomes (C versus B) but no greater rate of termination. Thus,
when initiation is limiting, high codon adaptation should not be expected to increase
the amount of protein that is produced per mRNA molecule (protein amounts
are the same in B and C). A lower density of ribosomes can also occur when two
initiation-limited genes have the same elongation rate, but one has a slower initiation
rate (D versus C). In this case, the amount of protein that is produced will be lower forthe mRNA that has the slower initiation rate (D). The extent to which variation inribosome densities67 arises from variation in initiation versus elongation rates remains
to be determined. In all cases shown here, as is true for most endogenous genes, thegene’s mRNA does not account for a substantial proportion of total cellular mRNA, sothat the rates of initiation and elongation do not substantially alter the pool of freeribosomes (compare with FIG. 4).

some interspecific variation is also attributable to suchmechanisms (see below).
Selective and neutral explanations for codon usage are
not mutually exclusive, and both types of mechanisms
surely have a role in patterning synonymous variation
within and between genomes3,5,9. Below we discuss thepatterns of codon usage that have been documented atvarious levels of biological organization in light of theirmutational or selective causes.

Iso-accepting tRNAs
A subset of tRNAs that carry
the same amino acid.

Patterns of codon usage
Patterns across species. The strongest single determinantof codon-usage variation across species is genomic GC
content. in fact, differences in codon usage between bacterial species can be accurately predicted from the nucleotide content in their non-coding regions3,10. Genomic

GC content is itself typically determined by mutational
processes that act across the whole genome. As a result,most interspecific variation in codon usage is attributed
to mutational mechanisms3,10, although the molecular causes of mutation biases are largely unknown10.
Contrary to early expectations, the GC content of bacterial genomes or protein-coding genes is not correlatedwith optimal growth temperature (although, interestingly,
structural RNAs show such a correlation)11.
in those species for which the point mutation ratedepends strongly on the sequence context of a nucleotide— for example, in mammals, which experience hypermutable CpG dinucleotides — the mutational model
predicts a strong context dependence of codon usage,
which has indeed been observed12. Thus, at the genomic
scale, neutral processes that do not discriminate amongsynonymous mutations remain plausible for explaining
interspecific variation in codon usage among higher
eukaryotes and they are well accepted as the primary
determinants of interspecific variation in most othertaxa (but see ReF. 13).
Aside from mutation biases, adaptation of codon
usage to cellular tRNA abundances can also influence
synonymous sequence variation across species (seebelow), as codon usage and tRNA regulation can coevolve. Finally, some neutral processes that are responsible for codon bias across taxa are not mutational per se.
even in the absence of selection at synonymous sites,
selection at non-synonymous sites can induce differences in nucleotide composition between coding andnon-coding regions5,14–16.
Patterns across a genome. There is often systematic
variation in codon usage among the genes in a genome,
usually attributed to selection. in organisms, including
Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis
elegans, Arabidopsis thaliana, Drosophila melanogasterand possibly also mammals (see below), there is a significant positive correlation between a gene’s expression
level and the degree of its codon bias, and a negative
correlation between expression level and the rate of synonymous substitutions between divergent species9,17,18 —features that are difficult to explain through mutation
alone. Although mutational effects could possibly covary with expression levels, because transcription canbe mutagenic19,20, this effect is unlikely to account for thecorrelations between codon usage and expression levels
that are observed in numerous species5,19,21.
The classic explanation for systematic variation acrossa genome is selectionist: codon bias is more extreme inhighly expressed genes to match a skew in iso-accepting
tRNAs and, thereby, provide a fitness advantage throughincreased translation efficiency or accuracy of protein
synthesis9,17,22–27. There is strong evidence for this hypothesis in several species, mostly in the form of broad correspondences between the ‘preferred codons’ that areused in highly expressed genes and measures of relative
tRNA abundances28–32. As a result, translational selection
remains the dominant explanation for systematic variation in codon usage among genes, despite the fact that
supporting evidence is sometimes incomplete: direct34 | jANUARy 2011 | VolUme 12

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REVIEWS
Box 1 | Selection for accuracy or efficiency?
The nature of translational selection remains a topic of active debate. Codons thatare adapted to tRNA pools might be preferentially used in highly expressed genes,
because such genes experience greater pressure for translational efficiency28,29,110,
accuracy22–27 or both. Efficient elongation of a transcript might increase its protein
yield2,36,37,92, or it may provide a global benefit to the cell by increasing the numberof ribosomes that are available to translate other messages, even if it does notincrease the yield of the transcript itself3,7,9,55. Accurate elongation, by contrast,
benefits the cell by reducing the costs of useless mistranslation products or the
toxicity of harmful mistranslation products111. These two models can makedifferent predictions for the fitness costs of maladaptive codons, as a function oftranscript level.
There are several lines of sequence-based evidence that discriminate between
the efficiency and accuracy hypotheses. Some of the most compelling evidence infavour of accuracy was introduced by Akashi22,23,113, who found a greater tendency
towards tRNA-adapted codons at residues that are strongly conserved acrossdivergent Drosophila species; this suggests that sites that are under strong negative
selection at the amino acid level also show stronger codon adaptation, presumably
to reduce mistranslation. The same finding was later extended to Caenorhabditis
elegans113 and unicellular organisms25,27. A separate line of evidence arises from thecorrelation between codon adaptation and gene length in Escherichia coli24,
reflecting a greater energetic cost of missense and nonsense translation errors ina long protein, especially if they occur near the 3′ end. However, the relationshipwith gene length does not hold in C. elegans, Drosophila melanogaster or
Arabidopsis thaliana21. Other evidence for the accuracy hypothesis comes fromsimulations of sequence evolution, protein translation and protein folding26.
There is also convincing evidence in favour of translational efficiency, especially inprokaryotes. The most compelling observation is a broad correlation between theminimum generation time of a bacterial species and the strength of selection it
experiences for codon adaptation in highly expressed genes34,114. We would expect
to see this correlation if preferred codons increase the elongation rate, which isbeneficial for rapid growth, but it is unclear why we would observe this correlation if
preferred codons increase only the accuracy of elongation. Furthermore, Zhang andothers have recently shown that codon usage in highly expressed yeast genes is
consistent with selection to avoid unnecessary ribosomal sequestration of messages(W. Qian, J. Yang, N. Pearson, C. Maclean and J. Zhang, personal communication).
The accuracy and efficiency hypotheses are not mutually exclusive, in general.
However, in a recent computational study, Shah and Gilchrist115 demonstrated thatcodons corresponding to more abundant tRNAs are not always expected to produce
lower missense error rates, as has been commonly assumed. Moreover, they foundthat, for some amino acids, pressure for elongation speed would result in a different
codon choice than would pressure for elongation accuracy. Whether patterns ofcodon bias in evolutionarily conserved residues22 occurs only for those amino acids
for which efficiency and accuracy selection have the same predicted effect oncodon choice remains unresolved and might help to distinguish between these twomodes of selection.

Negative selection
A form of natural selectionthat suppresses alternative
genetic variants in favour of
the wild type.

Horizontal gene transfer
The transfer of genetic material
from one species into another.

Isochore
A large fragment of achromosome that ischaracterized byhomogeneous GC content.

measurements of tRNA abundances are rare in higher
eukaryotes; the correspondence of tRNA abundancewith tRNA copy number 5 is weak in D. melanogaster andhumans33; and 30% of bacterial species show no evidenceof translational selection34.
There are two possible directions of causality relating
an endogenous gene’s expression level and the degreeof its codon adaptation35 to tRNA abundances. in one
view 2,36,37, high codon adaptation induces strong protein
expression, because rapid and/or accurate elongation
increases a given protein’s rate of synthesis; in the otherview, strong expression selects for high codon adaptation to avoid costs that scale with a gene’s expression
level. in the biotechnology literature, the former interpretation is de rigueur, whereas in the literature onmolecular evolution, the latter interpretation prevails3–5.

The idea that high codon adaptation induces high protein levels per mRNA molecule does not square wellwith the notion that initiation is generally rate limitingfor endogenous protein production7,9,38,39 (although it
may apply to heterologous genes (see below)). Wheninitiation is limiting, the elongation rate should not
influence the amount of protein that is produced froma given message7,9 (FIG. 2). moreover, from an evolutionary perspective, if high protein levels are desirable, itwould seem easier to tune a promoter for increasedtranscription than to select on hundreds of individualsynonymous mutations, each of which has only a marginal effect on the overall amount of protein synthesis.
Conversely, the use of poorly adapted codons to slow thetranslation of genes expressed at low levels36 would seem
wasteful compared to simply reducing transcription
or slowing initiation.
Although evolutionary studies generally agree thathigh expression selects for high codon adaptation in
endogenous genes (as opposed to the converse), the precise nature of fitness gains associated with translationallyadapted codons remains a topic of active debate (BOX 1).
Furthermore, even though translational efficiency isenergetically beneficial to the cell, efficient translation
generally increases the amount of cell-to-cell variationin expression levels40 and this noise is typically deleterious41. Although translational selection has received
the most attention, systematic variation in codon usage
across a genome can also be caused by neutral processes
in certain species; these processes include horizontal genetransfer42, different nucleotide bias in leading and lagging
strands of replication in bacteria43 and isochore structure
in mammals (BOX 2).
Patterns across a gene. Codon usage can vary dramatically even within a single gene. Synonymous mutationsat specific sites may experience selection because they
disrupt motifs that are recognized by transcriptionalor by post-transcriptional regulatory mechanisms, forexample, microRNAs. Sites that require ribosomal pausing for proper co-translational protein folding or ubiquitin modification44 may experience selection for poorly
adapted codons45 or strong mRNA folding 46. Codon
choice that promotes proper nucleosome positioning isselectively advantageous in eukaryotes, especially in 5′
regions47. And, finally, in mammals, synonymous mutations near an intron–exon boundary can create spurious splice sites or disrupt splicing control elements4,48,
causing disease4. This phenomenon helps to explain
the reduced rate of synonymous substitutions and SNP
density near splicing control elements49,50. Selection forproper splicing also extends to D. melanogaster, andsequence variation suggests that it is probably an evenstronger force than translational selection in shaping
codon usage near intron–exon boundaries51.
Although important, the mechanisms of intrageniccodon-usage variation described above are typically
restricted to specific taxa or special classes of sites.
Recent studies have argued for three mechanisms that
produce systematic variation in codon usage across thesites in a gene in a diverse range of species.

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REVIEWS
Ribosomal pausing
A temporary arrest of
the ribosome duringtranslation elongation.

Effective population size
The number of individuals ina population that produce
viable offspring.

Biased gene conversion
A recombination event inwhich one variant of genomic
sequence is preferentially‘copied and pasted’ onto
another one.

Fourfold degenerate sites
Positions within the coding
sequence of a gene at which all
four nucleotides encode thesame amino acid.

Shotgun proteomics
Methods of quantifying
protein levels in a complex
sample, typically usingmass spectrometry.

one of these mechanisms is selection against strong
5′ mRNA structure to facilitate translation initiation.
mRNA structure near the 5′ end of a coding region is
generally disadvantageous9 as it can inhibit ribosomal
initiation52,53 (FIG. 3a). eyre-Walker and Bulmer proposed
selection against mRNA structure to explain a trend
towards reduced codon adaptation in the 5′ region of
E. coli genes and a corresponding reduced rate of synonymous substitutions across divergent species54. more
recently, following similar observations in E. coli 55,
Gu et al. demonstrated a broad trend in all sequenced
prokaryotes and eukaryotes towards reduced mRNAstability near the translation initiation sites of genes,
especially for GC-rich genes56. This study relied oncomputational predictions of mRNA structure in shortwindows; combined with large-scale experimental studies (see below), this work suggests a systematic role forselection on mRNA structure in shaping codon usage inthe first 30–60 nucleotides of genes.
Tuller et al.57 recently described a second, systematic
trend in the pattern of intragenic codon usage: a ‘ramp’ of
poorly adapted codons in the first 90–150 nucleotides
of genes, which had earlier been observed in bacteria,
yeast and fruitflies58,59. This pattern has been preserved
across divergent species even when tRNA pools (estimated from gene copy numbers) have changed57. A rampof poorly adapted codons presumably slows elongation at
the start of a gene, which may provide several physiological benefits. Slow 5′ elongation is predicted to reduce thefrequency of ribosomal traffic jams towards the 3′ end57,60,
thus reducing the cost of wasted ribosomes and of spontaneous or collision-induced abortions. Alternatively, aramp of slow elongation may facilitate recruitment ofBox 2 | Mammals are different
Intragenomic patterns of codon usage in mammals are markedly different from those inother taxa. Selective mechanisms were initially ruled out for humans on the basis of
their small effective population size, which limits the efficacy of selection4. Moreover,the most obvious pattern of gene-to-gene codon-usage variation in mammals arises
not from selection but from large-scale variation in the GC content — that is, theisochores116. Isochores themselves are probably caused by processes that are primarily
related to recombination and repair such as biased gene conversion117.
Over the past decade, however, researchers have identified several sources ofpotentially strong selection on synonymous mutations in mammals, a trend that was
highlighted by Hurst and others4. Some of these observations fit within the classicalmodel of translational selection, for example, the presence of a weak but positive
relationship between gene expression and codon bias118,119, especially after accounting
for the local GC content120. But studies comparing expression levels to codon
adaptation (that is, to tRNA abundances) have been contradictory33,36,119. Researchers
have also observed significant differences in codon usage between genes specifically
expressed in several different tissues121, as well as variation in relative tRNAabundances by tissue type122. However, there is little evidence for systematic variation
associated with tissue type123, and the quantification of mammalian tRNAs, whichcontain numerous nucleotide modifications, is still relatively noisy122.
Instead, researchers have identified other mechanistic explanations for codon-usage
variation in mammals aside from translational selection. One possibility is selection forthe overall stability of mRNA transcripts124,125 through a skew towards C at fourfold
degenerate sites. In mice, computational analyses suggest that such skews have been
selected to promote mRNA stability124. Moreover, several diseases arise from mutations
that disrupt mRNA structure4,126, providing a clear target of selection. Anotherpossibility related to splicing control is described in the main text.

chaperone proteins to the emergent peptide61. otherexplanations, unrelated to elongation rate, are also plausible such as weaker selection for accurate translationnear the start of a gene, where missense and nonsense
errors would be less costly 24,59. The earliest interpretationof unusual 5′ codon usage posited selection to increase
the initiation rate9; interestingly, the 5′ region of poorly
adapted codons identified by Tuller et al. overlaps significantly with the region in which synonymous codon
choice systematically reduces mRNA stability 54–56,58.
it remains unclear which selective mechanisms are primarily responsible for the unusual and nearly universal
pattern of 5′ codon usage. multiple mechanisms may
certainly operate in different genes; however, it is unclear
why a single gene should experience selection both toincrease its rate of ribosomal initiation9 and to reduce
the subsequent rate of its early elongation57.
Cannarozzi et al.62 recently exposed a third, novel
pattern of intragenic codon usage in eukaryotes: the reuse or autocorrelation of codons across a gene sequence,
driven, they argue, to improve elongation efficiency
through tRNA recycling. if a recently used tRNA molecule is bound to the ribosome, or if it diffuses slowly
compared to ribosomal progression and re-acylation63,
then it would be efficient to re-use the same tRNA molecule for subsequent incorporations of the same aminoacid. This physical model predicts selection for using thesame codon or, more generally, a codon that is read by
the same tRNA species, at nearby sites in a gene that
encode the same amino acid. indeed, Cannarozzi et al.
observed significant autocorrelation of codons acrossgene sequences in most eukaryotes, especially in genesthat are rapidly upregulated in response to stress. of
course, autocorrelation would also be predicted if all sites
in a gene independently experience pressure for biased
codon usage, for example, to match the global pool oftRNAs. To control for overall codon usage, Cannarozzi
et al. compared the degree of autocorrelation in actual
gene sequences to gene sequences that had been reshuffled at random, finding more autocorrelation on averagein the unshuffled genes, although only marginally so.
more convincingly, they observed that autocorrelationis strongest for iso-accepting codons of rare tRNAs in
highly expressed genes, which is predicted by the tRNArecycling hypothesis but not by a selective pressure thatapplies at all sites independently.

Measurements of endogenous expression
Recent developments in mass spectrometry and fluorescence microscopy allow large-scale measurements ofendogenous protein levels64–66. Together with techniques
for quantifying ribosomal occupancy 67 and measuringelongation dynamics68, these advances provide a spectacularly detailed account of basic cellular processes, with
implications for our understanding of codon biases.
Variation in protein/mRNA ratios. Shotgun proteomics haverevealed an extensive role for post-transcriptional processes in determining eventual protein levels in bacteria,
yeast 69, worms70, fruitflies70 and especially mammals65,66.
Whereas the imperfect correlations between protein and

36 | jANUARy 2011 | VolUme 12

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REVIEWS
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Figure 3 | effects of mRnA secondary structure on translation initiation in
0CVWTG4GXKGYU^)GPGVKEU
bacteria. a | Structure in the ribosome binding site (RBS) usuallyinhibits initiation.
However, initiation can occur when the structured element is positioned between the
Shine–Dalgarno sequence (SD) and the start codon (AUG)129, or 15 nucleotides
downstream of the start codon130,131. b | Synonymous mutations in the region from
nucleotide –4 to +37 of a GFP gene alter the predicted folding energies by up to
12 kcal mol–1. A 5′ mRNA folding energy of below –10 kcal mol–1 strongly inhibits GFP
expression in Escherichia coli55. c | More than 40% of human genes have predicted 5′
folding energies below the –10 kcal mol–1 threshold and are therefore expected toexpress poorly in E. coli without modification. AU, arbitrary units. Part b is modified, withpermission, from ReF. 55 © (2009) American Association for the Advancement of Science.

Codon adaptation index
A measure of similarity
between the codon usage of agene and the average codon
usage of highly expressed
genes in a species.

RNA–seq
Quantitative analysis of RNA
in a complex sample byhigh-throughput sequencing.

Ribosomal footprint
A fragment of mRNA thatis protected by ribosomesfrom nuclease digestion in aribosomal-profiling experiment.

Upstream ORFs
ORFs that are located 5′ fromthe primary ORF. They arethought to inhibit translationof the primary ORF.

mRNA levels (R2 ≈ 47–77% in E. coli 65,71, 73% in yeast 65
and 29% in humans66) may previously have been seen
as measurement noise, researchers have since attributed much of the variation in protein/mRNA ratios tosequence-derived characteristics of genes. in a recent
study in human cells 66, the strongest correlates ofsteady-state protein levels, controlling for mRNA levels,
were coding-sequence length (reflecting the fact that
longer transcripts are less stable72 or slower to initiate73),amino acid content (reflecting the variable costs associated with synthesizing different amino acids, or variable
rates of protein degradation) and predicted 5′ mRNA
structure (reflecting lower initiation rates when the 5′
structure is strong). importantly, the codon adaptationindex35, which correlates strongly with mRNA levels
in yeast 65 and weakly in humans66, shows little or no
significant correlation with the amount of protein permRNA molecule in either organism65,66; this suggeststhat codon adaptation does not significantly increase
the protein yield from a given message, at least among
endogenous genes 74,75. it is important to note thatsteady-state protein levels are influenced by both protein production and protein degradation, so any variation in degradation rates unrelated to codon usage willfurther reduce the correlation between codon usage andprotein/mRNA ratios.

Ribosomal footprints. ingolia et al. recently devised aclever application of RNA–seq to quantify ribosomeprotected RNA fragments in a cell, thereby estimating‘ribosomal footprints’ across the transcriptome67. Thismethod has provided rich information about translational regulation and it has uncovered some startlingphenomena, such as an abundance of upstream ORFs
with non-AUG start codons. The footprint data inyeast show a greater mean density of ribosomes in thefirst 100–150 codons of genes, suggesting locally slow
elongation; this is consistent with the observed presenceof poorly adapted codons in the 5′ region58,59. There isalso a significant negative correlation, genome-wide,
between a transcript’s ribosome density and the experimentally measured strength of mRNA structure near its
start site76, suggesting that strong 5′ mRNA structure
retards translational initiation and reduces the densityof translating ribosomes.
Remarkably, on averaging data from all yeast genes,
Tuller et al. 37 also observed a negative correlation
between predicted mRNA folding energy and ribosome density among the first 65 codons, suggestingthat strong mRNA structure downstream of the startsite retards translational elongation. This observation
is surprising, given the helicase activity of translating ribosomes77; however, the correlation between the
genome-wide average profiles of mRNA folding andribosome density does not imply a correlation at thelevel of individual sites. ingolia et al. also measured
ribosomal footprints under amino acid starvation andfound that one-third of yeast genes showed substantially
increased or decreased translational efficiency in these
conditions compared with controls67. A detailed parsingof the relationship between a gene’s amino acid content
and translational response to starvation may improve
design principles for overexpressed heterologous genes,
which often induce starvation78,79 (see below).
Translational efficiency. Notions of translational efficiency differ in the literature on gene expression. ingolia
et al.67 defined the translational efficiency of a gene as
the number of bound ribosomes per mRNA molecule;by contrast, Tuller et al.37,57 and others defined efficiency
as protein yield per mRNA molecule (that is, the ratio
of protein abundance to mRNA abundance). The second definition is more relevant to issues of total protein
synthesis, whereas the former definition may be more
relevant to ribosomal availability and overall cellular fitness. These two notions of translational efficiency are
only weakly correlated for endogenous genes (R2 < 2.5%
comparing the data by ingolia et al.67 to ReF. 80), indicating that the density of ribosomes on a given mRNA
molecule does not determine the amount of proteinthat is produced from it. Similarly, in yeast, a gene’s
codon adaptation index 35 explains less than 3% of thevariance in protein abundance per mRNA67. Both ofthese observations are consistent with the idea that, formost endogenous genes, the initiation is rate limitingfor protein production38,39 and therefore determines theamount of protein produced from each message, regardless of ribosome density or codon adaptation7,9 (FIG. 2);

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REVIEWS
however, this logic may not apply to overexpressed
heterologous genes, which are described in the following
section (FIG. 4).

Measurements of heterologous expression
Codon bias has a crucial role in heterologous gene
expression. However, there is often a disconnection
between technological and evolutionary studies ofcodon bias — a gap that partly reflects genuine differences between endogenous and heterologous situations.
in many biotechnological applications, a transgene ismassively overexpressed, accounting for up to 30% of
the protein mass in cell. As a result, the principles that
relate heterologous codon usage to protein levels may
differ substantially from the endogenous case.
The idea that initiation generally limits translation
may not apply to an overexpressed transgene whosemRNA accounts for a large proportion of total cellular mRNA. in such a case, inefficient use of ribosomesalong the overexpressed mRNA may be sufficient tofeed back and significantly deplete available ribosomes, thereby reducing initiation rates and retarding
further heterologous protein production9 (FIG. 4). Thus,
we might expect that the elongation effects of codon
usage will influence protein yields per mRNA moleculefor overexpressed genes. Nonetheless, we should notnecessarily expect that the codons that are adapted toefficient elongation for endogenous genes will correspond to the efficient codons for heterologous genes,
because overexpression causes amino acid starvationand concomitant alternations in the abundances ofcharged tRNAs78,79,81. indeed, there was no significant
correlation between codon adaptation35 and expression
levels in two large-scale systematic experiments55,79. infact, even endogenous genes that are essential during
amino acid starvation such as amino acid biosynthetic
enzymes preferentially use codons that are poorly

C+PKVKCVKQPNKOKVGFUNQYGNQPICVKQP(TGGTKDQUQOGU

2TQVGKPU

D+PKVKCVKQPNKOKVGFTCRKFGNQPICVKQP

Figure 4 | the elongation rate may influence the rate of protein synthesis for an
overexpressed gene. Unlike most endogenous genes, mRNA froman overexpressed
0CVWTG4GXKGYU^)GPGVKEU
transgene may account for a substantial proportion of total cellular mRNA. In this case,slow elongation (caused by poor codon adaptation to charged tRNA pools, say) can
increase the density of bound ribosomes and thereby reduce the pool of available
ribosomes in the cell. Such a depletion of available ribosomes will feed back to reducethe initiation rate of subsequent translating ribosomes on the message, thereby
reducing the rate of protein synthesis. This is illustrated schematically by comparing
overexpressed mRNAs with slow elongation (above) and rapid elongation (below), but
identical initiation sequences. Thus, the relationship between codon adaptation and therate of protein synthesis per mRNA molecule may differ for an overexpressed transgene
compared to an endogenous gene (FIG. 2).

adapted to the typical pool of charged tRNAs, but are
well adapted to starvation-induced tRNA pools78,79.
Despite the complications described above, the fieldof codon optimization has traditionally focused on
adjusting codon usage to match cellular tRNA abundances in standard conditions, disregarding otherdimensions of bias. However, strategies are now changing. Several recent studies advocate for the role of global
nucleotide content 82,83, local mRNA folding 55,84, codon
pair bias85, a codon ramp57 or codon correlations62 in
optimizing heterologous expression (TABLe 1).
Effects of codon adaptation on expression levels. manystudies show strong effects of rare codons on heterologous expression. in E. coli, stretches of rare AGA or AGG
codons cause ribosome pausing and co-translationalcleavage of mRNA86, ribosomal frameshifting 87 or aminoacid misincorporation88. Consistent with theoretical
expectations, codons that are read by rare tRNAs canslow elongation by several fold89. And stretches of AGG
codons near the ribosome-binding site (RBS) can reduce
protein yields by obstructing translation initiation90.
Although such studies are convincing, they usually
address the effect of a subset of rare codons, often inlong stretches, in E. coli cells; it is not known whether
these principles can be applied in general.
observations such as those above were quickly followed by efforts to adjust the global codon adaptationof transgenes to cellular tRNA abundances. Several
approaches have been proposed: ‘CAi maximization’
replaces all codons by the most preferred codons in
the target genome, but this could result in unbalanced
charged tRNA pools2; ‘codon harmonization’91 puts some
non-preferred codons in positions that correspond to
predicted protein domain boundaries; and ‘codon sampling’ adjusts the codon usage to reflect the overall usage
in the target genome. in the absence of tRNA abundance
estimates, codon frequencies in the target genome aresometimes used. it has also been suggested that codon
usage should match the profile of charged tRNAs rather
than total tRNAs79,81. The utility of codon adaptation
approaches is still unclear, as they have not been systematically compared against each other, and several
anecdotal studies argue both for (for example, ReF. 92)
and against (for example, ReF. 93) their efficiency.
Codon adaptation algorithms typically optimize
many sequence properties at once. This makes it difficult to determine which parameter causes observed differences in expression. in two recent multi-gene studies,
between 60% and 70% of genes experienced increased
expression upon codon optimization94,95, but whether
this was a direct consequence of increased codon
adaptation or other sequence properties is unclear.
in our study of 154 synonymous variants of GFP, weobserved no significant correlation between the codon
adaption index 35 and expression levels in E. coli 55, buta weak positive correlation was later found using nonlinear regressions37,96. in any case, adaptation of codon
usage is limited to species with pronounced and wellunderstood variation in tRNA concentrations, such asbacteria and yeast.

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REVIEWS
Effects of nucleotide bias on expression levels. Nucleotide
biases are pervasive in natural genes and have the potential to alter the interactions of mRNA with DNA, withproteins and with itself, thereby influencing RNA production, degradation and translation rates. many ofthese effects are characterized, but this knowledge has
yet to find its way into standard codon optimization
procedures.

GC-rich mRNAs can form strong secondary structures, and, in bacteria, strong structure near the RBS prohibits initiation53,55,97 (FIG. 3b). As a result, more than 40%
of human genes would be expected to express poorly
when placed in E. coli without modification (Fig. 3c).
Strong structure near the start codon reduces heterologous expression in yeast as well (G.K., unpublishedobservations), consistent with evolutionary analyses56.

Table 1 | Coding-sequence covariates of gene expression and other sources of codon bias that are unrelated to gene expression
Parameter

species

Relationshiptype ofwith expression evidence

Proposed mechanism

Refs
Refs
(computation)* (function)

CAI, FOP or tAIAll

Complex(see text)

Experimental Translation elongation rate CAI35, FOP28,
and accuracy
tAI1282,37,55,
81,94,96

Rare codon stretches

Bacteria

Negative

Experimental Translation elongation rate
and accuracy

90

Rare codons between protein
domains

Bacteria

Complex

Experimental Translation elongation andprotein folding

91,132Frequency of starvationresistant codons

Bacteria

Positive

Experimental Translation elongation rate 79,81
and accuracy81

Frequency of abundant codons

All

Complex

Experimental Unclear‡

133

134

Weak mRNA folding at start codon

Bacteria

Positive

Experimental Translation initiation rate

mfold135

53,55,97mRNA stem–loop 15 nt
downstream of start codon

Bacteria,
mammals

Positive

Experimental Translation initiation rate

130, 131

130,131mRNA stem–loops further
downstream

All

Complex

Experimental Translation elongation

135

46,84

Transcription terminators

Bacteria

Negative

Experimental Transcription

RNAMotif136

RNase E sites

Bacteria

Negative

Experimental mRNA stability

137

miRNA target sites

Eukaryotes

Negative

Experimental Translation, mRNA stability TargetScan138

Various mRNA regulatory elements

All

Complex

Theoretical

High GC content, low A content

Mammals

Positive

Experimental Transcription, mRNA
processing, mRNA export

High CpG content

Mammals

Positive

Experimental Transcription

102

Codon pair bias

Bacteria,
mammals

Positive

Experimental Translation elongation rate 85, 104

85,104

Codon ramp

All

Complex§

Theoretical

57

Codon correlation

Eukaryotes

Positive

Experimental Translation elongation rate 62

62

UnknownAll

Complex

Experimental Protein-folding efficiency

107

Unknown

Eukaryotes

Complex

Theoretical

4, 142

UnknownAll

Complex

Experimental Protein post-translational
modification

Replication strand nucleotide bias

Bacteria,
Unknown
mitochondria

None

Unknown

CTAG avoidanceBacteria

None

Restriction avoidance

Codon adaptation

mRNA folding

Regulatory motifs

Translation, mRNA stability TransTerm139

Nucleotide bias
140, 141

82,83,101

Other sources of codon bias

Unknown

Translation initiation rate

Splicing regulation
44

44

143

143

Note, this table lists coding-sequence-derived parameters that can be changed by synonymous mutations. Other parameters may be important for expression (forexample, the identity of the amino-terminal amino acid or the length of the sequence) but they require non-synonymous changes. *Generic tools to calculate someof these parameters: codonW140 (http://codonw.sourceforge.net/), INCA141 and GeneDesigner144. ‡The frequency of abundant codons is highly correlated with GC
content in mammals. §The codon ramp is predicted to decrease the cost of translation, which may indirectly influence expression levels. CAI, codon adaptationindex; FOP, frequency of ‘optimal’ codons; miRNA, microRNA; tAI, tRNA adaptation index.

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REVIEWS
No such effect has been described in mammals; on thecontrary, high GC content generally increases expression
levels in mammalian cells (see below). However, a strong
mRNA hairpin in the coding sequence has been reported
to interfere with translation in mammalian cells84, andstrong hybrids between RNA and DNA (the R-loops)
may interfere with transcription98.
GC-poor mRNAs are unlikely to fold strongly, butthey often carry other sequence elements that limit
expression. For example, low GC content is commonly
believed to limit the expression of Plasmodium falciparum
genes in E. coli, although the mechanisms are unknown.
Such mRNAs may be targets for RNase e, which cleaves
AU-rich sequences with low sequence specificity 99.
The situation is slightly clearer in mammals, in which
low GC content (or high A content) has been shown to
reduce expression82,83. This effect is common knowledge
in virology, as HiV and human papilloma virus (HPV)
genes are poorly expressed in human cells unless thegene sequences are optimized100,101. The rate-limiting
step in these cases may be transcription or nuclear RNA
export 82,83, which is consistent with the efficient expression of GC-poor genes in cytoplasmic transcription
systems based on the vaccinia virus101.
little is known about the functional consequencesof replication strand-related bias or CTAG avoidance,
which are common in prokaryotes. High CpG content
was reported to correlate with high expression in mammalian cells102, possibly by altering the distribution of
nucleosomes on DNA.
Other effects of synonymous mutations on expression levels. other examples of synonymous mutations
influencing expression have been described as primarily anecdotal observations. in E. coli, overrepresented
codon pairs103 were proposed to decrease translation
elongation rates104, although this conclusion was later
disputed105. in an attempt to produce attenuated strains,
Coleman et al.85 partially de-optimized codon pairs in thepoliovirus genome and observed a reduction in protein
yield of several fold and a reduction in viral infectivity of
1,000-fold in mammalian cells. A version of GFP with

1.
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Conclusions
Recent years have begun to see a convergence of experimental work on endogenous and heterologous gene
expression, as both types of studies take advantage ofhigh-throughput, quantitative techniques. Heterologous
studies using large libraries of random or unbiased synonymous sequence variation55,81,97 are especially importantfor uncovering and comparing general rules to optimize
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gaining predictive power for transgene optimizationand deducing the underlying mechanisms that linkcodon usage and gene expression. High-dimensional,
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gene therapies108,109 — but they do not generally identify
molecular mechanisms. our hope, over the long term,is that cross-fertilization between biotechnological andmolecular biological studies will elucidate effective strategies for designing transgenes, as well as the mechanistic
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Acknowledgements

We thank L. Hurst and C. Wilke for helpful discussions. We
apologize to those whose work we were unable to cite becauseof space constraints. G.K. acknowledges funding fromthe Wellcome Trust. J.B.P. acknowledges support from the
Burroughs Wellcome Fund, the David and Lucile Packard
Foundation, the James S. McDonnell Foundation, the Alfred
P. Sloan Foundation, the Defense Advanced Research Projects
Agency (HR0011-05-1-0057) and the US National Instituteof Allergy and Infectious Diseases (2U54AI057168).

Competing interests statement

The authors declare no competing financial interests.

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