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Young proteins experience more variable selection pressures thanold proteins
Anchal Vishnoi, Sergey Kryazhimskiy, Georgii A. Bazykin, et al.
Genome Res. published online October 4, 2010
Access the most recent version at doi:10.1101/gr.109595.110

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Research

Young proteins experience more variable selection
pressures than old proteins
Anchal Vishnoi,1 Sergey Kryazhimskiy,1 Georgii A. Bazykin,2 Sridhar Hannenhalli,3,4
and Joshua B. Plotkin1,4
1

Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; 2Institute for Information Transmission
Problems of the Russian Academy of Sciences, Moscow 127994, Russia; 3Department of Genetics, University of Pennsylvania,
Philadelphia, Pennsylvania 19104, USA
It is well known that young proteins tend to experience weaker purifying selection and evolve more quickly than old
proteins. Here, we show that, in addition, young proteins tend to experience more variable selection pressures over timethan old proteins. We demonstrate this pattern in three independent taxonomic groups: yeast, Drosophila, and mammals.
The increased variability of selection pressures on young proteins is highly significant even after controlling for the factthat young proteins are typically shorter and experience weaker purifying selection than old proteins. The majority of ourresults are consistent with the hypothesis that the function of a young gene tends to change over time more readily thanthat of an old gene. At the same time, our results may be caused in part by young genes that serve constant functions overtime, but nevertheless appear to evolve under changing selection pressures due to depletion of adaptive mutations. In
either case, our results imply that the evolution of a protein-coding sequence is partly determined by its age and origin, andnot only by the phenotypic properties of the encoded protein. We discuss, via specific examples, the consequences of thesefindings for understanding of the sources of evolutionary novelty.
[Supplemental material is available online at http://www.genome.org.]
Protein sequences vary by three orders of magnitude in theircharacteristic rates of evolution. What determines a protein’s rateof evolution has been debated for several decades. Early hypotheses suggested that a protein’s evolution is governed by at least two
factors: the protein’s level of functional constraint (i.e., the densityof functionally important residues) (Ingram 1961) and the protein’s overall importance to the organism (Wilson et al. 1977).
Most research on the determinants of protein evolution has interrogated these structural/functional hypotheses by comparing
inferred substitution rates with high-throughput phenotypic measurements. Researchers have considered many phenotypic covariates of evolutionary rates, including the measured number ofphysical and genetic interactions of a protein, its codon usage, thefitness consequences of its knockout, its sequence length (Lipman
et al. 2002), its mRNA level, and its protein level (see Pal et al. 2006
and references therein). Of these covariates, expression levels show
the strongest correlations with evolutionary rates (Pal et al. 2001;
Drummond et al. 2006), leading to the view that selection against
mistranslation-induced misfolding plays a dominant role in shaping the evolutionary rates of protein sequences (Drummond andWilke 2008; Lobkovsky et al. 2010). However, the interdependencies
among these phenotypic features, and the variable amounts ofnoise with which they have been measured, complicate the causal
interpretation of their observed correlations with evolutionary rate
(Plotkin and Fraser 2007; Wolf et al. 2009).
Most comparative analyses, including the studies referenced
above, assume that the characteristic rate at which a protein sequence evolves depends on structural, functional, and expression

4

Corresponding authors.
E-mail jplotkin@sas.upenn.edu.
E-mail sridharh@pcbi.upenn.edu.
Article published online before print. Article and publication date are at
http://www.genome.org/cgi/doi/10.1101/gr.109595.110.

constraints that are intrinsic to the encoded protein and that canbe measured by phenotypic assays within a given organism.
However, some studies have raised an intriguing possibility thatthe rate of a protein’s evolution may partly be determined by its
evolutionary origin, independent of its intrinsic characteristics(Domazet-Loso and Tautz 2003; Daubin and Ochman 2004; Alba
and Castresana 2005, 2007; Garcia-Vallve et al. 2005; Wolf et al.
2009). After all, genomes are comprised of heterogeneous sets ofgenes that differ not only in their functions, but also in theirevolutionary histories. Some genes in a genome are ‘‘old,’’ in thesense that they have identifiable orthologs across a diverse range of
species spanning vast evolutionary distance. Other genes are‘‘young’’ in the sense that orthologs are identifiable only in closely
related species. Young genes can arise in a genome by duplication,
followed by accelerated substitutions, so that similarity to theancestral copy becomes undetectable (Ohno 1970; Long 2001;
Lynch and Katju 2004), by conversion of noncoding sequences(Long et al. 2003; Levine et al. 2006; Cai et al. 2008; Heinen et al.
2009) or by other mechanisms (Toll-Riera et al. 2009).
It is well established that a gene’s origin can influence its
molecular evolution: In bacteria, the so-called ‘‘ORFan’’ genes(genes with no homologs outside of a closely related group of strainsor species) are less likely to be lost in evolution than other genes(Van Passel et al. 2008); synonymous substitution rates of newly
acquired genes in bacteria and viruses depend on the nucleotide
composition of their source genomes (Lawrence and Ochman 1997;
Kryazhimskiy et al. 2008); in both prokaryotes and eukaryotes,
genes that have duplicated recently experience elevated substitution rates compared with those that duplicated in the distant past
(Lynch and Conery 2000; Jordan et al. 2004).
More generally, old genes are known to evolve more slowly
and experience stronger purifying selection than young genes(Domazet-Loso and Tautz 2003; Daubin and Ochman 2004; Alba
and Castresana 2005, 2007; Garcia-Vallve et al. 2005; Wolf et al.

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Vishnoi et al.
2009). Since gene age is defined by the phylogenetic breadth ofsignificant BLAST hits, some have argued that such a result istautological and circular (Elhaik et al. 2006). However, others haveconvincingly demonstrated that this pattern is nontrivial: The
characteristic evolutionary rate of a protein in recent times, i.e.,
within one taxonomic group such as Aspergillus, depends onwhether the protein has a homolog in distant species outside of thetaxonomic group (Alba and Castresana 2007; Wolf et al. 2009).
Moreover, the differences in evolutionary rates between young andold genes are significant even when controlling for expression
level and functional characteristics (Wolf et al. 2009). This observation is intriguing in light of the ongoing debate about the determinants of evolutionary rates, because it suggests that a protein
retains some ‘‘memory’’ of its age and that its molecular evolution
is influenced by its time of origin, not only by its function or expression level.
Here, we demonstrate a further nontrivial relationship between gene age and gene evolution: During relatively recent evolutionary timescales, young proteins exhibit more variable selection pressures (dN/dS) than old proteins. We show that this patternis highly significant even after controlling for the fact that (1) old
proteins tend to experience stronger purifying selection overall(Alba and Castresana 2005), and (2) old proteins tend to be longer(Wolf et al. 2009). We interpret these results in light of simple
models of protein evolution under constant and variable selective
regimes. On the one hand, our findings suggest that young genes
tend to experience variable selective regimes because they lose
their function along some lineages and gain new functions alongothers. At the same time, some of our results are consistent with
the idea that newly arisen genes serve the same function acrossdivergent lineages, but nevertheless appear to evolve under temporally variable selection pressures, as measured by dN/dS, due todepletion of sites that confer adaptive benefits (see below).

Results
Conceptual backgroundWe first discuss some conceptual models of protein evolution that
motivate our study. We outline two mechanisms (illustrated in Fig.
1) by which young genes might be expected to experience morevariables selection pressure than old genes.
First, since a newly arisen gene is likely to perform a redundant (e.g., if created by duplication) or highly specialized (ifcreated de novo or by horizontal transfer) function (Domazet-Loso
and Tautz 2003; Daubin and Ochman 2004), it is more prone thanan old gene either to lose its function or to acquire novel functions
in subsequent lineages (Wolf et al. 2009). Under these scenarios,
the selective constraint on the newly arisen protein will either berelaxed or increased along subsequent lineages in the phylogeny.
Consequently, the dN/dS ratio of a young gene is expected to increase or decrease along temporally subsequent branches in aphylogeny. In contrast, an old gene is more likely to be highly
optimized for its (typically essential) function (Wolf et al. 2009),
resulting in a relatively constant dN/dS ratio over recent times.
Aside from the mechanism above, there is a second, more
subtle reason why a newly arisen gene might experience morevariable dN/dS ratios—a reason that applies even if the newly arisen
gene serves the same, constant function in all species descendantfrom the species in which it originated. In the case of a constantfunction, a gene will gradually accumulate adaptive substitutions
in all lineages, which increase organismal fitness by improving,

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Figure 1. According to conceptual models of protein evolution, young
genes experience more variable selection pressures over time than old
genes. The two examples shown here illustrate a young gene that arose at
the common ancestor of species 1–4 (stars), compared with an old genethat arose in the distant past. (Bottom) The cumulative number of nonsynonymous substitutions over time, the slope of which is proportional tothe dN/dS value. If the young gene retains its original function (left), it
rapidly depletes adaptive sites, resulting in a dramatic slowdown in the
nonsynonymous substitution rate: v2 < v1. If the young gene experiences
a functional change in a lineage (either relaxed constraint or neofunctionalization; right), as indicated by a lighting bolt, it rapidly accumulates
nonsynonymous substitutions, resulting in v2 > v1. In both cases the
young gene experiences variable selection pressures (i.e., large |v1 – v2|),
whereas the old gene experiences roughly the same selection pressure in
recent times, because it retains its function and most of its adaptive sites
have already been depleted: v2 » v1.

say, the protein’s enzymatic activity. Neutral (typically synonymous) mutations will accrue at a constant rate, but, under mostfitness landscapes, adaptive (typically nonsynonymous) mutations will eventually be depleted, resulting in a gradual slowdown
in the rate of nonsynonymous substitutions (Hartl et al. 1985). The
quantitative properties of such a substitution pattern have recently
been studied theoretically (Kryazhimskiy et al. 2009), and such
patterns have been directly observed in specific bacterial enzymes(Hartl et al. 1985). As these theories and experiments show, a gene
under a constant selective regime (i.e., selection for a constantfunction) will initially accrue beneficial substitutions rapidly, but
eventually such substitutions will accrue more slowly as the supplyof adaptive sites is depleted. As a result of this process, the dN/dS
ratio will tend to decrease over evolutionary time—a pattern that
we might interpret as ‘‘changing selection pressures,’’ even though
the protein is adapting on a static fitness landscape.
Under this scenario of constant function, a young gene
initially has a large supply of available adaptive mutations. By
substituting some of these mutations, the gene’s function is slowly
optimized and the supply of further adaptive mutations is diminished; thus, we expect that the dN/dS value of a young gene will
decrease over time. In contrast, an old gene, being highly optimized to its function, is likely to have already exhausted all beneficial mutations by recent times; we expect it to evolve undernegative selection and fix only neutral and/or nearly neutral mutations. Thus, old genes tend to fall on the asymptote of the substitution trajectory, and so, their dN/dS values will remain roughly
constant over recent times. Thus, under this simple model of an
adaptive landscape (Hartl et al. 1985), a newly arisen gene thatserves a constant function over all descendant species should
be expected to exhibit descending dN/dS values along temporallyDownloaded from genome.cshlp.org on October 11, 2010 - Published by Cold Spring Harbor Laboratory Press

Variability in selection pressure on proteinssubsequent branches in a recent phylogeny, whereas an old gene of constantfunction is expected to have relatively
constant dN/dS values within a recent phylogeny.
In summary, according to the conceptual models discussed above, young
genes, whether they change their function or retain the same function subsequent to their origin, are expected toexperience more variable dN/dS ratios overtime than old genes. In subsequent sections we compare these theoretical expectations with sequence data.

Branch-specific dN/dS values in yeast,
Drosophila, and mammals
We studied temporal variation in the selective constraints on proteins in three
independent taxonomic groups: yeast,
Drosophila, and mammals. Within eachof these groups we identified five species
or groups of species (see Methods). The
species were chosen so as to ensure appropriate divergence along all branches
in the five-species phylogenetic tree inorder to obtain reliable estimates ofbranch-specific dN/dS ratios (Kryazhimskiy
and Plotkin 2008). Within each taxonomic group, for each orthologous genefamily with exactly one ortholog per
species, we performed a multiple alignment or obtained alignments from public
databases, as described in the Methods
section. For each alignment we used PAML
to estimate four or five dN/dS values (Fig.
2): v0 for the branch connecting theoutgroup; v1 and v2 for two subsequentinternal branches, v3 for most terminal
branches, and v4 for additional internal branches (for Drosophila). Genes withdS estimates smaller than 0.02 in any
branch were excluded from further analysis (other cutoffs produced similar results). This criterion yielded 2807 (of originally 3747) ortholog groups in yeast;
9118 (of 9657) ortholog groups in Drosophila; and 10,637 (of 13,771) ortholog
groups in mammals.

Old proteins are longer and exhibit
stronger purifying selection
than young proteins
We categorized each orthologous groupas either ‘‘old’’ or ‘‘young’’ depending onwhether or not a homolog was identifiable in bacteria (see Methods). Alternative definitions of old and young genes
based on a more closely related outgroupspecies produced similar results (see below).

Figure 2. The five-species phylogeny for each of the taxa analyzed in our study (yeast, mammals,
Drosophila). For each ortholog we estimated branch-specific dN/dS values along two subsequent
branches (v1, v2), as well as separate values for most terminal branches (v3), the outgroup branch (v0),and additional internal branches in Drosophila (v4). We quantified the temporal variability in dN/dS as theabsolute difference n = |v1 – v2|.

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Vishnoi et al.
As was reported previously (Wolf et al. 2009), we observed that old
genes exhibit stronger purifying selection in recent times than
young genes do, on average. In other words, in each of the threetaxonomic groups the distribution of v1 is skewed toward smaller
values among old genes than among young genes (and similarly
for v2). Despite this trend, there is a large overlap in the dN/dS
values between old and young genes, as reported previously (Fig. 3;
Wolf et al. 2009).
In addition, we observed that old genes are longer, on average,
than young genes, as reported previously (Wolf et al. 2009). Nonetheless, there is a substantial overlap in the lengths of old andyoung genes as well (data not shown).

Young proteins experience more variable selection pressure
than old proteins
For each of the three taxonomic groups we studied, the five-species
phylogeny contains two internal, temporally subsequent branches(Fig. 2). For each orthologous protein alignment, we calculated thecorresponding dN/dS values along these subsequent branches,
denoted v1 and v2. In order to inspect the temporal variation in
selection pressures we studied the absolute difference in dN/dS between these two branches: v = |v1 – v2|. (A related measure of variability in dN/dS, namely max{v1/v2, v2/v1}, yields similar results.)
A meaningful comparison of variability in dN/dS between old
and young genes requires that we first control for the fact that old
genes tend to have smaller v1 values. After all, dN/dS values areconstrained to be nonnegative, and so a gene’s v1 value will naturally influence the value of v = |v1 – v2|. Similarly, we must also
control for the fact that old genes are typically longer than new
genes, as length could also influence the value of v. In fact, we
control for the joint distribution of v1 and length in order tocontrol for the aforementioned effects as well as any possible interaction between length and v1 that might influence variabilityof dN/dS.
In order to control for these potential biases, we sampled anequal number of old and young genes in such a way so as to ensurethat the joint distribution of v1 values and lengths among the oldgenes in the sample was the same as the joint distribution among
the young genes in the sample (see Methods). This conservative
sampling approach discards lots of data, resulting in 710, 5346,
and 8088 sampled genes in yeast, Drosophila, and mammals, respectively (in each case, half of the sampled genes were old andhalf young). Using Peacock’s two-dimensional version of the
Komolgorov-Smirnov test (Peacock 1983), we verified that thejoint distributions of v1 and length did not differ between the two

Figure 3. The distribution of dN/dS values (v1) among old and young
mammalian genes. The old genes are skewed toward lower dN/dS values,
but there is a substantial overlap. When comparing old and young genes
we control for this bias by analyzing a subset of old and young genes withthe same distributions of v1 values.

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classes of sampled genes (Peacock test: P = 0.9 for yeast, P = 0.2
for mammals, P = 0.5 for Drosophila). (The distributions of v1 and v
values among the 8088 sampled mammalian genes are shown as
Supplemental Figs. S1 and S2.) We then compared the values of v =|v1 – v2| between these old and young genes, using the Wilcoxon
test (one-sided alternative). This procedure provides a test ofwhether young genes exhibit significantly greater temporal variation in selection pressures (i.e., larger values of v) than old genes,
controlling for both length and v1.
In mammals, we found that young genes exhibited significantly more variable selection pressures, i.e., larger values of v, thanold genes (P = 8.8 3 10 5, Wilcoxon test). In particular, the averagevariability in selection pressures was 25% higher among young
genes compared with old genes. Similarly, in Drosophila young
genes also exhibited significantly more variable selection pressuresthan old genes (average v was 26% greater among young genes, P =7.5 3 10 7) and also in yeast (average v was 56% greater among
young genes, P = 5.0 3 10 3). Thus, in all three taxa we find thesame phenomenon: Young genes experience significantly morevariable selection pressures, on average, than old genes (Table 1).
Table 2 shows the proportion of sampled genes, in either the
young or old categories, whose dN/dS values increased, decreased,or remained roughly constant over time. As the analysis above
would suggest, young genes were more likely to exhibit changing
dN/dS values, compared with old genes. When dN/dS values didchange over time, there was a bias toward increasing values, especially among the young genes.
Table 2 shows that there is a greater tendency toward v2 > v1
in young genes than there is in old genes. This implies that ‘‘depletion of adaptive sites’’ is not the primary mechanism responsible for the greater variability in dN/dS that we have observed among
young genes. Nevertheless, the depletion mechanism is likely tocontribute to the increased rate variability in some of the young
genes.

Alternative definitions of gene age produce the same
qualitative pattern
In the analysis above we used the existence of a homolog in bacteria to define ‘‘old’’ versus ‘‘young’’ genes. For Drosophila andmammalian genomes, the choice of bacterial species as the outgroup set is quite conservative, producing a stringent definition ofan old gene. To ensure that our results are robust with respect tooutgroup choice, we repeated the above analyses using a diverse
set of fungi species (Saccharomyces cerevisiae, Debaryomyces hansenii, Shizosaccharomyces pombe, and Cryptococcus neoformans) as theoutgroup set for defining old Drosophila genes, and the Anopheles
gambiae, Drosophila melanogaster and Caenorhabditis elegans genomes as the outgroup set for defining old mammalian genes.
These new criteria resulted in a different number of sampled old–young gene pairs that control for the joint distributions of lengthand v1 (3008 Drosophila pairs and 2754 mammalian pairs), butnonetheless highly significant differences in the variability of selection pressures in old versus young genes (Wilcoxon test, 5 3
10 6 for Drosophila, and 4 3 10 21 for mammals). Thus, even witha more inclusive definition of ‘‘old,’’ the data support our contention that old genes tend to experience less variable selection
pressures than young genes.
In addition, we randomly permuted the ‘‘old’’ and ‘‘young’’ labels of genes, as a negative control, and repeated the analysis above.
In this case, none of the Wilcoxon tests was significant (P = 0.54
for yeast, P = 0.78 for Drosophila, P = 0.55 for mammals), as expected.

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Variability in selection pressure on proteins
Table 1. The number of genes used in our statistical analysis, themean variability in dN/dS (n = |v1 – v2|) among old and young genes,
the proportional elevation of variability among young genes, andthe corresponding Wilcoxon P-value

No. of old-young pairs
Mean of young n
Mean of old nDifference in mean nWilcoxon P-value

Mammals

Drosophila

Fungi

4044
0.14540.116525%
8.8 3 10 52682
0.05590.044426%
7.5 3 10 7355
0.11840.076056%
5.0 3 10 3

In each of the three taxonomic groups young genes are significantly morevariable in their dN/dS values, on average, than old genes.

Gene age, but not housekeeping functionality, determines
variation in selection pressures
Old genes are known to be enriched for housekeeping functions(Wolf et al. 2009). Therefore, the possibility remains that genefunction, as opposed to gene age itself, underlies the observed differential variability in selection pressures between old and young
genes. To test for this possibility we asked whether categorization of genes by function (housekeeping or not) would lead to thesame qualitative results as those for categorization by age (old vs.
young).
To perform this analysis, we considered the previously identified set of 1364 human housekeeping genes (Podder and Ghosh2010). Such genes are enriched for critical biological functions,
including translation, cellular biosynthesis, etc. All other humangenes were categorized as ‘‘nonhousekeeping.’’ We repeated theanalysis above on the resulting 1281 pairs of housekeeping/nonhousekeeping genes that had been chosen to match their distributions of v1, as above. We found no significant difference in thevariability of selection pressures (v) between the two functionalclasses of genes (Wilcoxon test, P = 0.46) despite the fact that acomparison between old and young genes of the same power (1281
old/young pairs) yields a highly significant difference (Wilcoxon
test, P = 2.1 3 10 3). Thus, the observed difference in selection
pressures between old and young genes is unlikely to be caused bydifferences in housekeeping function, but rather should be attributed to differences in gene age itself.

Gene age, but not expression level, determines variationin selection pressures
Because old genes are more often essential (Wolf et al. 2009), they
may be expressed at higher levels on average than young genes.
Some models for the relationship between expression level andevolutionary rate suggest that genes expressed at high levels
should exhibit both lower dN/dS values and less variation in dN/dS
values compared with genes expressed at low levels (Gout et al.
2010). Therefore, much like the concern about housekeeping
functions above, the possibility remains that expression level, as
opposed to gene age itself, underlies the observed differential
variability in selection pressures between old and young genes. To
test for this possibility we asked whether categorization of genes byexpression level (high vs. low) would lead to the same qualitative
results as categorization by age (old vs. young).
We performed this analysis for the yeast and Drosophila because the putative relationships between expression level and ratevariability do not apply to mammals (Gout et al. 2010). Ideally, we
would like to categorize all genes into those expressed at high andthose expressed at low level in natural conditions. As is common
practice, we used a gene’s codon adaptation index (CAI) as a surrogate for its typical expression level in nature (Sharp and Li 1987).
We divided S. cerevisiae genes into those expressed at high levels(top 5% of CAI values) and the remaining genes. We repeated theanalysis above on the resulting 110 pairs of high/low CAI genesthat had been matched to control for their v1 and length distributions, as above. We found no significant difference in the variability of selection pressures (v) between these two classes of genes(Wilcoxon test, P = 0.09), despite the fact that a comparison between old and young yeast genes of the same power (110 old/young pairs) yields a significant difference (Wilcoxon test, P =0.01). The same results hold in Drosophila as well: There is no significant difference in the variability of selection pressures (v) between 425 pairs of high/low CAI genes matched to control for theirv1 and length distributions (Wilcoxon test, P = 0.38), despite thefact that a comparison between old and young Drosophila genes ofthe same power (425 old/young pairs) yields a significant difference (Wilcoxon test, P = 0.008). Thus, the observed difference inselection pressures between old and young genes is unlikely to becaused by differences in expression levels.

Illustrative examples of variable selection pressures
on young genes
The statistical analyses above reveal a significant tendency foryoung proteins to experience more variable selection pressuresover time, compared with old proteins. Below, we discuss three
examples of young genes drawn from the mammalian group,
which illustrate the various evolutionary scenarios that may be
responsible for the observed overall pattern. We emphasize thatthese three examples are included below only to illustrate the idea
behind possible underlying mechanisms, and they do not constitute proof of each mechanism. Although the statistical trends wehave observed are highly significant, it is easy to find examples
that do or do not follow the general trend. Nonetheless, the anecdotal examples are useful for illustrative purposes.

Example 1: v2 > v1 due to relaxed negative selection
OR5A1 is an olfactory receptor belonging to the largest multigene
family, expressed in a wide variety of metazoan species (Buck andAxel 1991; Freitag et al. 1995; Barth et al. 1997). The dN/dS value ofTable 2. The proportion of old and young sampled genes in eachof three taxa that exhibited increasing, decreasing, or roughly
constant dN/dS values

Mammals (4044 old/young pairs)
v2 > v1
v2 < v1
v2  v1
Drosophila (2682 old/young pairs)
v2 > v1
v2 < v1
v2  v1
Yeast (355 old/young pairs)
v2 > v1
v2 < v1
v2  v1

Old genes

Young genes

32%
14%
54%

34%
16%
50%

11%
9%
80%

17%
8%
75%

26%
16%
58%

44%
7%
49%

v1  v2, if the two values fall within 5% of each other. In each of the taxa,
a greater proportion of young genes exhibit changing dN/dS values thanthe corresponding proportion of old genes.

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Vishnoi et al.
OR5A1 increases along the primate lineage (v1 = 0.03 and v2 =0.91), consistent with the known relaxation of negative selection
on olfactory receptors in primates compared with other mammals(Glusman et al 2001). Thus, OR5A1 exemplifies the case of a young
protein whose temporal variation in selection pressures is probably
caused by loss of functionality in a very recent lineage.

Example 2: v2 > v1 due to increased positive selection
Lysozyme (LYZ) cleaves the peptidoglycan of the bacterial cell wall.
Several sites in the gene encoding this enzyme are known haveexperienced positive along the primate lineage (Messier andStewart 1997; Yang 1998), resulting in a new function that allows
primates to digest bacteria (Leonard 2002). As expected, we observed an increase in dN/dS along the primate lineage within
mammals (v2 = 1.01 vs. v1 = 0.11). Thus, LYZ exemplifies that thecase of a young gene with temporal variation in selection pressures
may be due to changing functionality across lineages.

Example 3: v2 < v1 due to possible depletion of adaptive sites
Oligodendrocyte myelin glycoprotein (OMG) is an anchor protein that originated along the mammalian lineage after mammals
and fish diverged from their common ancestor (Kehrer-Sawatzki
et al. 1998). The protein generally experiences strong purifying
selection in mammals, presumably because of its important rolein brain development (Kuma et al. 1995; Duret and Mouchiroud
2000; Wang et al. 2007). OMG is required for the production of the
myelin sheath that allows rapid conduction in axons, and it serves
this same function in all mammals (Wang et al. 2002). Since thisyoung gene arose recently (i.e., near the base of the mammalianclade) and serves the same function across all mammalian species,
we might expect that dN/dS will decrease along subsequent mammalian branches due to depletion of adaptive sites. Indeed, thispattern is observed (v1 = 0.44 and v2 = 0.17), which is consistent
with the plausible mechanism of depleting adaptive sites.

Discussion
We have demonstrated that the age of a gene influences the tempo
of its molecular evolution: Young genes experience more variable
dN/dS values than old genes. We have observed this pattern in three
independent taxonomic groups—mammals, Drosophila, and yeast—suggesting that the phenomenon is quite general. Although thegene age has a significant effect on the mean variability in rates,
controlling for length and v1, age alone does not explain a large
proportion of all variation in rate variability (<2% in each taxa).
Our results complement previous findings that young genes
evolve faster, on average, than old genes (Domazet-Loso and Tautz
2003; Daubin and Ochman 2004; Alba and Castresana 2005, 2007;
Garcia-Vallve et al. 2005; Wolf et al. 2009). Alba and Castresana(2005) proposed two alternative explanations for this trend (seealso Domazet-Loso and Tautz 2003). Their ‘‘constant constraint’’model posits that each gene has an intrinsic level of constraint (dN/dS) that it maintains throughout its life, but because faster evolving
genes are generally more dispensable, they are more often lost in
evolution. This model would predict no systematic differences inrate variability between old and young genes. Our analysis reveals
such systematic differences and is thus evidence against the‘‘constant constraint’’ model.
The ‘‘increasing constraint’’ model of Alba and Castresana(2005) posits that the strength of selection on a gene changes
through its lifetime: Immediately after birth, a gene evolves under
weak negative or positive selection, while later in life it evolves

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primarily under strong negative selection. The depletion of adaptive sites (Hartl et al. 1985), discussed above, provides a simple
mechanistic basis for the model of ‘‘increasing constraint.’’ Under
this model, the substitution rate of a protein should decrease overtime. Since the substitution rate cannot decrease indefinitely, we
expect that the decrease will be faster at initial stages of a gene’s lifeand slower at later stages. Indeed, many young genes experience
substantial decreases in their dN/dS ratios over time (Table 2), andmany old genes show no substantial changes in their dN/dS ratio
over time, but the difference in these trends between old and young
genes is not statistically significant in all taxa (data not shown)—suggesting that ‘‘increasing constraint’’ alone cannot explain theelevated variability of dN/dS ratios among young genes.
We have also observed many young genes whose dN/dS ratios
increase over time (see Table 2), which is inconsistent with eitherof the models suggested by Alba and Castresana (2005). We therefore propose to extend their ‘‘increased constraint’’ model to incorporate a period of high variability in a gene’s evolutionary rate
immediately after it is born. We have conceived of two sources ofsuch initial variability. First, a newborn gene is likely to perform
a redundant or highly specialized function (Domazet-Loso andTautz 2003), which can easily become unnecessary in a changing
environment. This would lead to a relaxed constraint on the young
gene and result in an increase of its dN/dS ratio. Later, the same gene
may be co-opted to another function and evolve under positive
selection, which would further elevate its dN/dS ratio. Alternatively,
even if a newborn gene maintains its original function, it gradually
exhausts available adaptive mutations, and so its dN/dS ratio decreases over time. According to both of these mechanisms, young
genes will experience more variable dN/dS values over recent times
than old genes, as we have observed.

Methods
Estimating dN/dS
In each of the broad taxonomic groups we studied—yeast, Drosophila, and mammals—we analyzed five species, or groups ofspecies, including an unambiguous outgroup (see Fig. 2). The
species were chosen so that the average dS along all branches would
exceed 0.02, as is required to obtain reliable estimates of dN/dS.
In addition, species were chosen so that no branch length would
exceed dS = 1.0, on average, in order to avoid the effects of saturation. For the yeast analysis we used Saccharomyces cerevisiae,
S. paradoxus, S. mikatae, and S. bayanus, with S. castelli as an outgroup. For mammals, we used Homo sapiens, Macaca mulatta, Canisfamiliaris, and Monodelphis domestica, with Ornithorhynchus anatinus as an outgroup. For Drosophila, we grouped the 12 species withsequenced genomes into five groups: Drosophila melanogaster,D. simulans, and D. sechellia comprised the first group; D. yakuba
and D. erecta comprised the second group; D. ananassae comprised
the third group; D. pseudoobscura and D. persimilis comprised thefourth group; D. willistoni, D. virilis, D. mojavensis, and D. grimshawi
comprise the fifth group, and was used as the outgroup.
In yeast, we identified orthologous open reading frames
from ftp://ftp.ncbi.nih.gov/genomes/Fungi/. We then aligned theorthologous yeast ORFs using MUSCLE, and superimposed thisalignment onto the amino acid sequence using pal2nal (Suyama
et al. 2006). For mammals, we used the orthologs and alignmentsfrom the UCSC Genome Browser. For Drosophila, we used theorthologs and alignments from FlyBase (ftp://ftp.flybase.net/
genomes/12_species_analysis/clark_eisen/).
The orthologous gene alignments for yeast, Drosophila, andmammals were each analyzed using PAML to estimate the dN/dS

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Variability in selection pressure on proteinsvalues in the five-species tree: v0 for the branch connecting theoutgroup; v1 and v2 for two subsequent internal branches, v3 formost terminal branches, and v4 for additional internal branches (ifany). All dN/dS values were estimated using maximum likelihood(PAML model 2). Genes with v1 > 5 or v2 > 5 were discarded, because these genes were typically associated with imprecise dS estimates. Note that we did not use the dN/dS values associated withterminal branches in our analysis because they are known to bebiased due to weakly selected mutations that segregate in the
population of each species. Treating such segregating mutations as
fixed differences, as PAML does, leads to an upward bias in the dN/
dS value along a terminal branch (Rocha et al. 2006; Kryazhimskiy
and Plotkin 2008).

Our sampling procedure is partly random, and so it producesslightly different sets of sampled genes for each pseudorandom
seed. Nonetheless, repeating the procedure above with different
pseudorandom seeds does not change our qualitative results.

Acknowledgments
We thank the Plotkin and Hannenhalli labs for productive conversations and input. J.B.P. acknowledges support from Burroughs
Wellcome Fund, the David and Lucile Packard Foundation, theJames S. McDonnell Foundation, the Alfred P. Sloan Foundation,and the Defense Advanced Research Projects Agency (HR001105-1-0057). J.B.P., S.H., and A.V. acknowledge support from NIGMS
R01GM085226.

Definitions of old and young genes
Genes were categorized as either old or young, depending on thepresence of a homolog in a distant reference species or set of species. Using a BLAST E-value threshold of 10 6, as in Wolf et al.
(2009), any gene with an ortholog in bacteria (ftp://ftp.ncbi.nih.
gov/genomes/Bacteria/) was considered to be old, while all othergenes were considered to be young.
For Drosophila and mammals we explored additional alternative definitions of old and young genes. In particular, for Drosophila we alternatively used the presence/absence of a BLAST hit
in S. cerevisiae, D. hansenii, S. pombe, and C. neoformans or C. albicans
as the definition of old/young genes. And for mammals, we alternatively used the presence/absence of a BLAST hit in A. gambiae,
D. melanogaster, or C. elegans as the definition of old/young genes(see main text).

Sampling old and young genes to control for v1 and length
In order to control for differences in the joint distributions of v1
and the length between old and young genes, we sampled a subset
of genes for which the joint distributions were virtually identicalusing the following sampling procedure. We first binned genelengths and v1 values into 200 arithmetic bins each, resulting ina two-dimensional array of 40,000 cells. Each one of these cells
represented a narrow range of allowable lengths and v1 values andcontained a certain number of corresponding old genes, n, anda certain number of corresponding young genes, m. Considering
each cell in turn, we sampled without replacement from each classthe number of genes equal to the lesser of n and m (e.g., in the casen < m we sampled all n of the old genes in the cell and n of theyoung genes, discarding the remaining young genes). After performing this procedure for each cell, we obtained an equal numberof sampled old and young whose joint distributions of lengths andv1 values were guaranteed to be nearly identical. We verified thatthe resulting sampled genes did not differ in their joint distributions using Peacock’s two-dimensional Komolgorov-Smirnov test
(Peacock 1983).
Applying this procedure to the mammalian genes produced
4044 sampled genes (half of these young and half old). As desired,
we found no significant difference in the joint distribution of v1
values and lengths between the sampled old and young mammalian genes (Peacock test, P = 0.2). Similarly, for Drosophila weobtained 2682 sampled genes without significant differences intheir joint distributions of v1 and length (Peacock test, P = 0.5). For
yeast this procedure discarded many genes because of significant
differences between the original joint distributions, and resulted in
355 sampled genes. Again, there was no significant difference intheir joint distributions of v1 and length between the old andyoung sampled genes (Peacock test, P = 0.9). In each taxonomic
group, we used the sampled genes when comparing the variability
in selection pressures, v = |v1 – v2|, between old and young genes.

References
Alba MM, Castresana J. 2005. Inverse relationship between evolutionaryrate and age of mammalian genes. Mol Biol Evol 22: 598–606.
Alba MM, Castresana J. 2007. On homology searches by protein Blast andthe characterization of the age of genes. BMC Evol Biol 7: 53. doi:
10.1186/1471-2148-7-53.
Barth AL, Dugas JC, Ngai J. 1997. Noncoordinate expression of odorant
receptor genes tightly linked in the zebrafish genome. Neuron 19: 359–
369.
Buck L, Axel R. 1991. A novel multigene family may encode odorant
receptors: A molecular basis for odor recognition. Cell 65: 175–187.
Cai J, Zhao R, Jiang H, Wang W. 2008. De novo origination of a newprotein-coding gene in Saccharomyces cerevisiae. Genetics 179: 487–
496.
Daubin V, Ochman H. 2004. Bacterial genomes as new genes homes: The
genealogy of ORFans in E. coli. Genome Res 14: 1036–1042.
Domazet-Loso T, Tautz D. 2003. An evolutionary analysis of orphan genes in
Drosophila. Genome Res 13: 2213–2219.
Drummond DA, Wilke CO. 2008. Mistranslation-induced protein
misfolding as a dominant constraint on coding-sequence evolution. Cell
134: 341–352.
Drummond DA, Raval A, Wilke CO. 2006. A single determinant dominates
the rate of yeast protein evolution. Mol Biol Evol 23: 327–337.
Duret L, Mouchiroud D. 2000. Determinants of substitution rates inmammalian genes: Expression pattern affects selection intensity but not
mutation rate. Mol Biol Evol 17: 68–74.
Elhaik E, Sabath N, Graur D. 2006. The ‘‘inverse relationship between
evolutionary rate and age of mammalian genes’’ is an artifact ofincreased genetic distance with rate of evolution and time of divergence.
Mol Biol Evol 23: 1–3.
Freitag J, Krieger J, Strotmann J, Breer H. 1995. Two classes of olfactory
receptors in Xenopus laevis. Neuron 15: 1383–1392.
Garcia-Vallve S, Alonso A, Bravo IG. 2005. Papillomaviruses: Different genes
have different histories. Trends Microbiol 13: 514–521.
Glusman G, Yanai I, Rubin I, Lancet D. 2001. The complete human olfactory
subgenome. Genome Res 11: 685–702.
Gout J-F, Kahn D, Duret L, Paramecium Post-Genomics Consortium. 2010.
The relationship among gene expression, the evolution of gene dosage,
and the rate of protein evolution. PLoS Genet 6: e1000944. doi: 10.1371/
journal.pgen.1000944.
Hartl DL, Dykhuizen DE, Dean AM. 1985. Limits of adaptation: The
evolution of selective neutrality. Genetics 111: 655–674.
Heinen TJAJ, Staubach F, Haming D, Tautz D. 2009. Emergence of a newgene from an intergenic region. Curr Biol 18: 1527–1531.
Ingram VM. 1961. Gene evolution and the haemoglobins. Nature 189: 704–
708.
Jordan IK, Wolf YI, Koonin EV. 2004. Duplicated genes evolve slower thansingletons despite the initial rate increase. BMC Evol Biol. 4: 22. doi:
10.1186/1471-2148-4-22.
Kehrer-Sawatzki H, Maier C, Moschgath E, Elgar G, Krone W. 1998. Genomic
characterization of the neurofibromatosis type 1 gene of Fugu rubripes.
Gene 222: 145–153.
Kryazhimskiy S, Plotkin JB. 2008. The population genetics of dN/dS. PLoS
Genet 4: e1000304. doi 10.1371/journal.pgen.1000304.
Kryazhimskiy S, Bazykin GA, Dusshoff J. 2008. Natural selection for
nucleotide usage at synonymous and nonsynonymous sites in influenza
A virus genes. J Virol 82: 4938–4945.
Kryazhimskiy S, Tkacik G, Plotkin JB. 2009. The dynamics of adaptationon correlated fitness landscapes. Proc Natl Acad Sci 106: 18638–
18643.

Genome Researchwww.genome.org

7

Downloaded from genome.cshlp.org on October 11, 2010 - Published by Cold Spring Harbor Laboratory Press

Vishnoi et al.
Kuma K, Iwabe N, Miyata T. 1995. Functional constraints against variations
on molecules from the tissue level: Slowly evolving brain-specific genes
demonstrated by protein kinase and immunoglobulin supergene
families. Mol Biol Evol 12: 123–130.
Lawrence JG, Ochman H. 1997. Amelioration of bacterial genomes: Rates ofchange and exchange. J Mol Evol 44: 383–397.
Leonard WR. 2002. Food for thought. Dietary change was a driving force inhuman evolution. Sci Am 287: 106–115.
Levine MT, Jones CD, Kern AD, Lindfors HA, Begun DJ. 2006. Novel genes
derived from noncoding DNA in Drosophila melanogaster are frequently
X-linked and exhibit testis-biased expression. Proc Natl Acad Sci 103:
9935–9939.
Lipman DJ, Souvorov A, Koonin EV, Panchenko AR, Tatusova TA. 2002. The
relationship of protein conservation and sequence length. BMC Evol Biol
2: 20. doi: 10.1186/1471-2148-2-20.
Lobkovsky AE, Wolf YI, Koonin EV. 2010. Universal distribution of protein
evolution rates as a consequence of protein folding physics. Proc Natl
Acad Sci 107: 2983–2988.
Long M. 2001. Evolution of novel genes. Curr Opin Genet Dev 11:673–680.
Long M, Betran E, Thornton K, Wang W. 2003. The origin of new genes:
Glimpses from the young and old. Nat Rev Genet 4: 865–875.
Lynch M, Conery JS. 2000. The evolutionary fate and consequences of
duplicate genes. Science 290: 1151–1155.
Lynch M, Katju V. 2004. The altered evolutionary trajectories of gene
duplicates. Trends Genet 20: 544–549.
Messier W, Stewart C-B. 1997. Episodic adaptive evolution of primate
lysozyme. Nature 385: 151–154.
Ohno S. 1970. Evolution by gene duplication. Springer-Verlag, Berlin/
Heidelberg/New York.
Pal C, Papp B, Hurst LD. 2001. Highly expressed genes in yeast evolve slowly.
Genetics 158: 927–931.
Pal C, Papp B, Lercher MJ. 2006. An integrated view of protein evolution.
Nat Rev Genet 7: 337–348.
Peacock JA. 1983. Two-dimensional goodness-of-fit testing in astronomy.
Mon Not R Astron Soc 202: 615–627.
Plotkin JB, Fraser HB. 2007. Assessing the determinants of evolutionary rates
in the presence of noise. Mol Biol Evol 24:1113–1121.

8

Genome Researchwww.genome.org

Podder S, Ghosh TC. 2010. Exploring the differences in evolutionary rates
between monogenic and polygenic disease genes in human. Mol Biol
Evol 27: 934–941.
Rocha EP, Smith JM, Hurst LD, Holden MT, Cooper JE, Smith NH, Feil EJ.
2006. Comparisons of dN/dS are time dependent for closely related
bacterial genomes. J Theor Biol 239: 226–235.
Sharp PM, Li WH. 1987. The codon adaptation index––a measure of
directional synonymous codon usage bias, and its potential
applications. Nucleic Acids Res 11: 1281–1295.
Suyama M, Torrents D, Bork P. 2006. PAL2NAL: Robust conversion ofprotein sequence alignments into the corresponding codingalignments. Nucleic Acids Res 34: W609–W612.
Toll-Riera M, Bosch N, Bellora N, Castelo R, Armengol L, Estivill X, Alba MM.
2009. Origin of primate orphan genes: A comparative genomics
approach. Mol Biol Evol 26: 603–612.
Van Passel MWJ, Marri PR, Ochman H. 2008. The emergence and fate ofhorizontally acquired genes in Escherichia coli. Plos Comput Biol. 4:
e1000059. doi: 10.1371/journal.pcbi.1000059.
Wang KC, Koprivica V, Kiam JA, Sivasankaran R, Guo Y, Neve RL, He Z. 2002.
Oligodendrocyte-myelin glycoprotein is a Nogo receptor ligand that
inhibits neurite outgrowth. Nature 417: 941–944.
Wang HY, Chien HC, Osada N, Hashimoto K, Sugano S, Gojobori T, Chou
CK, Tsai SF, Wu CI, Shen CKJ. 2007. Rate of evolution in brain-expressed
genes in humans and other primates. PLoS Biol 5: e13. doi: 10.1371/
journal.pbio.0050013.
Wilson AC, Carlson SS, White TJ. 1977. Biochemical evolution. Annu Rev
Biochem 46: 573–639.
Wolf YI, Novichkov PS, Karev GP, Koonin EV, Lipman DJ. 2009. The
universal distribution of evolutionary rates of genes and distinct
characteristics of eukaryotic genes of different apparent ages. Proc Natl
Acad Sci 106: 7273–7280.
Yang Z. 1998. Likelihood ratio tests for detecting positive selection andapplication to primate lysozyme evolution. Mol Biol Evol 15: 568–573.

Received April 23, 2010; accepted in revised form September 1, 2010.