Vol 13, Issue 5, 831-837, May 2003
LETTER
Selection on Human Genes as Revealed by Comparisons to Chimpanzee cDNA
Ines Hellmann1,
Sebastian Zöllner2,
Wolfgang Enard1,
Ingo Ebersberger1,
Birgit Nickel1 and
Svante Pääbo1,3
1Max Planck Institute for Evolutionary Anthropology,
D-04103 Leipzig, Germany; 2Department of Human Genetics,
University of Chicago, Chicago, Illinois 60637, USA
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ABSTRACT
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To better understand the evolutionary forces that affect human
genes, we sequenced 5055 expressed sequence tags from the chimpanzee
and compared them to their human counterparts. In conjunction with
intergenic chimpanzee DNA sequences and data on human single-nucleotide
polymorphisms in the genes studied, this allows us to gauge the extent
to which selection affects human genes at a genome-wide scale. The
comparison to intergenic DNA sequences indicates that about 39% of
silent sites in protein-coding regions are deleterious and subject to
negative selection. Further, when the divergence between human and
chimpanzee is compared with the extent of nucleotide polymorphisms
among humans in the same sequences, there is significantly higher
divergence in the 5' untranslated regions (UTRs) but not in other parts
of the transcript. This indicates that positive selection may have had
a considerable influence on 5'UTRs. The dinucleotide CG (CpG) also
exhibits a different substitution pattern within 5'UTRs as compared
with other parts of the genome.
Comparison of the human genome to genomes from
other species such as the mouse (Shabalina et al. 2001 ) and the
pufferfish (Roest Crollius et al. 2000) has emerged as one of the major
approaches toward understanding the evolutionary forces that shape the
human genome (Rubin 2001). However, for several reasons, comparisons to
species distantly related to humans cannot convey the entire picture.
First, patterns of mutation, recombination, and selection are likely to
have changed over the long time periods since these species shared
common ancestors with humans. Second, DNA sequences that are under
little or no selective constraints, such as intergenic, intronic, and
untranslated regions, are so diverged in these organisms that they can
hardly be aligned to human DNA sequences, if at all. Third, mutational
hotspots such as the dinucleotide CpG or microsatellites experience
such frequent changes that the mutational patterns that underlie the
differences seen between distantly related species cannot always be
accurately reconstructed. Thus, in addition to comparisons with highly
diverged genomes, comparisons with genomes of closely related species
are necessary in order to achieve a more complete understanding of the
evolution of the human genome.
The African apes (chimpanzees, bonobos, and gorillas) are the closest
living evolutionary relatives of humans. Of these, the chimpanzees, and
their sibling species, the bonobos, differ from humans by an average of
1.2% in overall genomic DNA sequences (Chen and Li 2001; Ebersberger
et al. 2002) and are estimated to have shared a common ancestor with
humans 4.6–6.2 million yr ago (Chen and Li 2001). Gorillas differ from
humans by an average of 1.6% in genomic DNA sequences and are
estimated to have shared a common ancestor with humans, chimpanzees,
and bonobos 6.2–8.4 million yr ago (Chen and Li 2001). Thus, on
average, chimpanzees and bonobos are the species most closely related
to humans. Of these, chimpanzees are both much more numerous and much
better studied. Therefore, for the comparison of the human genome with
a closely related genome, chimpanzees are the species of choice.
In order to obtain a large and relatively unbiased data set that
provides insight into how genes have diverged between humans and
chimpanzees, we sequenced 5055 chimpanzee expressed sequence tags
(ESTs) and compared them with their human counterparts. The results
show that unexpectedly high levels of purifying selection affect silent
sites in protein-coding parts of human genes. The data furthermore
indicate that 5' untranslated parts of human genes have been the target
of positive selection.
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RESULTS
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cDNA Sequencing
We constructed cDNA libraries from chimpanzee testis and brain and
generated a total of 5055 ESTs of high quality. When clustered, they
yielded 3139 unique chimpanzee cDNA contigs. We compared these
sequences to the public databases and identified 2844 orthologous human
DNA sequences. For 1845 of these sequence pairs, we could identify the
coding sequence (CDS) and consequently also the untranslated regions
(UTRs). From these sequence pairs, 1226 of the chimpanzee cDNA contigs
contained CDS, 1071 3'UTRs, and 582 5'UTRs. This represents
2%–4% of all known human genes (Waterston et al. 2002) and can thus
be expected to be a representative sample to compare the evolution of
the different types of sites in human genes.
Untranslated Regions
For the 5'UTRs, the GC content is 53%, and 10.2% of the compared
nucleotides occur in CpG contexts (Table
1). For the 3'UTRs, the GC content is
41.7% and 2.8% of the nucleotides are in a CpG context. The ratio of
observed CpGs relative to the amount of expected CpGs, given the GC
content of the DNA sequences, is 0.6 for 5'UTRs and 0.3 for 3'UTRs, and
the genome average is about 0.2 (Lander et al. 2001). Thus, although
CpGs are more frequent in both 5'UTRs and 3'UTRs than elsewhere in the
genome, the number of CpG sites is drastically higher in the 5'UTRs
than in the 3'UTRs.
In the 5'UTRs, differences between human and chimpanzee cDNAs occur at
1.12% of the positions. CpG sites in the 5'UTR differ at 4.2% of
positions, whereas non-CpG sites differ at 0.77% (Fig.
1). Thus, within 5'UTRs, differences occur
approximately five times more often at CpG sites than at non-CpG sites.
Transitional differences are 1.8 times more frequent than
transversional differences in 5'UTRs. It is generally thought that the
predominant type of mutation that affects methylated CpG sites is
cytosine deamination, which results in transitional differences (Shen
et al. 1994). However, we find no difference in the
transition–transversion ratio between CpG and non-CpG positions in
5'UTRs.
The 3'UTRs differ between humans and chimpanzees at 0.86% of
positions. CpG sites and non-CpG sites differ at 8.85% and 0.63%,
respectively (Fig. 1). Hence, in 3'UTR, CpG sites contain 14 times more
differences than non-CpG sites. Transitional substitutions at non-CpG
sites are 1.8 times more common than transversions, as in 5'UTR.
However, in contrast to 5'UTRs, at CpG sites in 3'UTRs, transitions are
3.7 times more frequent than transversions. Thus, the transition bias
is larger at CpG sites than at non-CpG sites.
In order to gauge to what extent 5'UTRs and 3'UTRs may evolve under
functional constraints, we compared their divergence between humans and
chimpanzees to the divergence of intergenic sequences (Ebersberger et
al. 2002). The latter class of sequences exhibits slightly higher
levels of divergence between humans and chimpanzees than intronic
sequences and may evolve under little functional constraint (Fig. 1 and
Table 1). The overall divergence at 5'UTRs does not differ
significantly from intergenic sequences (t-test;
P = 0.162, d.f. = 622). Similarly, the divergence of
non-CpGs in 5'UTRs does not differ from the intergenic non-CpG
divergence (t-test; P = 0.277, d.f. = 616).
However, CpG sites in 5'UTR contain three times fewer differences than
intergenic CpG sites (t-test; P < 10–6,
d.f. = 891). Thus, although non-CpG sites in 5'UTRs seem to be under
few constraints, CpG sites in 5'UTRs appear to be subject to
substantial functional constraints. Alternatively, they may have a
lower mutation rate than intergenic CpGs.
Within 3'UTRs, overall divergence is lower than that at 5'UTRs
(t-test; P = 2.6 x 10–6,
d.f. = 818) and intergenic sequences (t-test;
P < 10–6, d.f. = 1473). At non-CpG sites, the
divergence is 0.63%, that is, significantly less than in the two
other categories of sequences (t-tests: 3'UTR-intergenic:
P < 10–6, d.f. = 1497; 3'UTR-5'UTR:
P = 0.0031, d.f. = 771). Consequently, assuming that the
mutation rates for non-CpG sites within the different categories of
sequences are the same, about kg% of the substitutions within 3'UTR
are lost as a result of purifying selection. Furthermore, CpG sites in
3'UTRs have accumulated fewer differences than intergenic CpGs
(t-test; P < 10–6, d.f. = 1275), but
the effect is not as drastic as for CpGs in 5'UTRs.
Because CpGs in 3'UTRs, 5'UTRs, introns, and intergenic regions are
likely to be methylated to different extents, it cannot be assumed that
they are subject to similar average mutation rates. For example, the
fact that the transition/transversion ratio does not differ between
non-CpG sites and CpG sites in the 5'UTRs, whereas it is higher at CpG
sites in 3'UTRs (Table 1) could reflect differences in mutational
pressures between these regions because of differential methylation.
Thus, because the mutation rates are likely to differ for CpG sites in
different regions of genes, it is not possible to tease apart possible
mutation rate heterogeneity and selection acting on CpG sites in the
different regions.
Coding Regions
In the coding regions, we contrast the nondegenerate (nd) sites,
where all potential nucleotide changes result in amino acid
replacements, with fourfold-degenerate (4d) sites, where no potential
changes cause any amino acid replacement. From a total of 426.7 kb of
coding region sequences analyzed, 272.8 kb are nd sites, whereas 61.9
kb are 4d sites. The GC content at nd sites is 47% and the CpG content
4.8%. At 4d sites, the GC content is similarly 48%, whereas the CpG
content is 7.3%.
At nd sites, the overall divergence between humans and chimpanzees is
0.22%. The divergence at nd CpG sites is 1.5% and at non-CpG sites
0.16%. At 4d sites, the overall divergence between the species is
1.1%, roughly five times more than at nd sites. At CpG sites, the
divergence is 8.2% and at non-CpG sites 0.54%. Thus, at 4d sites, CpG
sites have accumulated 15.1 times more differences than non-CpG sites,
whereas at nd sites, CpG sites differ 9.4-fold more than non-CpG sites.
If we use intergenic sequences as a neutral reference for the number of
differences accumulated between humans and chimpanzees, we find that 4d
sites have a slightly lower divergence than expected (t-test;
P = 0.030, d.f. = 1324). In contrast, the divergence at nd
sites is 0.22% and therefore, as expected, substantially lower than in
intergenic regions, indicating that these sites evolve under extensive
functional constraints.
However, when overall divergence is compared, the effects of mutation
and selection are clearly compounded. For example, the fraction of CpG
sites, for which divergence is substantially higher than at non-CpG
sites, varies among the different categories of sites in coding regions
(Table 1). Furthermore, as indicated earlier, the extent of methylation
of CpG sites, and thus their mutation rate, may differ between
different categories of sequences. In order to contrast the extent of
functional constraints affecting different classes of sites, we
therefore restrict our comparisons to non-CpG sites. We again find that
nd non-CpG sites contain substantially fewer differences than the
putatively neutral intergenic regions (t-test;
P < 10–6, d.f. = 2370). The extent of the
reduction indicates that 82% of the mutations have been eliminated as
a result of purifying selection. Surprisingly, we find also that 4d
non-CpG sites contain significantly fewer differences than intergenic
sequence (F-test; P < 10–6, d.f. = 1353). The
extent of this reduction indicates that 39% of mutations have been
eliminated, presumably as a result of purifying selection (Fig. 1).
Thus, not only sites that cause amino acid replacement in coding
regions of human transcripts but also sites that have no direct effect
on the primary structure of the encoded protein seem to be subject to
substantial purifying selection.
Polymorphism vs. Divergence
In order to compare the level of human polymorphism to the level of
human–chimpanzee divergence in a set of homologous gene sequences, the
chimpanzee sequences were aligned to human mRNA-derived sequences. For
these 1304 sequence pairs, we retrieved all entries on human
single-nucleotide polymorphisms (SNPs). This left us with 136
orthologous 5'UTRs, 459 coding regions, and 228 3'UTRs for which both
the chimpanzee–human divergence and the human nucleotide diversity
could be estimated (Table 2, Fig.
2).
If most differences observed both among humans and between humans and
chimpanzees are selectively neutral, then the relative proportions of
differences seen in the different parts of the genes should be the same
for both within-species and between-species comparisons. However, the
relative levels of diversity and divergence in 5'UTRs, in 3'UTRs, at nd
sites, and at 4d sites clearly differ (Fig. 2).
If we compare the levels of diversity and divergence to those at 4d
sites (which may be the least affected by selection within transcribed
regions) we find that, within humans, differences at nd sites and
3'UTRs amount to 49% and 89%, respectively, of the differences at 4d
sites. The corresponding fractions for the human–chimpanzee
comparisons are 15% and 69%, respectively. The difference between the
relative levels of diversity and divergence is not significant for
3'UTR ( 2 = 2.56, d.f. = 1, P = 0.11), but
it is significant for nd sites (2 = 59.08, d.f. = 1,
P < 0.001). This also holds true if CpG sites are excluded
(2 = 20.75, d.f. = 1, P < 0.001; Table 2).
Thus, for changes that affect amino acids in proteins, we observed more
diversity within humans than expected, given the observed divergence
between chimpanzee and human. This indicates that slightly deleterious
amino acid mutations destined to be eliminated by purifying selection
segregate in humans (Ohta 1976).
5'UTRs differ by 0.04% among humans and by 1.18% between humans and
chimpanzees. Relative to the differences at 4d sites, this represents
48% and 111%, respectively. Thus, in stark contrast to nd sites, we
observe more divergence than expected given the diversity at 5'UTRs
(2 = 17.232, d.f. = 1, P < 0.001). The
exclusion of CpG sites from the analysis increases the discrepancy to
53% and 152% of the differences at 4d sites
(2 = 23.538, d.f. = 1, P < 0.001; Fig. 2).
Since our analyses indicate that 4d sites have been under negative
selection, we also normalized the intraspecific diversity in the
various parts of transcripts to a genome-wide estimate of diversity in
humans of 0.075% (Sachidanandam et al. 2001). Similarly, we normalized
the divergence to the genome-wide divergence between humans and
chimpanzees of 1.24% (Ebersberger et al. 2002; Fig. 2C,D). Using this
normalization (Fig. 2), we find that 4d site divergence amounts to 86%
of the genome average, whereas 4d site diversity amounts to 111%,
indicating that 4d sites are similar to nd sites and 3'UTRs in that
slightly deleterious variants segregate in the human population. In
contrast, the divergence in 5'UTRs represents 95% of the genome-wide
divergence but only 53% of the diversity. Thus, also using genome-wide
estimates of intergenic diversity and divergence, we find an excess of
divergence at 5'UTRs.
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DISCUSSION
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CpGs Sites
A substantial proportion of the DNA sequence divergence between
humans and chimpanzees is caused by changes at CpG sites (Ebersberger
et al. 2002). For example, 28% of all transitional differences between
humans and chimpanzees occur at CpG dinucleotides. The reason for this
is that methylated CpG sites are hot spots for transitions (Shen et al.
1994). In addition, transversions are overrepresented at CpG
dinucleotides (Nachman and Crowell 2000; Ebersberger et al. 2002).
Thus, one might assume that the amounts of CpG nucleotides in different
classes of DNA sequences would largely determine the extent of their
divergence between humans and chimpanzees. However, this is not the
case. For example, although 3'UTRs contain about half as many CpG sites
as CDSs, they have diverged almost twice as much. A reason for this may
obviously be the different amounts of functional constraints that
affect the sequences. This is reflected by the fact that
also non-CpG sites in 3'UTRs have diverged about twice as much as in
CDSs. However, additional factors must play a role because
the ratio of divergence at CpG and non-CpG sites differs between
different parts of the transcripts (Table 1). In particular, CpG sites
in 5'UTRs appear to evolve in a different way from CpG sites in other
parts of the transcripts. Although the ratios of the divergence at CpG
and non-CpG sites are 12.3 and 14.0 in coding regions and 3'UTRs,
respectively, and thus not too different from intronic and intergenic
sequences where it is 15.1 and 14.6, respectively, the ratio is only
5.4 in the 5'UTRs (Table 1). Furthermore, although the
transition/transversion ratio is substantially higher at CpG sites than
at non-CpG sites in coding regions, 3'UTRs, introns, and intergenic
regions, this is not the case in 5'UTRs. A possible explanation for
this is that CpG islands, that is, regions of high CpG content located
in upstream areas of about 70% (Davuluri et al. 2001) of genes, often
extend into the first exon of genes (Pesole et al. 1997). Because CpGs
in CpG islands may be conserved (Jones and Takai 2001) and not
methylated in the germ line, they may have both a lower divergence and
a lower transition/transversion ratio than CpG sites elsewhere. Indeed,
43% of the 5'UTRs compared exhibit CpG island features. CpGs within
these 5'UTRs have a divergence of 3.2%, and 5'UTRs not carrying CpG
island features have a divergence of 5.4% (2 = 20.6,
P = 10–5,d.f. = 1). Thus, CpG islands are
likely to be the cause of a large part, if not all, of the unusual
patterns of evolution of CpGs in the 5'UTRs of human transcripts.
However, further studies that also include nontranscribed upstream
areas of genes are necessary to fully understand the patterns observed.
Constraints on 3'UTRs and Fourfold Degenerate Sites
Because differences in the extent of methylation in different
regions of genes are likely to influence mutation rates at CpG sites,
we exclude CpG sites in order to estimate the extent to which different
regions of the transcripts are subject to functional constraints.
Non-CpG sites in 3'UTRs exhibit 29% less divergence than non-CpG sites
in intergenic regions, indicating that selective constraints act on
3'UTRs. Sequence motifs that influence mRNA stability and trafficking
(Duret et al. 1993; Pesole et al. 1997) are putative targets of
purifying selection in 3'UTRs.
At non-CpG 4d sites in protein-coding regions, we observe 39% lower
divergence than in intergenic regions, in agreement with a study
(Bustamante et al. 2002) that compared synonymous sites in functional
human genes to pseudogenes. It is not clear why as much as 39% of 4d
sites might be under purifying selection in humans. Because an overall
codon usage bias is observed genome-wide in humans (Lander et al.
2001), the preference of certain isoacceptor tRNAs over others may play
a role (Sharp and Li 1986). In humans, codon usage bias is positively
correlated with expression breadth (Urrutia and Hurst 2001), which in
turn covaries with expression levels (Duret and Mouchiroud 2000). If
codon usage bias is the reason for the low divergence at 4d sites, its
effect might be particularly strong for our data set, because genes
highly expressed in brain or testis are likely to be overrepresented in
our cDNA libraries. However, other factors such as conservation of mRNA
secondary structures (Eyre-Walker and Bulmer 1993) or sequences
involved in mRNA splicing (Cartegni et al. 2002) could also reduce
divergence at 4d sites. Eventually, functional studies of a
representative number of human transcripts carrying different
synonymous codons will be necessary to elucidate the functional basis
for the purifying selection that obviously affects a substantial
proportion of synonymous sites in human genes.
Constraints on Amino Acid Substitutions
To estimate the extent to which selection affects protein-coding DNA
sequences, we calculate the number of nucleotide substitutions that
change amino acids per nucleotide sites that potentially change amino
acids (Ka) and the number of substitutions that do not change
amino acids per site that cannot change amino acids (Ks) and
we compute the ratio between them (Ka/Ks; Kimura
1983; Eyre-Walker and Keightley 1999). Among the 1226 transcripts
compared between humans and chimpanzees, the average
Ka/Ks ratio is 0.22. In a previous study
(Eyre-Walker and Keightley 1999) in which 46 genes retrieved from
public databases were compared between chimpanzees and humans, the
average Ka/Ks ratio was 0.63, thus indicating much
fewer functional constraints than the present study. However, 10 of the
46 genes in the previous study encode components of the immune system
that are known to evolve rapidly. Furthermore, a recent study (Fay et
al. 2001) has estimated the proportion of human polymorphisms under
strong constraint by excluding rare variants suspected to include
slightly deleterious polymorphisms. For the remaining polymorphisms,
expected to be able to reach fixation, a ratio of amino acid changes to
silent changes ( a/s) of 0.20 was found,
almost identical to the Ka/Ks ratio found here for
the human–chimpanzee comparison. Thus, we believe that the estimate
from our data for genes selected at random from brain and testis
transcripts represent a good overall estimate of the level of selective
constraints under which human genes evolve.
Intriguingly, a study that compared 2820 rat and mouse coding regions
revealed a Ka/Ks ratio of 0.19 (Makalowski and
Boguski 1998), strikingly similar to the 0.22 observed between humans
and chimpanzees. This is unexpected because the effective population
size is generally thought to be larger in rodents than in humans, which
should result in more efficient selection against deleterious variants
(Ohta 1995). However, as the genes of our sample might be highly
expressed, they might also be unusually conserved (Duret and Mouchiroud
2000). Further studies of the Ka/Ks ratios in
various groups of mammals are necessary to arrive at an understanding
of the relative contribution of different factors that influence the
level of selection on the proteome.
Excess of Chimp–Human Divergence in 5'UTRs
When we normalize the extent of diversity among humans as well as
divergence between humans and chimpanzees to 4d sites, we find that
both nd sites and 3'UTRs exhibit more diversity than divergence. This
may be due to slightly deleterious variants that segregate in the human
population and are destined to become eliminated by selection. A rough
estimate of the proportion of such slightly deleterious variants is
69% for amino acid polymorphisms and 23% for 3'UTRs (Fig. 2A,B).
In contrast, within 5'UTRs we observe more divergence than expected
given the diversity. Because 4d sites are subject to purifying
selection, reduced divergence at 4d sites could in principle account
for this observation. However, when we restrict the analysis to non-CpG
sites and add 39% to the divergence at 4d sites (our estimate of the
extent of polymorphism removed by purifying selection), the excess of
divergence in 5'UTRs remains (2 = 7.509, d.f. = 1,
P = 0.006). Furthermore, when genome-wide estimates of
diversity (Sachidanandam et al. 2001) and divergence (Chen and Li 2001;
Ebersberger et al. 2002; Fujiyama et al. 2002) are used for
normalization, the excess of divergence at 5'UTRs still remains. It
should be noted, however, that the polymorphism data currently
available are crude estimates of the human diversity. Thus, the
estimates of the magnitude of the effects of selection have to be taken
with caution.
The excess of divergence at 5'UTRs is an unexpected finding. It raises
the possibility that 5'UTRs are subject to positive selection in humans
and chimpanzees. This is interesting with regard to the recent finding
that mRNA and protein levels have changed substantially in several
tissues between humans and chimpanzees (Enard et al. 2002). Because
transcriptional promoters often overlap with exons encoding 5'UTRs, and
because sequences in 5'UTRs are often involved in the control of
translation, it may be that DNA sequences that affect gene expression
levels at the transcriptional and translational levels have been
frequent targets of positive selection during human evolution. Further
work that explores the functional properties of promoters and 5'UTRs in
human and chimpanzees is necessary to test this suggestion.
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METHODS
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cDNA Libraries and Sequencing
We isolated total RNA from cerebral cortex of a female chimpanzee,
and cerebral cortex and testis of a male chimpanzee using Trizol (Life
Technologies). We purified the total RNA using RNAeasy columns (Qiagen)
and isolated mRNA using Oligotex (Qiagen) according to the
manufacturer's instructions. cDNA was synthesized according to the
SMART Protocol (Clontech); ligated into the SfiI sites of the
bacterial plasmid pUCHi, a derivative of pUC19 constructed in-house
that contains SfiI sites, allowing directional cloning of
cDNAs; and transformed into Epicurian Coli XL10-Gold (Stratagene).
Plasmids were prepared using QIAprep (Qiagen) and sequenced from their
5' ends using the M13 reverse primer with Big Dye Terminator Cycle
sequencing (Perkin Elmer) and ABI 3700 sequencing machines. A total of
4011 cDNA sequences were obtained from the male chimp brain library,
992 from the testis library, and 52 from the female brain library.
Sequence Analyses
Nucleotides with a Phred quality score (Ewing et al. 1998) below 15
were masked. If three or more adjacent nucleotides were masked, the
sequence was cut at that point and the longest of the resulting
fragments used for further analysis. Vector sequences were removed and
mitochondrial sequences were excluded. The resulting sequences were
assembled into contigs using the TIGR Assembler (Sutton et al. 1995).
The average number of reads that cover each base in the contigs was
1.49. An empirical upper limit of the sequencing error rate can be
gauged from the most frequent transcript, which occurred 30 times among
the cDNA clones (average clone length 488 bp). None of the clones
carried a mismatch. Thus, as a rough estimate the DNA sequences
determined contain less than 1 mismatch per 10,000 bp.
Repeats were masked using RepeatMasker (see
http://repeatmasker.genome.washington.edu/) and the resultant cDNA
sequences were used to search dbEST, the GenBank sections for primate
and high-throughput sequencing (htg), unigene cluster consensus
sequences (Coward et al. 2002), the human genome sequence assembly
(July 2001), and Refseq (http://www.ncbi.nlm.nih.gov/) using
the BLAST program (Altschul et al. 1990) on the HUSAR platform
(http://genome.dkfz-heidelberg.de). The best BLAST result from each
database with an e-value smaller or equal to 0.01 was realigned to the
unmasked chimpanzee sequence using Bestfit (Wisconsin GCG-package).
These alignments were generated by scoring matches with 1, mismatches
with –2, the opening of a gap with –4, and the extension of a gap
with –1, whereas gap extensions were penalized only for the first 50
bp. From these alignments, the one with the highest alignment score
( 30) was used for further analysis. This sieving process left us with
2890 alignments; from those, all alignments with 95% or less identity
were checked manually, leaving us with 2844 alignments.
The database entries for 1103 of these genes contained a gene
annotation that was used to extract the coding region. If the database
entry did not contain a gene annotation and was not an EST entry,
Genscan (Burge and Karlin 1997) was applied to the human genome
sequence starting 50 kb before and ending 50 kb after the alignment to
the chimpanzee cDNA contig. Identified coding regions were extracted
and collected in a local database, which was again searched with the
chimpanzee cDNA contig using BLAST. We thus identified 742 additional
CDSs. The average divergence within 4d sites, 3'UTRs, 5'UTRs, and nd
sites was similar for the genes for which the CDS was identified using
Genscan and the previously annotated ones (data not shown).
If the cDNA contig was longer than the coding region, everything before
the start codon of the CDS was assigned as 5'UTRs (582 5'UTRs), and
everything behind the stop codon as 3'UTRs (1071 3'UTRs). These
alignments were used to count substitutional differences in the 3'UTRs,
5'UTRs, and coding regions between human and chimpanzee. In order to
estimate the average percentage of nonsynonymous and synonymous
differences, we considered sites that were either nondegenerate or
fourfold degenerate in both species.
A nucleotide was counted as being within a CpG dinucleotide if the
chimpanzee and/or the human sequence contained a CG. A 5'UTR sequence
was considered to be within a CpG island if the ratio of
observed-to-expected CpG dinucleotides (given the base composition) was
higher than 0.6 and the GC content was above 50% over the entire 5'UTR
alignment (Gardiner-Garden and Frommer 1987).
Human–Chimpanzee Divergence
Because humans and chimpanzees are so closely related, multiple
substitutions at the same site are highly unlikely. Therefore, we
measured divergence by dividing the number of substitutions by the
number of base pairs compared for a given sequence (Nei and Kumar
2000). The significance of differences in the average divergence of
different sequence categories was assessed with a t-test
assuming unequal variances.
Human Diversity Data
Of the 1845 orthologous human sequences for which we could identify
the CDS, 1304 were mRNA-derived sequences. For simplicity, we used only
these mRNA-derived sequences, which were repeat masked and compared
with dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) using BLAST. For
identification of SNPs, alignments were required to be over 95%
identical and at least 50 bp long. SNPs derived from EST data mining
were excluded as they may contain false-positive SNPs that would cause
an inflated diversity in 3'UTRs because they are derived mainly from 3'
ends of transcripts.
We tried to account for the fact that different average numbers of
chromosomes were sampled for SNP discovery in 5'UTRs, 3'UTRs, and
coding regions by two different approaches. First, we accepted only
SNPs for which 20 or fewer chromosomes were sampled because, for some
regions, SNPs tended to be discovered in very small samples. Second, we
used only SNPs detected by reduced representation shotgun sequencing
(Altshuler et al. 2000) and in BAC overlaps (Sachidanandam et al.
2001). Both approaches resulted in approximately equal numbers of
chromosomes being sampled for 5'UTRs, 3'UTRs, and coding regions and
showed a similar relation of diversity among these regions for
nondegenerate and fourfold-degenerate sites (data not shown) as the
entire data in Figure 2. Similar levels of diversity in these regions
have also been seen by others (Cargill et al. 1999; Halushka et al.
1999; Sunyaev et al. 2000).
|
WEB SITE REFERENCES
|
|---|
http://genome.dkfz-heidelberg.de; Husar Analysis Package.
http://repeatmasker.genome.washington.edu; RepeatMasker server home
page.
http://www.ncbi.nlm.nih.gov/NCBI Web site.
http://www.ncbi.nlm.nih.gov/SNP; dbSNP.
|
Acknowledgements
|
|---|
We thank C.D. Bustamante, J.C. Fay, M. Kohn, E.S. Lander, M.
Przeworski, S.E. Ptak, and C.I. Wu for discussion and critical comments
and T. Kitano for analysis support.
The publication costs
of this article were defrayed in part by payment of page charges. This
article must therefore be hereby marked "advertisement" in
accordance with 18 USC section 1734 solely to indicate this fact.
|
Footnotes
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3 Corresponding author. 
E-MAIL paabo{at}eva.mpg.de; FAX 49-341-9952-555.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.944903.
|
REFERENCES
|
|---|
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. 1990. Basic local alignment search tool. J. Mol. Biol. 215: 403-410.[CrossRef][Medline]
Altshuler, D., Pollara, V.J., Cowles, C.R., Van Etten, W.J., Baldwin, J., Linton, L., and Lander, E.S. 2000. An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature 407: 513-516.[CrossRef][Medline]
Burge, C. and Karlin, S. 1997. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268: 78-94.[CrossRef][Medline]
Bustamante, C.D., Nielsen, R., and Hartl, D.L. 2002. A maximum likelihood method for analyzing pseudogene evolution: Implications for silent site evolution in humans and rodents. Mol. Biol. Evol. 19: 110-117.[Abstract/Free Full Text]
Cargill, M., Altshuler, D., Ireland, J., Sklar, P., Ardlie, K., Patil, N., Shaw, N., Lane, C.R., Lim, E.P., Kalyanaraman, N., et al. 1999. Characterization of single-nucleotide polymorphisms in coding regions of human genes [published erratum appears in Nat. Genet. 1999 Nov.23(3):373]. Nat. Genet. 22: 231-238.[CrossRef][Medline]
Cartegni, L., Chew, S.L., and Krainer, A.R. 2002. Listening to silence and understanding nonsense: Exonic mutations that affect splicing. Nat. Rev. Genet. 3: 285-298.[CrossRef][Medline]
Chen, F.C. and Li, W.H. 2001. Genomic divergences between humans and other hominoids and the effective population size of the common ancestor of humans and chimpanzees. Am. J. Hum. Genet. 68: 444-456.[CrossRef][Medline]
Coward, E., Haas, S.A., and Vingron, M. 2002. SpliceNest: Visualizing gene structure and alternative splicing based on EST clusters. Trends Genet. 18: 53-55.[CrossRef]
Davuluri, R.V., Grosse, I., and Zhang, M.Q. 2001. Computational identification of promoters and first exons in the human genome. Nat. Genet. 29: 412-417.[CrossRef][Medline]
Duret, L. and Mouchiroud, D. 2000. Determinants of substitution rates in mammalian genes: Expression pattern affects selection intensity but not mutation rate. Mol. Biol. Evol. 17: 68-74.[Abstract/Free Full Text]
Duret, L., Dorkeld, F., and Gautier, C. 1993. Strong conservation of non-coding sequences during vertebrates evolution: Potential involvement in post-transcriptional regulation of gene expression. Nucleic Acids Res. 21: 2315-2322.[Abstract/Free Full Text]
Ebersberger, I., Metzler, D., Schwarz, C., and Paabo, S. 2002. Genome-wide comparison of DNA sequences between humans and chimpanzees. Am. J. Hum. Genet. 70: 1490-1497.[CrossRef][Medline]
Enard, W., Khaitovich, P., Klose, J., Zollner, S., Heissig, F., Giavalisco, P., Nieselt-Struwe, K., Muchmore, E., Varki, A., Ravid, R., et al. 2002. Intra- and interspecific variation in primate gene expression patterns. Science 296: 340-343.[Abstract/Free Full Text]
Ewing, B., Hillier, L., Wendl, M.C., and Green, P. 1998. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8: 175-185.[Abstract/Free Full Text]
Eyre-Walker, A. and Bulmer, M. 1993. Reduced synonymous substitution rate at the start of enterobacterial genes. Nucleic Acids Res. 21: 4599-4603.[Abstract/Free Full Text]
Eyre-Walker, A. and Keightley, P.D. 1999. High genomic deleterious mutation rates in hominids. Nature 397: 344-347.[CrossRef][Medline]
Fay, J.C., Wyckoff, G.J., and Wu, C.I. 2001. Positive and negative selection on the human genome. Genetics 158: 1227-1234.[Abstract/Free Full Text]
Fujiyama, A., Watanabe, H., Toyoda, A., Taylor, T.D., Itoh, T., Tsai, S.F., Park, H.S., Yaspo, M.L., Lehrach, H., Chen, Z., et al. 2002. Construction and analysis of a human–chimpanzee comparative clone map. Science 295: 131-134.[Abstract/Free Full Text]
Gardiner-Garden, M. and Frommer, M. 1987. CpG islands in vertebrate genomes. J. Mol. Biol. 196: 261-282.[CrossRef][Medline]
Halushka, M.K., Fan, J.B., Bentley, K., Hsie, L., Shen, N., Weder, A., Cooper, R., Lipshutz, R., and Chakravarti, A. 1999. Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat. Genet. 22: 239-247.[CrossRef][Medline]
Jones, P.A. and Takai, D. 2001. The role of DNA methylation in mammalian epigenetics. Science 293: 1068-1070.[Abstract/Free Full Text]
Kimura, M., 1983. The neutral theory of molecular evolution. Cambridge University Press, Cambridge, UK.
Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860-921.[CrossRef][Medline]
Makalowski, W. and Boguski, M.S. 1998. Evolutionary parameters of the transcribed mammalian genome: An analysis of 2,820 orthologous rodent and human sequences. Proc. Natl. Acad. Sci. 95: 9407-9412.[Abstract/Free Full Text]
Nachman, M.W. and Crowell, S.L. 2000. Estimate of the mutation rate per nucleotide in humans. Genetics 156: 297-304.[Abstract/Free Full Text]
Nei, M. and Kumar, S., 2000. Molecular evolution and phylogenetics. Oxford University Press, New York, NY.
Ohta, T. 1976. Role of very slightly deleterious mutations in molecular evolution and polymorphism. Theor. Popul. Biol. 10: 254-275.[CrossRef][Medline]
___, 1995. Synonymous and nonsynonymous substitutions in mammalian genes and the nearly neutral theory. J. Mol. Evol. 40: 56-63.[CrossRef][Medline]
Pesole, G., Liuni, S., Grillo, G., and Saccone, C. 1997. Structural and compositional features of untranslated regions of eukaryotic mRNAs. Gene 205: 95-102.[CrossRef][Medline]
Roest Crollius, H., Jaillon, O., Bernot, A., Dasilva, C., Bouneau, L., Fischer, C., Fizames, C., Wincker, P., Brottier, P., Quetier, F., et al. 2000. Estimate of human gene number provided by genome-wide analysis using Tetraodon nigroviridis DNA sequence. Nat. Genet. 25: 235-238.[CrossRef][Medline]
Rubin, G.M. 2001. The draft sequences. Comparing species. Nature 409: 820-821.[CrossRef][Medline]
Sachidanandam, R., Weissman, D., Schmidt, S.C., Kakol, J.M., Stein, L.D., Marth, G., Sherry, S., Mullikin, J.C., Mortimore, B.J., Willey, D.L., et al. 2001. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409: 928-933.[CrossRef][Medline]
Shabalina, S.A., Ogurtsov, A.Y., Kondrashov, V.A., and Kondrashov, A.S. 2001. Selective constraint in intergenic regions of human and mouse genomes. Trends Genet. 17: 373-376.[CrossRef][Medline]
Sharp, P.M. and Li, W.H. 1986. Codon usage in regulatory genes in Escherichia coli does not reflect selection for ‘rare’ codons. Nucleic Acids Res. 14: 7737-7749.[Abstract/Free Full Text]
Shen, J.C., Rideout, W.M., III, and Jones, P.A. 1994. The rate of hydrolytic deamination of 5-methylcytosine in double-stranded DNA. Nucleic Acids Res. 22: 972-976.[Abstract/Free Full Text]
Sunyaev, S.R., Lathe, W.C., Ramensky, V.E., and Bork, P.E. 2000. SNP frequencies in human genes an excess of rare alleles and differing modes of selection. Trends Genet. 16: 335-337.[CrossRef][Medline]
Sutton, G.G., White, O., Adams, M.D., and Kerlavage, A.R. 1995. TIGR Assembler: A new tool for assembling large shotgun sequencing projects. Genome Sci. Technol. 1: 9-19.
Urrutia, A.O. and Hurst, L.D. 2001. Codon usage bias covaries with expression breadth and the rate of synonymous evolution in humans, but this is not evidence for selection. Genetics 159: 1191-1199.[Abstract/Free Full Text]
Waterston, R.H., Lindblad-Toh, K., Birney, E., Rogers, J., Abril, J.F., Agarwal, P., Agarwala, R., Ainscough, R., Alexandersson, M., An, P., et al. 2002. Initial sequencing and comparative analysis of the mouse genome. Nature 420: 520-562.[CrossRef][Medline]
Received October 30, 2002;
accepted in revised format February 26, 2003.
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ABSTRACT
RESULTS
w) (B). Panels A and
B are scaled such that changes at 4d sites have equal heights.
Panel C shows the differences between the species normalized
to the genome-wide divergence between humans and chimpanzees
(Ebersberger et al. 2002