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Genome Res. 13:1638-1645, 2003 ©2003 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/03 $5.00 Letter Divergence in the Spatial Pattern of Gene Expression Between Human Duplicate GenesDepartment of Ecology and Evolution, University of Chicago, Chicago, Illinois 60637, USA
Microarray gene expression data provide a wealth of information for elucidating the mode and tempo of molecular evolution. In the present study,we analyze the spatial expression pattern of human duplicate gene pairs by using oligonucleotide microarray data,and study the relationship between coding sequence divergence and expression divergence. First,we find a strong positive correlation between the proportion of duplicate gene pairs with divergent expression (as presence or absence of expression in a tissue) and both synonymous (KS) and nonsynonymous divergence (KA). The divergence of gene expression between human duplicate genes is rapid, probably faster than that between yeast duplicates in terms of generations. Second,we compute the correlation coefficient (R) between the expression levels of duplicate genes in different tissues and find a significant negative correlation between R and KS. There is also a negative correlation between R and KA,when KA ≤ 0.2. These results indicate that protein sequence divergence and divergence of spatial expression pattern are initially coupled. Finally,we compare the functions of those duplicate genes that show rapid divergence in spatial expression pattern with the functions of those duplicate genes that show no or little divergence in spatial expression.
Ever since Ohno (1970
In the present study, we investigate the relationship between CDS
divergence and spatial expression divergence among human duplicate genes
(paralogs). To our knowledge, this is the first study that uses microarray
data to analyze the evolution of human gene expression on a genome-wide scale.
Specifically, we focus on the following questions: (1) how quickly do human
paralogs diverge in their expression; (2) does expression divergence increase
with gene sequence divergence, that is, evolutionary time; (3) what are the
functions of gene pairs with rapid divergence in expression; and (4) does the
present study of spatial expression of human paralogs support the conclusion
drawn from the study of temporal expression of yeast paralogs
(Gu et al. 2002b
Identification of Duplicate Genes Human U95A oligonucleotide array contains 12,387 probes. These include probes from 7565 human genes with annotated CDSs in GenBank. The other probes predominantly correspond to ESTs, which were not used in this study. Duplicate genes with annotated CDSs were identified and grouped into multigene families by using a rigorous method developed by Gu et al. (2002a  ; see Methods). From this
analysis, we estimated that the U95A array contains 875 multiple gene
families.
A total of 1404 independent duplicate gene pairs were selected for further
analysis. KS and KA divergences
between duplicate genes were calculated. The expression data for these gene
pairs studied in 25 independent and nonredundant tissues were retrieved from
Su et al. (2002
Proportion of Gene Pairs With Diverged Expression Increases With
Time First, we used KS as a proxy of divergence time. A high positive correlation (although not significant) is observed between the proportion of gene pairs with diverged expression and KS (Fig. 1A). This is true for the proportion of genes with diverged expression in at least one tissue and in at least two tissues. Strikingly, 73.3% of the gene pairs with an average KS of only 0.064 already have diverged in expression in at least one tissue, whereas 56.7% of these genes have diverged in expression in at least two tissues. These percentages increase to 90.0% and 73.3%, respectively, for gene pairs with an average KS of 1.2. Thus, rapid divergence in spatial expression pattern is observed between duplicate genes. The relationship between divergence time (measured by KS) and the proportion of gene pairs with diverged expression is approximately linear.
A statistically significant positive correlation is observed between KA and the proportion of gene pairs with diverged expression in either at least one or in at least two tissues (Fig. 1B). However, the correlation coefficient is smaller than the one observed when KS is used because KS is a better proxy of evolutionary time (see Discussion). Again, divergence in gene expression occurs very rapidly. Indeed, at an average KA of 0.044, 78.3% of gene pairs have diverged in expression in at least one tissue, and 60% of them have diverged in expression in at least two tissues. The proportion of genes with diverged expression increases rapidly and reaches a plateau at KA = 0.2. At an average KA of 0.212, almost all gene pairs (98.3%) have diverged in expression in at least one tissue, and 88.3% of gene pairs have diverged in expression in at least two tissues. Thus, even when we used KA as a proxy of evolutionary time, we observed rapid divergence in gene expression among duplicate genes and a significant correlation between KA and the proportion of gene pairs with diverged expression.
Correlation Between CDS Divergence and Expression Divergence A significant negative correlation was found between ln[(1 + R)/(1 - R)] and KS for genes in group A (R = -0.65, P < 0.0004; Fig. 2A) and in group B (R = -0.34, P < 0.0012; Fig. 2B). To test whether the transformation changed our conclusion, we also carried out the linear regression between KS and R (data not shown). This again resulted in a significant negative correlation for both group A (R = -0.63, P < 0.0005) and group B (R = -0.31, P < 0.0164). Thus, the correlation coefficient of gene expression between duplicate genes decreases approximately linearly with divergence time as measured by KS.
A weak negative correlation (data not shown) was observed between KA (KA < 0.70) and ln[(1 + R)/(1 - R)] for group A (R = -0.26, P < 0.0001) and group B (R = -0.19, P < 0.0001). However, this correlation becomes stronger for both groups (R = -0.42, P < 0.0006 for group A and R = -0.38, P < 0.0001 for group B) when only gene pairs with KA < 0.2 are examined (Fig. 3A,B). With KA > 0.2 (Fig. 3C,D), the correlation is considerably weaker and no longer statistically significant (R = -0.15, P < 0.0643 for group A and R = -0.05, P < 0.21). The choice of KA < 0.2 as a dividing point is arbitrary; however, the correlation coefficient changes only slightly from R = -0.41 (R = -0.36 for group B) for KA < 0.15 to R = -0.36 (R = -0.37 for group B) for KA < 0.25. Therefore, initially there is a coupling between gene expression divergence and KA.
Functions of Gene Pairs With Rapid Divergence or No Divergence in
Expression
It is also interesting to look into the function of duplicate genes that show no or little expression divergence, even though they duplicated a long time ago. Thus, we investigated gene pairs with KS > 3 and with no divergence in tissue expression (a total of 33 gene pairs; Table 2). Interestingly, two thirds of these gene pairs are almost ubiquitously expressed (expressed in 24 to 25 out of the 25 tissues analyzed), and another 15% are expressed in one tissue only (i.e., tissue-specific). Then we added gene pairs with R > 0.8 and KS > 3 (a total of six gene pairs). Only one gene pair is shared between the two groups. The gene pairs that have been well conserved in expression are enzymes (transferases, hydrolases, and helicases), transcription factors, membrane-bound proteins (e.g., adducins and connexins), structural proteins (keratin and tubulin), and proteasome components (Table 2). However, as the number of the proteins in each functional class is small, none of these classes is found to be significantly overrepresented among the gene pairs with slow divergence in expression, when they are analyzed using the Gene Ontology database.
We found that a large proportion of human duplicate genes have diverged rapidly in their spatial expression. Assuming that the average synonymous rate in higher primates is 1.5 x 10-9 nucleotide substitutions per site per year (Yi et al. 2002 ), 75.5% of human paralogs diverge in their expression in at
least one tissue after only 25 Myr (KS = 0.068). It is
likely that the true proportion of gene pairs with diverged gene expression is
even higher than shown here, because only 25 tissues were analyzed and only a
single (presumably normal) physiological condition was studied. In addition,
the classification of tissues used by Su et al.
(2002) does not correspond to
the histological classification. For example, such complex organs as pancreas
are called tissues in Su et al.
(2002), whereas in reality
they are composed of multiple tissues. These organs by themselves are likely
to exhibit a wealth of differential spatial gene expression. We estimate that
the rate of expression divergence in human paralogs is  40 times slower
than that of yeast paralogs (Gu et al.
2002b), if the absolute time of divergence is considered. However,
the generation time is several orders of magnitude shorter in yeast than in
humans. Thus, when calculated per generation, expression divergence is more
rapid in humans than in yeast. This might be due to a more complex
transcription regulation in mammals than in lower eukaryotes
(Huang et al. 1999). It could
also be because of more possibilities in which such divergence can be
manifested; for example, gene expression is regulated in a larger number of
tissues in humans than in yeast. Alternatively (or additionally), this could
be intrinsic to the spatial pattern of gene expression. A study of temporal
gene expression divergence in humans should distinguish between these two
possibilities.
Expression divergence increases approximately linearly with
KS and, thus, with the evolutionary time. Therefore,
similar to that in yeast duplicates (Gu et
al. 2002b Note that the correlation coefficient (R) was calculated over many tissues (tissues in which at least one of the duplicates is expressed). Such pooling of data will include genes that are not relevant to the experiment under consideration. Such genes may show similar expression patterns, and thus, their inclusion would tend to increase the correlation of expression and underestimate the divergence in expression. Initially, R and KA are coupled (KA < 0.2). After KA becomes >0.2, R is not correlated with KA. Note that at KA = 0.2, almost all duplicates have already diverged in their expression in at least one tissue.
In this study, KS and KA, but not
protein sequence divergence (d), were used as proxies of time since
gene duplication. KS is a more appropriate proxy of
divergence time compared with the other two measures because
KS varies substantially less among genes than does
KA or d (Li
1997
The expression data obtained by the hybridization of RNA to the
oligonucleotide arrays are supposed to be more accurate than is cDNA
microarray data (Wodicka et al.
1997 It is important to note that cross-hybridization tends to underestimate the degree of expression divergence. Therefore, the presence of cross-hybridization should reinforce rather than contradict our conclusion of rapid expression divergence between duplicate genes. Nevertheless, to test the ability of the Affymetrix arrays to discriminate between the paralogs under study, we performed two tests. First, we compared the probe sequences between two genes for each duplicate pair. (Each gene was represented by 16 oligonucleotide probes; each probe was 25 nucleotides long.) From the original 1404 independent gene pairs selected, only seven had one or more probes (two to seven probes in each case) with identical (or reverse complement) sequences. Additional four gene pairs had probes with one nucleotide mismatch (one to five probes in each case). Thus, it seems that cross-hybridization between duplicate genes was not a serious problem and did not significantly affect our results. Second, we considered duplicate pairs that were expressed in multiple tissues and that showed differing expression in at least one tissue. These were the cases in which the probes were apparently able to discriminate between the duplicates to some degree at least. Here the genes were considered diverged in expression in a tissue if their expression values differed as average difference (AD) > 200 (see Methods). Most duplicate gene pairs satisfy this criterion. We tested for a relationship between expression and sequence divergence in the remaining tissues for these genes. The results (data not shown) did not significantly differ from the original results, ensuring that the correlation is real. Our investigation of gene pairs that have rapidly diverged in their expression indicates that typically spatial expression pattern alters both in terms of presence or absence in particular tissues and in terms of the absolute amounts of mRNA transcripts (Table 1). An interesting observation regarding gene pairs that show no divergence in their expression over extensive evolutionary time is that they are usually either ubiquitously expressed or tissue-specific (Table 2). For these gene duplicates, both copies are preserved in the genome without a change in their spatial expression and are most likely maintained by purifying selection. We speculate that in such cases, it is advantageous to have a higher dosage of the gene transcript in the cell.
We found a large number of proteins involved in the defense system of an
organism among the duplicate pairs with rapid divergence in spatial
expression. This is in agreement with a strong selective pressure for
adaptation in such proteins (Hughes and Nei
1988 It is worth noting that only a subset of human duplicate genes has been included in this study. These included largely the well-characterized genes that had been discovered before the completion of the Human Genome Project. This could have introduced a bias, for instance, toward inclusion of duplicates that have differing functions and that, therefore, may be more likely to have differing expression patterns than randomly selected duplicate gene pairs would. This study examines divergence in one of the phenotypic manifestations of duplicate genes, namely, divergence in the pattern of spatial expression. It would be of great interest to investigate the molecular basis of such divergence, that is, divergence in the regulatory regions of gene expression.
Identification of Duplicate Genes The GenBank accession numbers for the sequences of the U95A array (Affymetrix) were downloaded from the Affymetrix Web site (http://www.affymetrix.com ).
The corresponding nucleotide sequences were retrieved by using Batch Entrez.
Then, GenBank entries were parsed, and only the entries with the annotated CDS
(CDS tag) were used in a subsequent analysis.
To identify duplicate gene pairs, we followed the method of Gu et al.
(2002a
The yn00 module (Yang and Nielsen
2000
All gene pairs were aligned using CLUSTALW
(Thompson et al. 1994
Expression Data Analysis For studying the relationship between KS (or KA) and the correlation coefficient of gene expression, we analyzed only the gene pairs in which either both (group A; Suppl. Table 2) or at least one (group B; Suppl. Table 3) of the genes was expressed in at least five tissues (AD > 200), and only these tissues were considered. The AD values were log2-transformed. The Pearson correlation coefficient R was transformed into ln[(1+R)/(1 - R)] to make the scale more appropriate for the linear regression analysis. The linear regression was carried out between each pair of KS (or KA) and the transformed R.
We are grateful to Z. Gu, H. Kaessmann, and T. Oakley for comments on the earlier versions of the manuscript; to the reviewers for many excellent comments improving our manuscript; and to A. Nekrutenko for help in revising this manuscript. This study was supported by NIH grants. 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.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.1133803.
1 Present address: Department of Biology, Penn State University, 208 Mueller
Lab, University Park, Pennsylvania 16802, USA.
2 Corresponding author. [Supplemental material is available online at www.genome.org.]
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Received December 23, 2003;
accepted in revised format April 25, 2003.
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ABSTRACT
RESULTS
