MBE Advance Access originally published online on March 14, 2006
Molecular Biology and Evolution 2006 23(6):1101-1103; doi:10.1093/molbev/msk002
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Letter |
Adaptive Protein Evolution and Regulatory Divergence in Drosophila
* Department of Ecology and Evolutionary Biology, University of Arizona; Department of Plant Sciences, University of Arizona; and Department of Computer Science, University of Arizona
E-mail: jgood{at}email.arizona.edu.
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Abstract |
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Two recent studies demonstrated a positive correlation between divergence in gene expression and protein sequence in Drosophila. This correlation could be driven by positive selection or variation in functional constraint. To distinguish between these alternatives, we compared patterns of molecular evolution for 1,862 genes with two previously reported estimates of expression divergence in Drosophila. We found a slight negative trend (nonsignificant) between positive selection on protein sequence and divergence in expression levels between Drosophila melanogaster and Drosophila simulans. Conversely, shifts in expression patterns during Drosophila development showed a positive association with adaptive protein evolution, though as before the relationship was weak and not significant. Overall, we found no strong evidence for an increase in the incidence of positive selection on protein-coding regions in genes with divergent expression in Drosophila, suggesting that the previously reported positive association between protein and regulatory divergence primarily reflects variation in functional constraint.
Key Words: gene expression • positive selection • protein evolution
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Introduction |
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TOPAbstractIntroductionMethodsSupplementary MaterialsAcknowledgementsReferences |
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Although changes in gene regulation and in protein structure have been conventionally treated as independent modes of evolution (King and Wilson 1975
), two lines of evidence suggest that protein sequence evolution might be coupled with divergence in gene expression in Drosophila. First, some of the classic examples of adaptive allozyme polymorphisms within species are associated with differences in gene expression (Laurie-Ahlberg 1985; Berry and Kreitman 1993; Odgers, Healy, and Oakeshott 1995). Second, there is a positive correlation between the rate of protein evolution (i.e., amino acid substitution, dN) and divergence in expression levels for male-biased genes surveyed in Drosophila melanogaster and Drosophila simulans (Nuzhdin et al. 2004; Lemos et al. 2005).
A positive correlation between rates of protein sequence evolution and expression divergence could be driven by positive selection on genes causing both protein structure and regulation to evolve rapidly (Nuzhdin et al. 2004). An alternative explanation for a coupling between regulatory and structural divergence is variation in purifying selection: genes with less functionally constrained protein sequence may also be less constrained in expression (Castillo-Davis, Hartl, and Achaz 2004; Lemos et al. 2005). To distinguish between these alternative hypotheses, we examined the extent to which divergence in gene expression is associated with positive selection on protein sequences in Drosophila. Using genomic data from five closely related species of Drosophila (D. melanogaster, D. simulans, Drosophila yakuba, Drosophila erecta, and Drosophila ananassae), we used a maximum likelihood framework to calculate rates of protein evolution for 1,862 genes and tested for evidence of positive selection (Yang et al. 2000). We compared these data with two different estimates of divergence in gene expression: (1) divergence in the level of expression between D. melanogaster and D. simulans (Ranz et al. 2003) and (2) divergence in the pattern of expression during metamorphosis in D. melanogaster, D. simulans, and D. yakuba (Rifkin, Kim, and White 2003).
We found that genes with divergent expression levels between D. melanogaster and D. simulans have rates of protein evolution (dN/dS) that are similar to genes with no expression divergence (table 1; all comparisons Wilcoxon P > 0.10). However, in all comparisons, genes with divergent expression levels do have higher median dN/dS levels than genes with similar levels of expression, consistent with previous reports of a weak positive correlation between expression divergence and protein evolution (Nuzhdin et al. 2004; Lemos et al. 2005). Interestingly, we found that female-biased and male-biased genes evolve at similar rates, in contrast to a previous report that male-biased genes show a fourfold increase in dN/dS (Zhang, Hambuch, and Parsch 2004).
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We tested for a positive association between divergence in gene expression level and positive selection on protein sequence and found a slightly higher incidence of positive selection (4.6% under selection) in genes with similar expression levels between D. melanogaster and D. simulans than in genes with divergent expression (3.2% under selection; fig. 1). A negative association was also observed when considering only male-biased genes (6.8% vs. 5.6%) and only female-biased genes (4.0% vs. 3.6%). None of these differences was significant using a Fisher's Exact test; however, in each case, the trend was in the opposite direction of what would be expected if adaptive protein evolution drives the positive relationship between the rate of protein evolution and expression divergence.
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We also examined expression divergence during metamorphosis in four D. melanogaster lines, one D. simulans, and one D. yakuba (Rifkin, Kim, and White 2003
). Their experiment identified genes with divergent expression patterns by comparing the ratio of gene expression at two developmental time points among lineages. Although a more complex metric for measuring expression divergence, their approach avoids hybridization artifacts due to interspecific divergence (Gilad et al. 2005) because expression patterns are based on within-lineage comparisons. As with expression level, genes with divergent expression patterns among species during metamorphosis have similar rates of evolution as genes with no expression divergence among species (table 1; Wilcoxon P > 0.10). In contrast to what we detected when comparing divergence in expression level, we found a slight positive association between expression divergence patterns during metamorphosis and incidence of positive selection on protein-coding regions (6.0% vs. 4.6% under positive selection). But again, this difference was not significant (Fisher's Exact test, two-tailed P = 0.224).
The lack of a strong relationship between structural and regulatory evolution in our study may reflect noise introduced by combining neutral and adaptive expression divergence. For example, it is possible that positive selection for expression divergence could be most frequent in genes with less functionally constrained protein sequence. Such a scenario could generate a positive correlation between expression and protein sequence divergence but is difficult to evaluate given the current data. The analysis by Rifkin, Kim, and White (2003 and updates) of expression patterns during metamorphosis represents one of the first attempts to classify evolutionary modes of regulatory divergence using genome-level data (see also Khaitovich, Pääbo, and Weiss 2005) but is not a formal test of positive selection on expression. We combined genes identified to be neutrally evolving with those identified as undergoing lineage-specific selection to form our set of genes with divergent expression during metamorphosis (fig. 1). However, we found no difference in the rate of protein sequence evolution (Wilcoxon P > 0.10) or the frequency of positive selection on protein-coding regions between genes whose expression patterns were classified by Rifkin, Kim, and White (2003) as evolving due to lineage-specific selection and genes for which neutrality could not be rejected (5.9% vs. 6.3%, fig. S1, see Supplementary Material online).
Overall, these data suggest that positive selection on protein sequence is not strongly coupled with divergence in gene expression in Drosophila (fig. 1). Thus, the correlation between dN and expression divergence previously documented by Nuzhdin et al. (2004) and Lemos et al. (2005) is more likely due to differences in purifying selection than in adaptive evolution on both protein structure and gene regulation.
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Methods |
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All predicted protein and nucleotide sequences for D. melanogaster were downloaded from the Ensembl database (http://www.ensembl.org/index.html). Orthologous sequences from D. simulans, D. yakuba, D. erecta, and D. ananassae were identified from the Eisen Group Annotations Release 1.0 (February 18, 2005; Venky Iyer, Daniel Pollard, and Michael Eisen) based on a combination of direct pairwise comparisons with D. melanogaster and computational gene prediction. Orthologous amino acid sequences were aligned with ClustalW v1.8 using default parameters (Thompson, Higgins, and Gibson 1994
). We used the amino acid alignment for each gene as a guide to align corresponding nucleotide sequences with RevTrans v1.3 (Wernersson and Pedersen 2003).
All analyses on the rate of protein evolution among taxa and tests of positive selection were conducted using the codeml program in the PAML package v3.14 (Yang 1997). Pairwise estimates of the number of nonsynonymous substitutions per nonsynonymous site (dN) and the number of synonymous substitutions per synonymous site (dS) were calculated using maximum likelihood (Goldman and Yang 1994). To test for evidence of positive selection in each gene, we fit sequence data from D. melanogaster, D. simulans, D. yakuba, D. erecta, and D. ananassae to two models allowing evolutionary rates (dN/dS) to vary across codon sites (models M7 and M8; Yang et al. 2000). M7 assumes that dN/dS values vary across codon sites but are functionally constrained between 0 and 1, while M8 allows for an additional category of sites under positive selection (dN/dS > 1). Genes were assumed to be under positive selection if the data fit M8 better with a likelihood ratio test (P < 0.01). We required the dN/dS ratio for positively selected sites in model M8 to be greater than 1.1 (
> 1.1) and the proportion of positively selected sites to exceed 1%. Less conservative criteria produced similar results.
We defined expression divergence according to previously published results. Divergent transcripts between D. melanogaster and D. simulans were defined as genes with nonoverlapping 95% confidence intervals in expression level between species estimated using a Bayesian approach (Ranz et al. 2003). Rifkin, Kim, and White (2003) surveyed gene expression in D. melanogaster (four lines), D. simulans, and D. yakuba at two developmental time points. In their experiment, expression divergence was classified into three evolutionary categories based on patterns of variation among the six lineages (see Rifkin, Kim, and White 2003 for details). Briefly, genes were assumed to be evolving due to stabilizing selection if they showed little or no variation among the six lineages (i.e., genes not divergent in expression in our study). Genes showing significant variation among lineages (i.e., genes divergent in expression) were classified as evolving due to lineage-specific selection if they showed insignificant variation within D. melanogaster or neutral if the patterns were consistent with a mutation-drift model. We used an updated version of these data available from the authors at http://genome.med.yale.edu/Comparative/. Estimates of protein evolution and summarized expression data for all 1,862 genes are given in table S1 (see Supplementary Material online).
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Supplementary Materials |
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Supplementary fig. S1 and table S1 are available at Molecular Biology and Evolution online (http://www.mbe.oxfordjournals.org/).
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Acknowledgements |
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We thank V. Iyer, D. Pollard, and M. Eisen for making their genome annotations publicly available. We are grateful to H. Ochman and the Integrative Graduate Education and Research Traineeship (IGERT) fellows for insightful discussion during the development of this project. The manuscript was improved by comments from M. Dean, H. Ochman, M. Nachman, and two anonymous reviewers. This research was conducted as part of the University of Arizona National Science Foundation IGERT grant Genomics Initiative (DGE-0114420).
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Footnotes |
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Marcy Uyenoyama, Associate Editor
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References |
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