Department of Biological Sciences, Western Michigan University, Kalamazoo, Michigan
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Abstract |
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Key Words: Clarkia breweri evolution of floral scent gene duplication maximum likelihood positive selection
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Introduction |
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The importance of positive Darwinian selection as a process shaping the evolution of protein coding genes has been suggested by numerous recent studies (Zhang, Rosenberg, and Nei 1998; Zanotto et al. 1999; Bishop, Dean, and Mitchell-Olds 2000; Yang and Bielawski 2000 and references cited therein; Swanson et al. 2001). The study of selection on protein coding genes relies on estimates of the nonsynonymous/synonymous (dN/dS) rate ratio, . Cases in which
= 1 suggest the genes in question evolve neutrally, whereas purifying, or negative, selection is inferred when
< 1. Evidence for positive selection is obtained only in cases where
> 1 (Yang and Bielawski 2000). Recently, maximum likelihood methods have been developed for the detection of positive selection among lineages (Yang 1998), among sites within a gene (Nielsen and Yang 1998; Yang et al. 2000), or among sites within specific lineages of a phylogeny (Yang and Nielsen 2002). In addition to simply inferring whether positive selection has been an important force shaping protein evolution, a recently developed Bayesian method has been applied to identify the most likely sites under positive selection in cases where estimates of
> 1 (Nielsen and Yang 1998). In this paper, these recently developed methods are utilized to investigate the evolution of the floral scent gene, IEMT, in Clarkia breweri.
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Materials and Methods |
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Results and Discussion |
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The second test compared model M0 with M3 (discrete model), the latter of which was presented in Yang et al. (2000) to investigate whether selective pressure varies among sites. Model M3 (with three site classes) assumes that there is some proportion of sites belonging to each of three classes with different ratios (table 1). In this case, both the proportions and
ratios for three site classes were estimated from the data. The LRT statistic for this comparison was 2(diff. lnL) = 242.84 (P < 0.001, 4 df ). This significant result suggests that the
ratio is variable among sites in the COMT and IEMT genes, with
0 = 0.02 at 60% of the sites,
1 = 0.20 at 35% of the sites, and
2 = 0.68 at 5% of the sites. Although
appears to be heterogeneous for these sequences, this test provides no evidence for sites under positive selection in these genes, because in no case was
estimated to be greater than 1. However, because the same
ratios were estimated for all lineages, any difference in selective constraint in IEMT only could not be detected.
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Assuming the branch-sites model, PAML 3.1 was used to calculate the posterior probabilities that a particular site belongs to the site class that has experienced positive selection. As shown in figure 2A, 30 sites were assigned to the site class with = 1.93 (posterior probability > 0.8). Of these substitutions, 11 were in the region of the coding sequence that has been shown to be important for the different substrate specificities of COMT and IEMT based on chimeric COMT-IEMT protein constructs (region B in fig. 2A) (Wang and Pichersky 1998). Furthermore, within this region, all of the sites shown by site-directed mutagenesis to have major effects on substrate discrimination of IEMT had high posterior probabilities (fig. 2B) (Wang and Pichersky 1999). It should also be noted that two of the sites with the highest posterior probabilities (138 and 139) were shown to have some of the largest effects on substrate discrimination.
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If it is assumed that efficient biosynthesis of methyl eugenol and methyl isoeugenol is important for the fitness of Clarkia breweri, then the inference that multiple IEMT sites have evolved by positive selection is supported by the experimental studies on enzyme kinetics (Wang and Pichersky 1999). Unlike proteins that have experienced Darwinian selection for increased proportions of charge changing residues (e.g., arginine in eosinophil cationic protein [Zhang, Rosenberg, and Nei 1998]), it appears that IEMT has been selected for increased substrate specificity. Although the order in which mutations accumulated is unknown, experimental studies do suggest that the many of the sites shown to be under positive selection could have had synergistic effects on the ancestral IEMT substrate specificity. For example, Wang and Pichersky (1999) demonstrated that mutations of IEMT at positions 134 and 135 led to an enzyme with a 42% lower ability to discriminate substrates than wild-type (fig. 2B). Combined mutations of sites 134 and 135 and 168 and 169 reduced the substrate specificity by 95% compared with wild-type (fig. 2B). Finally, it was shown that mutations of 134 and 135, 168 and 169, and 137 and 139 resulted in an enzyme with almost no ability to catalyze the formation of methyl isoeugenol. If the selective agent were sensitive to varying levels of emissions, then the pattern of mutations in the active site of this enzyme is consistent with positive Darwinian selection for increased substrate specificity because the rate of volatile production is probably directly related to substrate preference. Further studies are needed to determine the role of IEMT because the volatiles it produces may attract pollinators, repel herbivores from the flowers, or have some other as of yet unknown function.
In a general context, the results of this study have implications for the study of genes involved in secondary metabolism. Much like IEMT, only a small number of amino acid changes were responsible for novel substrate specificities in another methyltransferase, eugenol-O-methyltransferase (EOMT), relative to its sister gene, chavicol-O-methyltransferase (Gang et al. 2002). If positive selection is localized to a few codons in one or a few lineages, the recently developed branch-sites model used here will likely become increasingly important in the study of adaptive evolution of genes involved in secondary metabolism.
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Acknowledgements |
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Footnotes |
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Geoffrey McFadden, Associate Editor
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