* Graduate School of Human and Environmental Studies
Department of Zoology, Graduate School of Science, Kyoto University, Kyoto, Japan
Department of Entomology, Comstock Hall, Cornell University, Ithaca, New York
Sapporo Science and Technology College, Sapporo, Japan
|| Primate Research Institute, Kyoto University, Inuyama, Japan
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Abstract |
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Key Words: arginine kinase elongation factor1 long-wavelength rhodopsin intron phylogeny bumble bee
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Introduction |
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Despite recent progress in analyzing gap characters, these have not been widely accepted as phylogenetic markers due, in part, to insufficient empirical study of the quality of gaps as characters. Some authors assume that gaps are less homoplastic and therefore more phylogenetically reliable than base substitutions, since gaps generally occur less frequently (Lloyd and Calder 1991; Giribet and Wheeler 1999; van Dijk et al. 1999). However, others emphasize the potential for gaps to be misleading (Bapteste and Philippe 2002). Despite the need for further critical evaluation of gaps as phylogenetic characters, few studies have focused on investigating homoplasy levels of gaps as compared with base substitutions or assessing the contribution of gaps to phylogenetic resolution and nodal support (Graham et al. 2000; Simmons, Ochoterena, and Carr 2001; Bapteste and Philippe 2002). Additional empirical study of relative homoplasy levels among different types of gap characters and of the degree to which these contribute to phylogenetic reconstruction would facilitate the full and appropriate utilization of information potentially available in variable-length sequence data.
In this study, we tested the potential of gaps as phylogenetic characters by analyzing their occurrence upon a well-resolved, substitution-based tree and assessing their contribution to further resolution among 66 species (23 subgenera) of bumble bees (Bombus). Bombus is a diverse, monophyletic genus comprising nearly 250 species (38 subgenera) (Williams 1998) and is therefore an ideal group for studying patterns of indel evolution among species and subgenera. We analyzed exon and intron sequences of three nuclear genes: long-wavelength rhodopsin (LW Rh), arginine kinase (ArgK), and elongation factor-1 F2 copy (EF-1
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Materials and Methods |
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For initial alignment, we used ClustalX version 1.81 (Jeanmougin et al. 1998) with the default parameter settings. The alignments obtained were then corrected manually for obvious misalignments. Sequence alignment for the introns within LW Rh was trivial and required only three simple gaps, which were parsimony-uninformative. However, alignment for introns within ArgK and EF-1 required gaps of various lengths, which differed markedly in terms of structural characteristics; relatively long gaps that were easily aligned occurred throughout ArgK intron sequences (fig. 1A), whereas numerous shorter gaps were required at five particular regions within EF-1
intron sequences, resulting in multiple optimal alignments (fig. 1B). This difference in gap characteristics provided an excellent opportunity to study different evolutionary modes of length mutations occurring within the diverse genus Bombus.
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We investigated the influence of gap characters on phylogenetic inference by conducting an additional parsimony analysis with gap information included as coded characters. Alignable gaps of ArgK intron were coded as binary (presence/absence) characters using the method of Simmons and Ochoterena (2000) and added to the data matrix. We weighted base substitutions and indel mutations equally, because there was no a priori reason to differentially weight gap costs relative to base substitutions. For the ambiguously aligned regions of EF-1 intron (fig. 1B), we employed the program INAASE (Integration of Ambiguously Aligned Sequences) (Lutzoni et al. 2000) to accommodate these regions in parsimony analysis. Following the criteria detailed in Lutzoni et al. (2000), we delimited five ambiguously aligned regions within the EF-1
intron. However, coding one of these regions (region 4) resulted in more than 32 multistate characters, which exceeds the number that can be handled by PAUP*. We therefore divided the region into two subregions such that sequences within each region are likely homologous (region 4a and region 4b in fig. 1B). Several division schemes that we tried all yielded the same number of most-parsimonious trees of the same topology, indicating the validity of this approach.
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Results and Discussion |
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Analysis of the data matrix including the 16 characters coding ArgK gaps ("GAP" characters throughout) resulted in the same shortest trees (1354 steps: CI = 0.48; RI = 0.77; RC = 0.46) as did the data matrix including only base substitutions. Inclusion of GAP characters (16 of 411 informative characters) did not contribute to resolution, but bootstrap values for the nodes concerned increased from >53% to >72%, and the total branch support (Bremer 1994) increased from 323 to 335.
The six multistate characters coding ambiguous regions of EF-1 ("INAASE characters" throughout) mapped onto the substitution-based tree with a total of 187 steps and a CI of 0.77 (RI = 0.87; RC = 0.67). This indicates that the INAASE characters contain rich information consistent with the phylogenetic signal of base substitutions. Simultaneous analysis of an expanded data matrix, including the 16 GAP characters and the six INAASE characters resulted in four most-parsimonious trees of length 1535 (CI = 0.53; RI = 0.78; RC = 0.48) (supplementary online data) with lower homoplasy levels than the analysis based on substitutions and GAP characters. The six INAASE characters collectively had CI = 0.80, RI = 0.89, and RC = 0.71. Although the alignment ambiguous EF-1
intron regions accounted for only
5% of all sites in the original data matrix, INAASE characters added 181 steps to the tree (12% of the total steps) and contributed substantially to phylogenetic resolution, resulting in a drastic decrease in the number of shortest trees (from 891 to 4) and recovery of 62 of 64 possible nodes in the strict consensus tree (an addition of 9 nodes). INAASE characters also significantly improved nodal support, increasing average bootstrap values by 3.9 % (based on nodes in common on the strict consensus trees), and summed branch support by 55 (from 335 to 390).
Our analyses showed that gaps and ambiguously aligned regions of nuclear intron sequences contain useful phylogenetic signal concordant with that of base substitutions. Unambiguously aligned gaps exhibited minimal homoplasy and were consistently congruent with the substitution-based tree, whether derived from MP, NJ, or ML method. Thus, our results reinforce several earlier suggestions that gaps are phylogenetically reliable characters (Lloyd and Calder 1991; van Ham et al. 1994; Graham et al. 2000; Simmons, Ochoterena, and Carr 2001). For example, Lloyd and Calder (1991) found that all seven informative gaps longer than 1 bp within -globin pseudogene of primates could be assigned to specific branches of substitution-based tree without homoplasy. Similarly, van Ham et al. (1994) showed that 13 of 15 informative gaps longer than 1 bp could be mapped on substitution-based trnL-trnF intergenic spacer gene tree in Crassulaceae plants without homoplasy. However, in these studies single-nucleotide indels were often homoplastic or ambiguously aligned. Multinucleotide indels are probably more reliable than single-nucleotide indels because the former are less frequent than single-nucleotide indels in many data sets (Saitou and Ueda 1994; van Ham et al. 1994; Graham et al. 2000) and because homoplasies by parallel and back mutations can occur only when they match exactly in length and position (and sequence for insertions) with the corresponding indels (Lloyd and Calder 1991).
On the other hand, the results of several other studies contrast with those described above. For example, in Bapteste and Philippe's (2002) analysis of eukaryotic phylogeny, the observed pattern of amino acid indels within enolase and impdh genes likely resulted from recombination and lateral gene transfer as well as from convergence and reversal. In other cases, apparently identical indels seem to have originated independently in distantly related taxa (van Ham et al. 1994; Philippe and Laurent 1998; Graham et al. 2000). However, these results do not necessarily indicate that indels are generally of lesser utility than base substitutions, because homoplasy can be found in any class of molecular characters, especially when dealing with phylogeny on a large scale (e.g., across phyla). Considering these results together with ours, we conclude that indels can be highly reliable characters, especially at lower taxonomic levels, but recognize that gaps, like all classes of phylogenetic characters, are not devoid of homoplasy. It may therefore be inadvisable to identify higher monophyletic groups based solely on a single indel (or a few indels) (Bapteste and Philippe 2002).
Recent methodological progress in handling gap characters in phylogenetic analyses affords the opportunity to incorporate this useful phylogenetic information derived from sequence data. However, these new methods are confined to the parsimony optimality criterion, and a well-justified, statistically-robust, generally applicable, and widely accepted method for incorporating indels within explicitly model-based phylogenetic methods such as standard implementations of ML is still lacking. The development of explicit models of indel evolution should not be particularly difficult, but selection of parameters to be included and statistical interpretation of resulting analyses will be far from straightforward (see Farris 1999; Sanderson and Kim 2000). Even within the context of parsimony analysis, the difficulty of assuming gap-to-substitution costs or weighting gaps of different lengths has impeded extensive use of the methods. However, gaps are strong indicators of common descent, and full utilization of indel information to corroborate and refine phylogenies inferred primarily from substitution data would surely improve the accuracy and efficiency of phylogeny estimation for many data sets (e.g., Rokas and Holland 2000; Danforth 2002).
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Acknowledgements |
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Footnotes |
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