School of Biological Sciences, Washington State University
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Abstract |
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Introduction |
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In the past few years, the Ecdysozoa and Lophotrochozoa hypotheses have been accepted by many molecular systematists, anatomists, developmental biologists, and paleontologists (Schmidt-Rhaesa et al. 1998
; Adoutte et al. 2000
; Conway Morris 2000
; Giribet et al. 2000
; Peterson, Cameron, and Davidson 2000
; Peterson and Eernisse 2001
). Although challenged by a few researchers (Foster and Hickey 1999
; Wagele et al. 1999
; Hausdorf 2000
), these hypotheses have accumulated support from other gene sequences, namely from Hox genes (de Rosa et al. 1999
), ß-thymosin (Manuel et al. 2000
), and from expanded sets of SSU rRNA sequences (Garey and Schmidt-Rhaesa 1998
; Giribet et al. 2000
; Peterson and Eernisse 2001
).
Despite the impact the Ecdysozoa and Lophotrochozoa hypotheses have had, they were founded on incomplete rRNA information. That is, the SSU gene is just the smaller part of a gene family that also contains the large-subunit rRNA genes (LSU: 28S, 5.8S, and the little used 5S genes), which also are valuable for deep-level phylogenetic analysis (Hillis and Dixon 1991
). Given these facts, LSU rRNA genes should be a powerful tool with which to test the Ecdysozoa and Lophotrochozoa hypothesesfor if the larger rRNA genes fail to support them, these hypotheses will be severely compromised. In this study, we sequenced the LSU rRNA genes from 15 distantly related taxa of protostomes and used the combined LSU and SSU sequences to test the Ecdysozoa and Lophotrochozoa hypotheses. Furthermore, because complete LSU sequences are almost unknown for protostomes (De Rijk et al. 1995
), our new sequences should provide a foundation for other complete rRNA gene studies in the future.
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Materials and Methods |
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Specimens and Sequences
Information on the sources of the animals, voucher specimen numbers, precise parts of the genes used, the new GenBank accession numbers (AF342785AF342805), and sequences taken from the literature, are all available as supplementary material at the Molecular Biology and Evolution website (http://www.smbe.org). The 15 protostome species used were Amphiporus sp. (nemertean), Chordodes morgani (nematomorph), Eisenia fetida (oligochaete), Halicryptus spinulosus (priapulan), Limulus polyphemus (arthropod), Peripatoides novaezealandiae (onychophoran), Phascolopsis gouldii (sipunculan), Phoronis ijimae (=vancouverensis) (phoronid), Placopecten magellanicus (mollusc), Proceraea cornuta (polychaete), Sagitta elegans (chaetognath), Stylochus zebra (platyhelminth), Terebratalia transversa (brachiopod), Trichinella spiralis (nematode), and Urechis caupo (echiuran), and the five deuterostome outgroups were Branchiostoma floridae (cephalochordate), Ciona intestinalis (tunicate), Florometra serratissima (echinoderm), Hydrolagus colliei (vertebrate), and Ptychodera flava (hemichordate). In general, we used taxa whose SSU rRNA genes are slowly evolving, as did Aguinaldo et al. (1997)
. However, not all their taxa were available to us, and only two of the protostome species were the same in both studies (T. transversa and T. spiralis). In using deuterostomes as the outgroup, we assume that deuterostomes are the sister group of all protostomes, an assumption supported by many recent studies (Balavoine and Adoutte 1998
; Giribet et al. 2000
; Peterson and Eernisse 2001
). Following the recommendation of Giribet et al. (2000)
, no diploblast outgroups were used because diploblast LSU and SSU sequences are very different from those of triploblasts (Medina et al. 2001
) and seem too dissimilar to assure accuracy of the triploblast tree. The five outgroup taxaBranchiostoma, Ciona, Florometra, Hydrolagus, and Ptychodera (taken from Winchell et al. 2002
)were chosen primarily to yield uniform sampling across the deuterostomes (one or two representatives per phylum) and secondarily as the least divergent and most complete sequences available. Previously sequenced LSU and SSU genes from the protostomes Caenorhabditis elegans, Drosophila melanogaster, and Aedes albopictus were not used because of high divergence (long branches: De Rijk et al. 1995
). By contrast, LSU from the chaetognath Sagitta was sequenced and used here, despite the likelihood of high divergence because taxonomists have requested more gene sequences from this enigmatic phylum (Garey and Schmidt-Rhaesa 1998
; Giribet et al. 2000
). To assure that the divergent Sagitta would not disrupt the phylogenetic analyses, such analyses were performed twice, first without Sagitta and then with Sagitta included.
DNA extraction, primers, PCR amplification procedures, purification, sequencing, and fragment assembly were as described by Mallatt and Sullivan (1998)
and Winchell et al. (2002)
. The gene sequences were imported into Seqlab, a Macintosh X Window application (Smith et al. 1994
). The concatenated 28S, 5.8S, and 18S rRNA genes were aligned by eye, with the alignment rigidly based on secondary structure using the LSU rRNA model of Xenopus laevis (Schnare et al. 1996
) and the SSU models of X. laevis and Strongylocentrotus purpuratus (Gutell 1994
). Our alignments are available from EMBL (ftp://ftp.ebi.ac.uk/pub/databases/embl/align) under the accession numbers ALIGN_000087 and ALIGN_000088.
As in our past studies of deep-level phylogeny, the entire conserved core of the 28S gene was readily alignable across taxa and was used; the divergent domains, comprising over one-third of the 28S gene, were excluded (see table 7.1 in Mallatt, Sullivan, and Winchell 2001
; Hassouna, Michot, and Bachellerie 1984
). In the SSU genes, about 15% of the sites could not be aligned across taxa and were excluded. Overall, we used 2,348, 149, and 1,517 aligned sites from the 28S, 5.8S, and 18S genes, respectively, for a total of about 4,000 sites in the analysis.
Phylogenetic Analyses
Three data sets were analyzed: (1) combined LSU + SSU genes; (2) SSU only; and (3) LSU only. We combined the genes because the incongruence length difference test yielded a value of P < 0.27, thereby suggesting that the LSU and SSU sequences were not significantly more incongruent than random partitions (but see Sullivan [1996]
for a criticism of that test as an arbiter of data combination).
The search strategies for inferring optimal phylogenetic trees included equally weighted maximum parsimony (MP), maximum likelihood (ML), and minimum evolution (ME) using LogDet-Paralinear distances (Lake 1994
; Lockhart et al. 1994
; Swofford et al. 1996
). We executed each of these optimality criteria with PAUP* version 4.0 beta 4a (Swofford 2000
). To obtain the best tree using ML, we followed an iterative search strategy (Swofford et al. 1996
; Mallatt and Sullivan 1998
), in which the GTR + I +
model was found to fit the data best.
Of the three tree-building methods used, ME analysis with LogDet distances is emphasized over ML and MP because it is designed to give the best results when the gene sequences differ in base composition across taxaas our sequences do (see Results). This LogDet method only crudely accounts for heterogeneity in rates of evolution across nucleotide sites, although it has been shown to yield results similar to more complex models of rate heterogeneity (Waddell, Penny, and Moore 1997
; J. Sullivan, personal communication). The way it accounts for rate heterogeneity is by using a value called Pinv, the proportion of invariable sites in the genes. Because Pinv cannot be measured directly, we used the ML method of calculating this parameter, even though ML slightly overestimates Pinv (see Winchell et al. 2002
); if the ML-derived value for Pinv led to many trees with undefined values in 1,000 ME bootstrap replicates, it was lowered slightly until it led to fewer than 10 such undefined trees. To determine how sensitive the ME results were to variation in estimates of Pinv, we also used the following as Pinv values; namely, the proportion of sites that were constant in our set of protostome and deuterostome sequences (Pc) and the arbitrarily decreasing range of Pinv values: 0.55, 0.45, 0.35, 0.25, and 0.15.
To measure nodal support for our ME, ML, and MP trees, nonparametric bootstrap analyses (Felsenstein 1985
) were performed with 1,000 replicates for the ME and MP searches and 100 replicates for the ML searches.
Spectral Analysis
Spectral analysis, a method that quantifies both support (S) and conflict (C) within gene sequences for groups of taxa (or splits), is an alternative to tree-based analyses (Lento et al. 1995
; Penny et al. 1999
). In this part of the study, the LogDet-transformed distances were analyzed by the Spectrum program (Charleston 1998
, http://taxonomy.zoology.gla.ac.uk/
mac/spectrum/spectrum), with Pinv set at the same ML-based value as in the ME-tree calculation. (Again, using Pinv is a crude way to account for heterogeneous rates of evolution across nucleotide sites.) This Spectrum program must be supplied with a threshold support value, which must be chosen according to several criteria: It must be low enough to yield many splits but not so many as to overload the display buffer and crash the program; also, this threshold value must yield standardized conflict values (see subsequently) in roughly the same size range as the support values so that both support and conflict are interpretable when plotted together on a histogram (as in fig. 4
). A threshold of 0.0003 substitutions per site was chosen because it fit these criteria best. Conflict values were standardized according to Lento et al. (1995)
.
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Results |
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Phylogenetic Trees
Figure 1
shows the trees from the basic set of 19 taxa (i.e., without Sagitta) as calculated from the combined LSU + SSU, the SSU-only, and the LSU-only gene sequences by the ME, ML, and MP methods. Brief examination of these trees reveals that SSU and LSU produce similar phylogenies, and that relative branch lengths are roughly similar in both the SSU and LSU trees. That is, taxa with rapidly evolving SSU sequences (longest branches) usually have rapidly evolving LSU sequences, and slowly evolving (short-branched) SSU sequences go with slowly evolving LSU sequences. This suggests that the SSU and LSU rRNA genes evolve by a similar set of rules.
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To check the validity of the ME bootstrap values in figure 1 , ME bootstrap analyses were rerun upon the combined LSU + SSU sequences over a wide range of Pinv values (table 2 ). As seen in this table, support varied with Pinv for only a few groups, and the high bootstrap values for the main groupsprotostomes, Lophotrochozoa, Ecdysozoaseemed unaffected by Pinv. At or just below the ML estimate of Pinv = 0.62, there is bootstrap support >60% for every group listed in the table.
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Spectral Analysis
Without Sagitta
The results reported in this section were derived from the 19 core taxa without Sagitta (although adding Sagitta did not change the following findings). Figure 4
is a Lento plot of support and conflict values for various splits calculated by spectral analysis of the LogDet distances from the combined LSU + SSU sequences. The precise support and conflict values are listed in column 1 of table 3
, as S1 and C1 values, respectively. Spectral analysis showed most support for the groups that were also supported by bootstrap analysis, although it should be noted that most splits containing ecdysozoans have high conflict values because of the several highly divergent sequences in this superphylum.
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To assess which of the gene componentsLSU or SSUcontributed more to the splits, these two components were analyzed separately (compare columns 2 and 3 in table 3 ). On the basis of the relative sizes of their support-versus-conflict indices, the SSU gene contributes more than LSU to the higher-order splits of Lophotrochozoa, protostomes, and Ecdysozoa (that is, the SSU index exceeds the LSU index for each of these groups). By contrast, LSU contributes more to the other, lower-order, taxonomic splits (F, IK, and M in the table) because the LSU index exceeds the SSU index for these splits. Recall this is the same pattern exhibited by bootstrap analysis (fig. 1AC ).
With Sagitta
When the sequence from the chaetognath Sagitta was included, spectral analysis of the combined LSU + SSU data favored a Peripatoides + Sagitta pairing. The support value for this pairing was greater (S = 11.4 x 10-3 cf. 2.6 x 10-3) and the conflict value less (C = 5.2 x 10-3 cf. 11.6 x 10-3) than for the best alternative split of Peripatoides + Limulus, yielding a relative support-versus-conflict index of 10.0.
The main findings of this study are summarized in figure 5 .
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Discussion |
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Subgroups of Taxa
Together, the LSU + SSU sequences seemed to resolve some subgroups within Ecdysozoa and Lophotrochozoa. Identifying such subgroups is a major goal of invertebrate taxonomy, toward which SSU alone has made only a small contribution, despite the availability of hundreds of SSU sequences from a wide range of taxa (Garey and Schmidt-Rhaesa 1998
; Adoutte et al. 2000
; Giribet et al. 2000
). For example, SSU-based phylogenies show extensive paraphyly of molluscs, annelids, and some other phyla within the Lophotrochozoa (Aguinaldo et al. 1997
; Halanych 1998
; Winnepenninckx, Van de Peer, and Backeljau 1998
; Giribet et al. 2000
). Given the small number of taxa used in our study and the fact that our bootstrap values were only in the 60% and 70% range, our proposal that LSU + SSU sequences can resolve subgroups within Ecdysozoa and Lophotrochozoa is made with caution. Nonetheless, these subgroups were also supported by our spectral analysis, and most of them have been proposed previously based on more extensive morphological and molecular evidence. To be specific, such subgroups include: (1) echiurans + polychaetes (evidenced by morphology and elongation factor 1
genes; McHugh 1997, 2000
), (2) a lophophorate + mollusc + annelid clade (from fossil evidence; Conway Morris and Peel 1995
), which is independent of flatworms (Garey and Schmidt-Rhaesa 1998
), and (3) priapulids as basal ecdysozoans (based on extensive and conserved SSU sequences; Peterson and Eernisse 2001
; Garey 2001
; but see Schmidt-Rhaesa et al. 1998
).
Despite these apparent successes, the sipunculan (Phascolopsis) sequence suggests a failure. Morphology places sipunculans with annelids and molluscs, and their mitochondrial genes relate them to annelids (Boore and Staton 2002
). Our data missed this relation, probably because sipunculan rRNA sequences are divergent (Winnepenninckx, Van de Peer, and Backeljau 1998
; Giribet et al. 2000
) and attracted artifactually to the long-branched flatworm sequence (fig. 1A
). This sipunculan example shows that adding LSU to SSU does not yield a perfect phylogenyand adding sequences from more protostomes will undoubtedly yield more anomalies and other examples of paraphyly. Even so, the value of combining LSU and SSU is demonstrated by the fact that our LSU + SSU data resolved known subgroups that our SSU data did not (compare fig. 1A and B
).
Our combined-gene analysis joins the lophophorate and mollusc sequences (Terebratalia, Phoronis, and Placopecten). This echoes an old view that allied brachiopods with molluscs, a view not held by modern experts (Hyman 1959
; p. 516), who either consider brachiopods and molluscs closer to some polychaete than to each other (Conway Morris 1998
, p. 188) or else consider lophophorates basal to all other lophotrochozoans, including molluscs (Valentine 1997
; Peterson and Eernisse 2001
). In one sense our evidence is questionable, in that it places the phoronid closer to the mollusc than to the brachiopod. This placement is unconvincing, considering that phoronids and brachiopods are similar animals united by a lophophore and similar coeloms (Brusca and Brusca 1990
, chapt. 21). In another sense, however, our evidence does support a lophophorate + mollusc clade, in that it places the sequences of both lophophorates closer to that of the mollusc than to any other protostome (fig. 5
). If the lophophorate + mollusc clade should prove to be real, then brachiopod and mollusc shells could be homologous, rather than just analogous as is now believed. Shell structure and pores, shell muscles, and shell formation from a mantle are similar in both phyla, although these shells have different relations to the body axis (Barnes 1980
, p. 914; Pechenik 1985
, p. 378; Meglitsch and Schram 1991
, pp. 212, 514; Nielsen 1995
, p. 352; Reindl, Salvenmoser, and Haszprunar 1995
).
Results Explained
Despite our modest taxon sampling, there are three reasons why our results should contain true signal and not be the result of sampling error. First, there seems to be a real benefit in adding LSU sequences to the traditionally used SSU sequences. The evidence for this, as mentioned previously, is that LSU added resolution within the Lophotrochozoa and Ecdysozoa, whereas no such resolution existed in the SSU tree (compare fig. 1B with A
). This is the "adding more characters" approach to increasing phylogenetic resolution (Mitchell, Mitter, and Regier 2000
), and its apparent success in this study suggests that the characters in the LSU genes are indeed valuable additions.
The second reason for the resolution attained must be our use of the LogDet-based distance method because the ML and MP methods did not identify the subgroups of Ecdysozoa and Lophotrochozoa that ME did (compare the ME, ML, and MP bootstrap values in fig. 1A
). The effectiveness of this LogDet method for retrieving well-known clades from rRNA data has been noted before (Aguinaldo et al. 1997
; Mallatt, Sullivan, and Winchell 2001
; Garey 2001
). Its effectiveness may stem not only from the fact that this method performs best when nucleotide frequencies are nonstationary across taxa but also because all 12 types of nucleotide substitutions are free to occur at different rates, unlike in the ML or MP methods (Swofford et al. 1996
, pp. 459461). Spectral analysis of the LogDet distances also yielded high taxonomic resolution. Spectral analysis has a theoretical advantage over tree-building methods because in the tree-building process support values from characters that favor contradictory trees cancel one another so that information is lost, whereas in spectral analysis all these values are retained to produce total support values; that is, in spectral analysis, no support signal is lost. As mentioned previously, another advantage of spectral analysis is that it allows conflict values to be taken into account, whereas tree-building methods do not.
The third reason for the apparent resolving power of this study is that most of the taxa used had slowly evolving sequences. There is much debate in molecular phylogenetics over whether the best way to resolve difficult phylogenies is to use just the most slowly evolving sequences (the approach of Aguinaldo et al. 1997
and Garey and Schmidt-Rhaesa 1998
) or to add more and more sequences to split up the long branches (the dominant approach used with SSU gene sequences; Eernisse 1997
; Giribet et al. 2000
). We favor the former approach because we believe that long-branch attraction (Felsenstein 1978
) remains a major confounding problem in SSU-based studies, that using slowly evolving sequences minimizes this problem, and that the currently dominant approach of adding taxa for the sake of thoroughness has not improved resolution much, perhaps because new long branches have been added as often as the older branches were broken up. Even so, our philosophy is to avoid using the most divergent sequences rather than to use only the most conserved ones, and we certainly believe that many more taxa than the 15 used here are needed to resolve the relationships within Ecdysozoa and Lophotrochozoa. This idea that the judicious omission and addition of taxa improves phylogenetic estimation was championed, in more depth, by Kim (1998)
.
Position of Chaetognaths
The phylogenetic position of chaetognath worms is a longstanding problem. Their anatomical characters are confusing (Bone, Kapp, and Pierrot-Bults 1991
, p. 2), and their rRNA sequencesthe only molecular information available from this phylumare highly divergent. The SSU sequences of chaetognaths have always grouped with other rapidly evolving protostome sequencesrhabditid nematodes, gastrotrichs, gnathostomulids, and recently, with onychophorans (Halanych 1996, 1998
; Eernisse 1997
; Littlewood et al. 1998
; Zrzavy et al. 1998
; Giribet et al. 2000
)with a lack of consistency that suggests artifactual long-branch attraction. Both our phylogenetic tree (fig. 2
) and spectral analysis placed Sagitta with Peripatoides, which we suspect is another artifact of long-branch attraction, here between two highly divergent and CG-rich sequences (note that even LogDet cannot correct for the most extreme cases of nucleotide nonstationarity; Foster and Hickey 1999
). As evidence supporting this suspicion, when we omitted the Peripatoides sequence and reran the LSU + SSU bootstrap analysis, Sagitta no longer joined the Ecdysozoa but appeared as an independent protostome lineage (in ME and MP trees: not shown). Thus, the position of chaetognaths is left unresolved in our summary figure 5
.
Testing Some Other Hypotheses
The Ecdysozoa and Lophotrochozoa hypotheses contradict several classical, morphology-based hypotheses of triploblast relationships: that annelids and arthropods are interrelated as Articulata (based on similarities in body segmentation; Schmidt-Rhaesa et al. 1998
) and that the flatworm line, then the nematode line, branched from the base of the triploblast tree before the appearance of deuterostomes and protostomes (Barnes 1980
, chapt. 3). In parts 13 of table 4
, these three classical hypotheses are tested by spectral analysis of our combined LSU + SSU-gene data. None of these hypotheses has even one-twentieth as much support as the Ecdysozoa and Lophotrochozoa hypotheses that oppose it. Further evidence against these classical hypotheses has been provided by many recent molecular and anatomical studies (Ruppert 1991
; Eernisse, Albert, and Anderson 1992
; Balavoine 1998
; Boore and Brown 2000
; Peterson and Eernisse 2001
).
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The second hypothesis about deuterostomes is that they are related to lophophorates (brachiopods, phoronids, bryozoans). Lophophorates have been classified as deuterostomes based on their development and the fact that their lophophore resembles a similar feeding structure in pterobranch hemichordates (Hyman 1959
; Brusca and Brusca 1990
, p. 797). However, molecular evidence from many genes now indicates that lophophorates are protostomes (Halanych et al. 1995
; de Rosa et al. 1999
; Saito, Kojima, and Endo 2000
). Furthermore, as seen in part 5 of table 4
, spectral analysis gives the lophophorate + deuterostome hypothesis negative support.
Relative Value of the LSU Versus the SSU Gene in Molecular Phylogeny
Over 60 LSU rRNA sequences are now known from animals (listed in Mallatt and Sullivan 1998
; Medina et al. 2001
; Winchell et al. 2002
). On the basis of our experience with these sequences, it is possible to compare the relative strengths and weaknesses of the LSU and SSU genes for phylogenetic analysis. Overall, the LSU gene seems to contain less phylogenetic signal, at least for resolving the deepest branches of the animal tree (compare fig. 1C to B;
also see Winchell et al. 2002
). This means that LSU should not be used alone for deep-level phylogeny but should always be combined with SSU, and indeed, in our experience, the combined LSU + SSU sequences always give better resolution than do SSU sequences alone. Although the LSU gene does add signal, it requires about three times more work to amplify and sequence than the SSU gene. However, the fact that the conserved core region of the 28S gene is both well defined and informative about deep-level animal taxonomy makes aligning LSU genes simpler and more objective than aligning SSU genes.
The divergent domains of the 28S gene, although not used in this higher-level phylogenetic analysis, can be included for lower-level analyses all the way down to the species or even subspecies level (Littlewood 1994
; Mallatt and Sullivan 1998
; Jarman et al. 2000
; Litvaitis et al. 2000
; Winchell 2001
). The SSU gene, by contrast, has few rapidly evolving regions and provides little resolution at lower taxonomic levels.
Finally, as already mentioned, a weakness of protostome and deuterostome LSU gene sequences is that they are susceptible to nonstationarity of base frequencies. By contrast, all the SSU data sets with which we have dealt were stationary.
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Conclusions |
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Although additional, independent genes must also be used, LSU rRNA gene sequences could help to free systematists who now use SSU from the frustrating cycle that many are experiencing, i.e., a cycle of adding progressively more SSU sequences without much improvement in taxonomic resolution. Widespread taxonomic sampling of protostome LSU sequences should begin, and if the findings of the present study are true indicators, the new studies should emphasize slowly evolving over rapidly evolving sequences and should include LogDet-based distance methods for maximum efficiency. We foresee a time when no rDNA-based phylogenetic study will be considered complete unless it includes both the LSU and SSU genes.
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Acknowledgements |
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Footnotes |
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Keywords: large-subunit ribosomal RNA
28S rRNA
small-subunit ribosomal RNA
phylogeny
protostome
Ecdysozoa
Address for correspondence and reprints: Jon Mallatt, Box 644236, School of Biological Sciences, Washington State University, Pullman, Washington 99164-4236. jmallatt{at}mail.wsu.edu
.
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