Department of Genetics, North Carolina State University
Faculty of Mathematics
Faculty of Technology, University of Bielefeld, Bielefeld, Germany
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Abstract |
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Introduction |
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The evolutionary origin of serpins is unclear. Members of this superfamily have not been found in prokaryotes or even in yeast; however, they are present in viruses and many different groups of eukaryotes, including plants, nematodes, arthropods, and vertebrates. This distribution suggests that serpins may have arisen early in eukaryotic evolution, possibly by fusion of two ancestor polypeptides that ligated an N-terminal helix-rich domain to the carboxyl domain of present-day serpins, which consists primarily of ß-sheets (Wright 1993
).
Several aspects of the serpins suggest that they might provide an excellent vehicle for studying important questions about protein evolution, structure, and function. First, there has been an extensive functional radiation among serpin proteins. Serpins regulate numerous separate intracellular and extracellular processes, including blood coagulation, fibrinolysis, cell migration, cell differentiation, embryo implantation, complement activation, tumor suppression, and other functions (Potempa, Korzus, and Travis 1994
). While most serpins appear to act as protease inhibitors (Wilczynska et al. 1995
), some have lost this inhibitory role and function instead in blood pressure regulation (angiotensinogen) or hormone binding (corticosteroid-binding globulin).
Second, there is considerable gene clustering. For example, the genes coding for human 1-antichymotrypsin, protein C inhibitor, kallistatin,
1-antitrypsin, and corticosteroid-binding globulin all occur in close proximity on the same human chromosome, suggesting that they might have arisen by tandem duplications from a common precursor (Billingsley et al. 1993
; Rollini and Fournier 1997
). However, in spite of their close physical proximity, these proteins have disparate functions, raising interesting questions about the evolution of gene function and expression.
Third, serpin genes exhibit a variety of distinct exon-intron patterns. The exon-intron structure of genes may contain important phylogenetic signals, and several authors have suggested the feasibility of evolutionary classifications based on comparative intron positioning (Long, de Souza, and Gilbert 1995
; Logsdon, Stolzfus, and Gilbert 1998
). This is certainly true for the serpin genes (Bao et al. 1987
; Ragg and Preibisch 1988
; Remold-O'Donnell 1993
; Ragg et al. 2001
). However, it is unclear whether classifications based on exon-intron structure are congruent with those based on amino acid sequence data. Indeed, it is well known that different estimates of phylogenetic relationships may result from analyses carried out on data from different levels of biological organization (e.g., Patterson 1987
; Sanderson and Hufford 1996
; Li 1997
; Patthy 1999
). Considerable data are available for serpin proteins from different levels of organization which can be used for comparative analyses of phylogenetic signals. Such comparative analyses can provide important information about the concordance of genomic, functional, and evolutionary classifications.
Fourth, many groups of proteins exhibit dynamic structural properties (Branden and Tooze 1999
); however, the origins and diversification of such dynamics are poorly understood. Because serpins exist in active, cleaved, and latent forms (Irving et al. 2000)
, detailed analyses of their evolutionary diversification may prove to be very useful in resolving questions about the origin and dynamics of such structural heterogeneity.
Thus, these attributes suggest that serpins constitute an important group for comparative evolutionary analyses of protein structure and function. However, such studies require a well-documented phylogeny upon which to base speculations about structural and functional variation. For example, a reliable phylogeny should provide a plausible explanation for variation in genomic structure, since exon-intron structure is well known to change over evolutionary time. Indeed, an understanding of the patterns of change in genomic structure will facilitate an understanding of the evolutionary history of the genes and their products. Elsewhere, we have shown that exon-intron structure is an important indicator of phylogenetic history in the serpins (Ragg et al. 2001)
. However, the most recent phylogenetic analyses of the serpin proteins by Irving et al. (2000)
ignore the consequences of exon-intron structure on estimates of phylogenetic relationships.
Here, we provide a robust evolutionary classification for a large group of serpins derived from several tree reconstruction methods. These analyses integrate amino acid sequences with exon-intron structure and family-specific diagnostic amino acid sites. They evaluate the null hypothesis that classifications based on amino acid sequence data and exon-intron structure are concordant.
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Materials and Methods |
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To integrate exon-intron structure with amino acid sequence changes, we constrained the database to include primarily those serpin sequences whose genes had well-documented exon-intron structures. We included only those vertebrate serpin genes for which the genomic structure had been clearly established from the genomic and cDNA sequences available (as of fall 1999). We took advantage of the fact that orthologous serpin genes (i.e., those coding for the same protein in distinct organisms) apparently exhibit the same exon-intron structure, at least in the conserved part of the serpins (i.e., downstream of amino acid position 32 using the 1-antitrypsin standard of Ragg et al. [2001]). Consequently, only one gene was chosen from any such family of orthologous genes, and, when known, we chose the human gene.
To conserve space, we have placed a list of protein sequences, phenotypes, and accession numbers, the sequence alignment, and other descriptive materials on a permanent website (http://bibiserv.techfak.uni-bielefeld.de/library/serpins/).
Phylogenetic Methods
Matrices of pairwise maximum-likelihood (ML) distance estimates (Durbin et al. 1998
) were computed from the aligned serpin domain sequences using either the BLOSUM 62 or the Jones-Taylor-Thornton (JTT) substitution matrix. The BLOSUM (blocks substitution matrix) scores are derived from local, ungapped alignments of a large number of distantly related proteins (Henikoff and Henikoff 1992
). Several variations of the BLOSUM matrix are available (e.g., BLOSUM 62) where the number refers to the minimum percentage of identity of the blocks used to construct the matrix. In contrast, the JTT substitution matrix (Jones, Taylor, and Thornton 1992
) uses a large collection of global alignments of closely related sequences.
Phylogenetic trees were estimated using ML and neighbor-joining (NJ) methods. ML methods are often preferred for estimating phylogenetic trees because they are based on explicit statistical models. Unfortunately, ML approaches are often not computationally feasible, particularly for moderate-to-large data sets. The quartet puzzling (QP) algorithm was introduced as a faster way to carry out ML phylogenetic analyses (Strimmer and von Haeseler 1996
). Consequently, we used QP (as implemented in PUZZLE 4.0.2) for these analyses because the computation requirements of other ML methods could not be satisfied for a data set of 110 sequences.
QP reconstructs ML trees for every quartet that can be formed from n sequences. Starting with one of the ML quartets, the neighbor relations of the remaining quartets are used to direct the addition of taxa, and this process continues until a complete n-taxon tree is constructed. This "puzzling" step is repeated 1,000 times with a different initial quartet and taxon addition order each time. A majority-rule consensus then generates the QP tree using only the well-supported taxon groupings. QP provides a resolved tree, reflecting only if the data are conclusive; otherwise, a multifurcating tree is constructed. The resulting ML trees estimated from BLOSUM 62 and JTT are denoted ML(B) and ML(J), respectively. Estimates of support analogous to bootstrap values are assigned to each node of the tree. QP also computes the number and percentage of unresolved quartets, i.e., those for which the ML values of the three quartet topologies are so similar that it is impossible to chose among them (Strimmer, Goldman, and von Haeseler 1997
).
In addition, NJ trees (Saitou and Nei 1987
) without QP were produced using the ML pairwise distance matrices from BLOSUM 62 (NJ(B)) and JTT (NJ(J)). These trees were each bootstrapped 500 times, and a consensus NJ tree was constructed.
To simplify comparisons, all trees in these analyses were rooted on PRTZ, which is the major endosperm albumin and the only plant sequence analyzed here. Levels of support are coded as filled circles (90%100%), open circles (75%89%) and plus signs (50%74%).
Quartet-Based Clique Analysis
An additional method for determining putative clades of related sequences, called partial split decomposition, was developed to provide a nonhierarchical way of clustering sequences. Graphs were constructed from a distance matrix D by first choosing two sequences a, b and then building a graph whose vertices are formed from the remaining sequences by connecting any two c, d of these by an edge if D(a, b) + D(c, d) < min[D(a, c) + D(b, d), D(a, d) + D(b, c)] holds. For treelike distances, this graph is the disjoint union of complete subgraphs whose vertices represent clades or complements of clades, depending on the root's position. Consequently, we looked for collections of sequences (C) giving rise to maximal cliques in these graphs and counted how many pairs a, b such a collection C would contain. In the ideal case, this number varies between t - 1 and 1/2t(t - 1) if t is the number of sequences outside C. The most significant of these putative clades was then compared with those found on NJ(B). More details can be found on the website described above.
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Results and Discussion |
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Figure 2
provides the NJ tree NJ(B) based on BLOSUM 62. This tree was estimated using the conventional NJ algorithm. The QP algorithm was not employed. Comparison of the NJ(B) and NJ(J) trees indicates that the major groupings and branching patterns are quite similar. ANGT might be considered more related to group 4 in the NJ(J) tree compared with the NJ(B) tree; however, the support from the bootstrap values for this conclusion is not very strong. The clusters of proteins that result from these two NJ trees show close correspondence to the groups of proteins described by the analyses of exon-intron structure (Ragg et al. 2001)
.
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The NJ(B) tree provides estimates of the deep-node structure of the tree not found in the ML trees. While it must be emphasized that this deep-node structure is not statistically well supported in the analyses of the sequence data themselves, we will see below that the incorporation of extrinsic data from the positions of introns, gene clustering, and partial split decomposition corroborates most of the NJ(B) estimates.
In a companion paper (Ragg et al. 2001)
, we examined the exon-intron structure, diagnostic amino acid sites, and rare indels in the vertebrate serpins and identified six distinct groups with individual genomic structures. Figure 3
provides a summary of the exon-intron structures of the vertebrate serpins. A detailed discussion of the various proteins included in each group in figure 3
, together with a graphical description of their exon-intron structure, is given in Ragg et al. (2001)
.
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These distinguishing features are summarized in table 1 .
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The sequence analyses suggest that there are several "orphan" groups in the phylogenetic trees whose evolutionary statuses are unclear. Included is a nematode cluster with two groups of serpin proteins: (1) BM-SPN-2, a serpin isolated from the filarial nematode Brugia malayi (Zang et al. 1999
), and (2) two sequences from Caenorhabditis elegans. The exon-intron structures of these sequences are known. They exhibit significant differences, but there are simply too few sequences to justify a decision about their status. Similarly, there is not enough information about the mosquito AFXA anticoagulant protein to clarify its evolutionary status. These various relationships in our subsequent phylogenetic analyses were primarily based on a greater diversity of sequences. Recall that the present analyses were restricted to only those serpins with well-defined exon-intron structures in order to evaluate the null hypothesis that classifications based on sequence data are concordant with those based on other types of information. These phylogenetic relationships are evident in the NJ trees irrespective of whether the BLOSUM 62 or the JTT substitution matrix was used to estimate the pairwise distances.
Generally speaking, there is close correspondence in the classification of serpin proteins. Based on the sequence and intron location data, the same sets of proteins are usually grouped together, and they generally show the same branching patterns. Groups 3, 4, 5, 6, M, and V reflect statistically well supported clades with strong statistical support at their defining nodes. Sequence analyses of group 1, the ovalbumins, indicate that they are a monophyletic group, but one exhibiting considerable diversity, as described by many previous authors (e.g., Remold-O'Donnell 1993
; Scott et al. 1999
).
One interesting aspect of these analyses concerns the origins of variation in exon 2 in the 1-antitrypsin group. Based on several lines of evidence, Ragg et al. (2001)
suggest that the HCII and ANGT groups belong to the
1-antitrypsin group (group 2) but are distantly related to the other proteins in this group, i.e., A1AT, CBG, COTR, and THBG.
The sequence analyses described here provide additional support for this hypothesis. In terms of genomic structure, an obvious distinction is that the second exon in both HCII and ANGT has a noticeable N-terminal extension not seen in the equivalent exon of the other sequences. The sequence alignments of the 1-antitrypsin group show that HCII and ANGT differ considerably from each other and from the other proteins in this region. The sequence information suggests that the exon extensions arose as two separate evolutionary events.
These findings are evident in the NJ(B) tree, which shows HCII and ANGT branching off early in the 1-antitrypsin lineage. Indeed, the branching sequence is HCII, then the PEDF, IC-1, and A2AP group, followed by the ANGT group. This branching pattern is also supported by partial split decomposition.
Comparison with Other Phylogenetic Analyses
There are some important differences between our results and those of Irving et al. (2000)
. The most prominent difference is that analyses based on amino sequence data (both NJ and ML(B)), exon-intron location, and diagnostic amino acid sites suggest that PAI1, GDN, and NEUS compose a single evolutionary lineage. Irving et al. (2000)
suggest that PAI1 and GDN constitute one clade, while the neuroserpins (NEUS) are in a separate evolutionary lineage. Interestingly, this is the same result given by the ML(J) analyses which we rejected because it did not agree with the known exon-intron data. Similarly, the analyses of Irving et al. (2000)
do not place IC1 in the same clade as PEDF and A2AP, as suggested by the exon-intron data.
Accuracy of ML and NJ Trees
It is interesting that in spite of often enthusiastic recommendations for ML methods, both ML trees depict as separate and unrelated clades sets of proteins whose genes (1) were derived from a common ancestral gene by duplication (e.g., the ovalbumins) and (2) have the same exon-intron structure. The ML(B) tree places in four separate and unrelated clades the proteins PAI2, SCC1, SCC2, PI8, megsin, and bomapin, which are coded by genes that map to the same 500-kb location on the human chromosome (fig. 2
). These genes have the same intron-exon boundaries (Ragg et al. 2001)
and probably arose by tandem duplication of a precursor gene (Scott et al. 1999
). The ML(J) analysis confounds this problem by also separating the PEDF, GDN, and neuroserpin lineages into two different clades. Ascertaining whether such problems are a function of QP (Adachi and Hasegawa 1998
) or of ML algorithms in general would be an interesting avenue for further investigation.
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Acknowledgements |
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Footnotes |
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1 Keywords: serpins
molecular evolution
phylogeny
protein evolution
maximum likelihood
neighbor joining
2 Address for correspondence and reprints: William R. Atchley, Department of Genetics, North Carolina State University, Raleigh, North Carolina 27695-7614. atchley{at}ncsu.edu
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