Abstract
Protein evolution is imprinted in both the sequence and the structure of evolutionary building blocks known as protein domains. These domains share a common ancestry and can be unified into a comparatively small set of folding architectures, the protein folds. We have traced the distribution of protein folds between and within proteomes belonging to Eukarya, Archaea, and Bacteria along the branches of a universal phylogeny of protein architecture. This tree was reconstructed from global fold-usage statistics derived from a structural census of proteomes. We found that folds shared by the three organismal domains were placed almost exclusively at the base of the rooted tree and that there were marked heterogeneities in fold distribution and clear evolutionary patterns related to protein architecture and organismal diversification. These include a relative timing for the emergence of prokaryotes, congruent episodes of architectural loss and diversification in Archaea and Bacteria, and a late and quite massive rise of architectural novelties in Eukarya perhaps linked to multicellularity.
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Introduction
Proteins are fundamental components of our biological world. Their function is determined by how amino acids are arranged in three-dimensional (3D) space and their emergence and diversification are imprinted in both the sequence and the structure of evolutionary building blocks known as protein domains (Aravind et al. 2002; Chothia et al. 2003). While the world of protein architecture can be considered uncharted (Kunin et al. 2003), domains have been unified into a comparatively small set of protein fold designs (Gerstein and Hegyi 1998). Consequently, the protein world appears to be finite (<104 folds in size) and slowly curbed by progress in structural genomics and acquisition of the sequence of entire protein complements (proteomes) (Grant et al. 2004).
Most proteins have been formed by gene duplication, recombination, and divergence and the protein world can be studied by matching proteins of known structure to genome sequences (Aravind et al. 2002; Chothia et al. 2003). Protein folds are among the most conserved components in nature, making them good candidates for the study of distant evolutionary relationships. Folds were surveyed in a number of genomes (Gerstein and Levitt 1997; Gerstein 1997, 1998; Frishman and Mewes 1997; Wolf et al. 1999; Hegyi et al. 2002) and indexed in several databases (Lee et al. 2003). Fold composition, measured as presence–absence of individual folds, was used to reconstruct whole-genome trees based on the idea that closely related organisms must share significantly more fold architectures than distantly related ones (Gerstein 1998; Wolf et al. 1999; Lin and Gerstein 2000). These phylogenetic trees generally resembled ribosomal RNA (rRNA) phylogenies, had generally well supported topologies, and were built using both distance and parsimony methods. While genome (proteome) trees depict organismal diversification (Wolf et al. 2002), a genomic census of architectures can also be used to reconstruct the history of architectural diversification of proteins.
Using a “Hennigian” cladistic approach based on shared and derived features in structure that are descriptive of common descent (Caetano-Anollés 2002), we recently reconstructed a phylogeny of protein architecture (Caetano-Anollés and Caetano-Anollés 2003). We counted the number of genes corresponding to fold architectures in genomes and used these measures of “genomic demography” to map the world of proteins and track both architectural and organismal history directly at the proteome level. Rooted phylogenetic trees of proteomes and fold architectures revealed clear evolutionary patterns, dramatic diversification events in the history of life, and a common ancestor with an architectural-rich and relatively modern organization. All structural classes of globular proteins appeared very early in evolution and in defined order, the α/β class being the first, followed by the α +β, the all-α, and the all-β classes, and by small (S) and multidomain (M) proteins. This trend was also evident throughout the tree of architectures, supporting the idea that designs with interspersed α-helices and β-sheets were segregated in the course of evolution, first within their structure and then confined to separate molecules (as in the all-α and all-β classes), an observation that is consistent with the random origin hypothesis of proteins (White 1994) and patterns of modularity and simplification in molecular design (Hartwell et al. 1999; Ancel and Fontana 1999). The most primitive protein folds shared a common architecture of barrels or interleaved β-sheets and α-helices, and clear trends of architectural transformation were evident in the tree of architectures, such as an evolutionary increase in the curl and stagger of β-barrels in the all-β class. While these patterns relate to global structural changes, they do not address how protein architectures are shared within an evolutionary context throughout the three domains of life.
We here extend our initial studies of the protein world by exploring how protein folds are distributed among and within organismal domains. These distributions were mapped (traced) along the branches of a phylogenetic tree of protein architecture that was reconstructed from measures of fold frequency in individual proteomes, charting organismal and genomic information in structural space. Our approach is based on two fundamental premises: (1) that protein structure is far more conserved than sequence and carries considerably phylogenetic signal and (2) that biological designs that have been successfully implemented tend to be reused over and over again in nature. A number of considerations support these premises.
Protein structure is directly linked to function and is therefore the subject of natural selection and strong evolutionary constraint (Chothia and Lesk 1986). Consequently, 3D structure is less prone to mutation than sequence and the information embedded in structure persists longer than in primary sequence. In fact, the higher we go in the structural hierarchy of the protein world, the more resilient the architectural designs (Murzin et al. 1985; Orengo et al. 1997). While proteins in protein families are closely related, folds can pool distant relatives with <25% sequence homology (Chothia et al. 2003). These realizations are important. Theoretical considerations suggest that sequence data may be inherently limited in its ability to uncover deep phylogenetic signatures and ancient relationships (Sober and Steel 2002; Penny et al. 2003; Mossell 2003). Primary sequence runs out of useful information when the repeated accumulation of substitutions in nucleotide sites (site saturation) erases evolutionary history. Convergent evolution of nucleotide sites, differing substitution rates among sites and lineages, and nonindependent site substitutions are just few of many other contributing factors (Philippe and Laurent 1998). Consequently, phylogenetic hypotheses describing deep phylogenetic relationships can be sometimes equivocal or incorrect if solely based on primary sequence. Genomics now enables the use of other types of biological information that can be preserved for longer periods of time, such as rare genomic changes (e.g., intron indels, retrotransposon integrations, genome rearrangements [Rokas and Holland 2003]) or structural features in macromolecules (Caetano-Anollés 2002). However, it is difficult to find features that can be applied globally to a wide range of taxonomical levels. We contend that genomic demography of high levels of structural organization in proteins offers the right framework to uncover distant relationships.
From a perspective centered on fitness, architectural designs that had been successfully deployed will have more chances to be reused in other biological contexts, and are expected to become popular. Consequently, robust and well-evolved structures have more chances of withstanding the effects of time and becoming widely used. In this study, we use an initial model of structural evolution in which protein folds that are more prevalent in nature originate from innovations in structure occurring earlier in evolutionary time. The model is based on Okham’s principle of preferring simple explanations to complex ones and is supported by the survey and comparison of protein folds, statistical analyses of genome sequences, and network behavior of protein fold occurrence (Caetano-Anollés and Caetano-Anollés 2003). The model is simple but is consistent with a process of fold acquisition that obeys a power law and is driven by the gain and loss of protein domains (Huynen and van Nimwegen 1998; Quian et al. 2001; Rzhetsky and Gomes 2001; Karev et al. 2002, 2003). In fact, power law distributions of folds in proteomes are better described by quasilinear birth–death–innovation models that take into account domain family size (Karev et al. 2002, 2003). These models suggest that small protein families are more dynamic, more prone to innovation and elimination, and more recent than large ones. This supports the “polarization” of phylogenetic characters and the rooting of trees in our study. The application of steady-state demographic techniques to the age distribution of duplicate genes in completely sequenced genomes has shown that the average rate of duplication of an eukaryotic gene (0.01/gene/MY) is of the same order of magnitude as the mutation rate per nucleotide site and that the half-life of a gene averaged over many species is small (∼4 MY [Lynch and Conery 2000, 2003a]). Variation in duplication rates and genetic effective population size appear to be drivers of genome complexity and a direct consequence of the quasi-equilibrium birth-and-death process (Lynch and Conery 2003a, b). Despite being highly transient, some genes are preserved for long periods of time and contribute to both long-term phenotypic evolution (via subfunctionalization or neofunctionalization processes) and size increase of domain families and fold categories. These genes, together with processes such as domain “shuffling,” are probably the main contributors to innovation in protein architecture and are responsible for converting functionally versatile proteins into specialized forms.
Based on these considerations, our study explores how the architectural protein repertoire was tailored during organismal diversification by gene genesis and innovation, gene loss, and horizontal gene transfer (HGT) events, uncovering patterns in the origin and diversification of protein molecules and life.
Materials and Methods
Genomic Demography and Character Coding
Protein entries matching fold categories in the Structural Classification of Proteins (SCOP) database (Murzin et al. 1995) were retrieved from PEDANT (Frishman et al. 2001) in a set of 32 species with finished genomic sequences encompassing Eukarya (Arabidopsis thaliana var. Columbia, Caenorhabditis elegans, Drosophila melanogaster, Neurospora crassa OR74A, Saccharomyces cerevisiae S288C, and Schizosaccharomyces pombe 972h), Archaea (Aeropyrum pernix K1, Archaeoglobus fulgidus DSM4204, Halobacterium sp. NRC-1, Methanobacterium thermoautotrophicum delta H, Methanococcus jannaschii DSM26621, Pyrococcus horikoshii OT3, Sulfolobus solfataricus P2, Sulfolobus tokodaii strain 7, and Thermoplasma acidophilum), and Bacteria (Aquifex aeolicus VF5, Bacillus subtilis 168, Borrelia burgdorferi B31, Chlamydia pneumoniae CWL029, Clostridium acetobutylicum ATCC824, Deinococcus radiodurans R1, Escherichia coli K12, Haemophilus influenzae Rd [KW20], Helicobacter pylori J99, Mycobacterium tuberculosis CDC1551, Mycoplasma genitalium G-37, Mycoplasma pneumoniae M129 [ATCC29342], Rickettsia prowazekii MadridE, Staphylococcus aureus N315, Synechocystis sp. PCC6803, Thermotoga maritime MSB8, and Treponema pallidum Nichols). SCOP release 1.59 cluster PDB structural entries into 686 fold categories depicting evolutionary and structural relationships (Lo Conte et al. 2002). Fold recognition in PEDANT involves 10 PSI-BLAST iterations using each SCOP domain against a nonredundant protein sequence database, the construction of an IMPALA-based SCOP profile library, and its use to search genomic sequences (Frishman et al. 2001).
For phylogenetic analysis, each genome was described by a set of linearly ordered multistate characters representing the frequency with which the fold occurs in the proteome. This frequency of fold architectures was termed genomic abundance (G). The gap-recoding technique of Thiele (1991) developed for the analysis of morphometric data was used to normalize G and compensate for differences in genome size and proteome representation. Briefly, a rescaling function was used to rescore character information while retaining information on both rank order and size of gaps between character states. Raw frequency data were ranked as ordered sets of states, standardized using a square root transformation to account for unequal variances, and range standardized to an arbitrary 0–20 scale (compatible with most phylogenetic programs). This ensured that differences within and between characters were equalized with the transformation and range-standardization procedure and that data would approximately fit a power-law with exponents of the order of 2, i.e., matching decay gradients for the vast majority of genomes analyzed. Once coded, characters were aligned in ordered columns and subjected to phylogenetic analysis.
Phylogenetic Analysis
A universal tree of protein architecture was reconstructed from the occurrence of folds in proteomes using maximum parsimony (MP) as the optimality criterion and directed character states in PAUP* (Swofford 1999). The method of tree reconstruction was described previously (Caetano-Anollés and Caetano-Anollés 2003). Characters were polarized with the ANCSTATES command by assuming that the number of proteins in a genome that exhibits a particular fold increases in the course of evolution, with character state transformation proceeding from states of higher frequency to states of lower frequency (see Discussion for rationale). Trees were automatically rooted at the point where the hypothetical ancestor connects to the tree. Character argumentation is supported by the power-law behavior of fold occurrence (Huynen and van Nimwegen 1998) and is consistent with birth–death–innovation models that describe a preference for duplication of genes encoding already common features and the “rich-get-richer” growth of the protein world (Quian et al. 2001; Rzhetsky and Gomes 2001; Karev et al. 2002, 2003). When ranking genome components by their occurrence, we consider that those protein families that grew early in evolution will be prominent in many genomes and that the number of family members increases in single steps corresponding to the addition or removal of an homologous gene in a family. We assume that this process is reversible and expresses an asymmetry with gene duplication being favored over gene loss.
Phylogenetic reliability was evaluated by the nonparametric bootstrap method (Felsenstein 1985), generally implemented using 2 × 103 pseudoreplicates in PAUP*, and by double decay (DD) analysis using RADCON (Thorley and Page 2000). Ensembles of DD-derived reduced cladistic consensus (RCC) support trees were used to measure strengths and weaknesses of phylogenetic hypotheses (Wilkinson et al. 2000). The structure of phylogenetic signal in the data was tested by the skewness (g1) of the length distribution of at least 104 random trees and permutation tail probability (PTP) tests of cladistic covariation using at least 103 replicates. Ensemble consistency (CI) and retention (RI) indices were used to measure homoplasy and synapomorphy.
Fold Distribution Among Organismal Domains and Cumulative Frequency Plots
We classified protein folds into categories that describe the spread of architectures across the three organismal domains of life, Eukarya, Archaea, and Bacteria. A fold that appears in at least one proteome but in all organismal domains was assigned to the EAB category, one that is only present in Archaea and Bacteria to the AB category, and so on. Cumulative frequency plots were used to depict order and rate of appearance of fold distribution categories. Cumulative fold number was given as a function of distance in nodes from a hypothetical ancestral fold on a relative 0–1 scale, termed “node distance” (nd). These plots can be considered time plots of lineages (Nee et al. 1994) with a time axis defined in relative units (cladogenic events). Fold distribution categories and a distribution index (f) that describes the distribution of individual folds among proteomes within each category were traced along the branches of the tree of protein architecture. The f index represents the fraction of proteomes harboring a fold within a category and ranges from absence (f = 0) to presence in all proteomes analyzed (f = 1). The f values were sometimes averaged over all folds within a category or over folds with a same nd (\( \bar f \)), and ANOVA used to evaluate statistical significant differences. The number of protein families associated with each fold in SCOP was also analyzed to study how unifolds, mesofolds, and superfolds (Coulson and Moult 2002) were distributed in the tree of architectures. Values were averaged for folds in individual fold distribution categories or fold groups.
Character Tracing
Fold distribution categories and the distribution index were traced along the branches of the tree of architecture in MacClade (Maddison and Maddison 1999), using algorithms for Wagner and square-change parsimony, respectively. Wagner parsimony minimizes the sum of the absolute values of changes along the branches of the tree using a linear parsimony criterion (Swofford and Maddison 1987), and squared-change parsimony reconstructs the ancestral states of continuous-valued characters (Maddison 1991). Note that squared-change parsimony minimizes the sum of the squared changes on the branches and can be considered a Bayesian probability estimate under a Brownian motion model of evolution. Methods are “agnostic” in that they justify for example the existence of both increases and decreases in continuous-valued characters along branches of the tree.
Results
Tracing Protein Fold Distribution Across Organismal Domains of Life
The monophyletic nature of Eukarya, Archaea, and Bacteria in a universal phylogeny of proteomes inferred from fold occurrence data (Caetano-Anollés and Caetano-Anollés 2003) supports the existence of three organismal domains in life (Woese et al. 1990). We therefore classified protein folds into categories that describe their distribution across these domains. Categories include folds common to all (EAB), a subset of (EB, EA, and AB), or individual (E, B, and A) domains. Fold distribution categories and an index (f) describing the popularity of individual folds among proteomes in each category were traced in a phylogenetic tree of protein architecture and the timing and rates of appearance used to signal evolutionary patterns in the history of proteins and life. The tree used in this study was reconstructed from the occurrence of folds in 32 proteomes using MP and directed character states. Note that the tree is intrinsically rooted, that leaves correspond to folds and nodes to architectural diversification events, and that nodes close to the base of the tree reflect more ancient events than those close to the leaves.
Clear Fold Distribution Patterns in the Proteome World
Cumulative frequency plots were used to depict order and rate of appearance of fold distribution categories in the tree (Fig. 1). Cumulative fold number was given as a function of distance in nodes (nd) from a hypothetical ancestral fold. These representations were quite robust, as suboptimal tree reconstructions showed that systematic and random error did not affect the rates of fold accumulation substantially.
Frequency plots showed that the first folds to appear in evolution were those that were common to Eukarya, Archaea, and Bacteria (Fig. 1A). These early EAB architectures were highly represented in the 32 proteomes analyzed (\( \bar f \) = 0.83) and included most folds belonging to a genetic core of universal genes exhibiting the same phylogenetic pattern as rRNA (Harris et al. 2003) (Table 1). Most EAB folds were confined to the base of the universal tree (Fig. 2) and could be considered superfolds and mesofolds, based on the number of protein families associated with them (Fig. 3). The average number of protein families per fold was consequently high, 5.72 ± 0.56 (SE) for all EAB folds and 9.63 ± 1.34 (SE) for EAB folds at the base of the tree (Figs. 2 and 3). Figure 4 shows a subtree reconstructed from 17 of the most primitive folds (most of them superfolds) spanning the 0–0.12 nd range, using directed and undirected multistate characters. The example illustrates how tree topologies reconstructed from multistate characters are not significantly altered by character polarization. Interestingly, polarizing the character transformation series in the opposite direction, results in a less parsimonious reconstruction (1185 versus 1066 steps) (see legend to Fig. 4), an observation that supports character argumentation. This same behavior was obtained when reconstructing several other subtrees of the tree of architectures (data not shown). The number of protein families within individual fold architectures was traced in the subtree of ancient architectures and the value for the ancestral fold reconstructed at the base of the tree (Fig. 4C). Based on extant protein information, the ancestor of the tree of architectures appears to have had 31 protein families associated with it.
In contrast, the second derived half of the inferred evolutionary tree was mainly composed of folds belonging to one or subsets of organismal domains (Fig. 2). These folds were patchily distributed among proteomes (\( \bar f \) = 0.51), perhaps resulting from architectural innovation, reductive evolution, and HGT. This trend toward patchiness was evident when plotting \( \bar f \) values as function of nd (Fig. 1B). Most folds in the second one half of the tree were unifolds, composed of only one or two protein families (Figs. 2 and 3). Interestingly, mesofold and superfolds common at the base of the tree recurred later in evolution, but at a low frequency.
Tracing distribution categories in the tree showed interesting patterns. First, noncommon categories appeared in defined order, AB being first (nd ≥ 0.522), followed by EA and EB (nd ≥ 0.577), then by A and B (nd ≥ 0.579), and finally, by E (nd ≥ 0.670) (Fig. 1). Rates of appearance were also illustrative; especially those related to the E and EB categories, which increased late but quite massively. Second, categories were represented in proteomes at varying levels (ANOVA, p < 0.0001). While EAB, EA, and, to a lesser degree, E folds were highly represented within their categories, all the rest were sparsely distributed (Fig. 1A, insets). Finally, folds in the different categories were clearly clustered in the tree, suggesting episodes of protein diversification (Fig. 2).
Topological features in the tree were used to classify folds into different groups (Fig. 2). Two major clades emerged about halfway in evolution (nd ∼ 0.43), one defining two clear subclades (groups II and III) and the other defining several subclades (>20 nodes each) composed mostly of folds shared by proteomes in one or two organismal domains (groups IV–IX). Note that the emergence of these two major clades coincides with the onset of organismal diversification.
The Rise of Prokaryotes
The very early appearance of architectures in the AB category (initially in group IV; Fig. 2) suggests a prokaryotic lineage common to Archaea and Eukarya. These AB folds are currently associated for example with iron-uptake systems (a.76), cobalamine synthesis (c.39), oxidation in specialized glycolytic pathways (d.152), DNA bending (a.55), and chemotaxis (c.40, a.58). Phylogenetic analysis of folds appearing within the 0.52–0.56 nd range is shown in Figure 5. The tree shows a topology that defines the onset of the major fold divide described above (groups II and III vs. group IV). The topology was well supported by the RCC trees derived from DD analysis. Curiously, the appearance of AB folds during this transition phase was accompanied by a sharp decrease in f values, suggesting a marked episode of architectural loss in the prokaryotic ancestors.
Architectural Loss and Diversification in Archaea and Bacteria
AB folds appearing in group IV, and subsequently in group III, had a very low collective \( \bar f \) value (0.37) compared with surrounding EAB folds. Furthermore, their gradual appearance was accompanied by the emergence of a group of 19 EA and EB folds. Interestingly, EB folds were confined to group III and EA folds to group IV. This could signal episodes of architectural loss in the prokaryotic ancestors of Archaea and Bacteria, respectively, induced perhaps by genome reduction. Figure 6 shows folds appearing within the 0.58–0.60 nd range. This diversification time frame includes the origin of first folds unique to Archaea and Bacteria and shows a marked decrease in the f values of associated folds. The pronounced dip of f values starting at about 0.6 nd unit in the tree (Fig. 1B) and the low \( \bar f \) value averaged for folds in clade IV (Fig. 2) are consistent with a marked diversification episode associated with gene loss. Folds unique to Archaea and Bacteria that followed immediately could also result from episodes of architectural innovation (presumably coupled with HGT). Note that folds in the A and B categories were those with the lowest \( \bar f \) (Fig. 1A, inset), showing the distinct behavior of prokaryotic proteomes. Interestingly, over half of all A and B folds arose during this period of diversification and B folds in clades III had significantly higher f values (0.65) than those appearing in clades IV and VIII (0.10). These differences may indicate different mechanisms of diversification in these two architectural groups.
Late Rise of Architectural Novelties in Eukarya
Folds unique to Eukarya appeared quite late in the evolutionary tree (nd > 0.670) (Fig. 1) and massively (especially in group V; Fig. 2). Proteins believed linked to multicellularity (Chervitz et al. 1998; Copley et al. 1999; Patthy 2003), including those involved in intra- and intercellular signaling and cell death programs (e.g., necrosis, apoptosis), generally contained multiple domains within only 50 fold categories (Table 2), 34 of which arose during the Eukaryal diversification phase (Fig. 2). Interestingly, 14 folds were eukaryotic novelties and 12 folds originated immediately after prokaryotic diversification events, most of which were EAB folds. One of these folds is the LysM domain, a fold that was recently linked to a Nod factor receptor in plants mediating the establishment of the Rhizobium–legume nodule symbiosis (Limpens et al. 2003). Note that 20 folds of the total fell within the EAB category, but only 4 were really ancestral (b.1, a.7, c.62, and d.144). Many of the folds associated with multicellularity, especially those in group IX, were mesofolds (Table 2), and these were responsible for the increase in the average number of protein families associated with folds in groups VI through IX (Fig. 2).
Discussion
We here explore how protein folds, defined by the well-established SCOP classification of proteins (Murzin et al. 1995), cross organismal domain boundaries along the branches of a phylogenetic tree of protein architecture. The trees we use for phylogenetic “tracing” of these distribution patterns reconstruct the evolutionary history of protein diversification using data generated from a genomic census of architecture. Consequently, they depend on (1) the accuracy and balance of genomic databases, (2) efficient and accurate assignment of structures to proteins, (3) adequate structural classification schemes, and (4) the methods of phylogenetic tree and character state reconstruction. The survey of proteomes in terms of structural components can be influenced by biases in the databases and sampling error (Gerstein and Hegyi 1998). Biases include over- and underepresentation of certain sequences and structures in the protein repositories. PDB databanks are especially biased by preferences of individual investigators for targets and organisms and physical constraints imposed by crystallography and NMR spectroscopy. Structural assignments have been quite empirical and cover only a fraction of the proteome. However, the use of advanced hidden Markov models (HMM) and threading techniques has advanced the mapping of protein folds to more than half of protein-encoding genes in currently available genome sequences (Grant et al. 2004). The number of “orphan” sequences awaiting structural assignments will most surely decrease with further advances in structural genomics, and this benefits our approach. Similarly, progress in the number of completely sequenced genomes, now approaching 200 and yielding over 1 million protein sequences, will only enhance genomic demography estimates and widen taxonomical coverage. Our study is based on SCOP, a robust protein classification scheme supported by structural and evolutionary considerations (Murzin et al. 1995; LoConte et al. 2002). We do not expect that the operational definition of fold be seriously challenged in the near future, even though many folds may be better described by “continuous” rather than “discrete” distributions in structure space (Harrison et al. 2003). Comparison of releases 1.43 and 1.59 of SCOP showed that revisions did not affect substantially the reconstructed trees (Caetano-Anollés and Caetano-Anollés 2003). However, challenges to the monophily of specific fold architectures should be addressed appropriately with rigorous studies, such as those characterizing the TIM barrels (Copley and Bork 2000; Nagano et al. 2002), as well as the existence of convergent evolutionary events that could place unrelated protein families within nonnatural fold categories. Our study makes no effort to explore such instances and places trust on the accuracy of SCOP assignments. In this regard, our approach considers an individual fold as a collection of proteins undergoing different but concomitant evolutionary processes that translate into patterns of recent (close relationship) or ancient origin (distant relationship). Finally, phylogenetic methods affect the reconstruction of trees. We use MP as the optimality criterion, i.e., we prefer solutions for reconstruction of phylogenies and ancestral character states that require the least amount of change. The task is computationally demanding, as trees have over 500 leaves and are reconstructed from multistate characters. Consequently, it is unrealistic at this time to use even more demanding model-based methods such as maximum likelihood (ML) or Bayesian approaches.
MP works well when change is rare and tree branches are short but does not take into account branch length and can generate “long-branch attraction” artifacts (Felsenstein 2004). However, the use of large trees can mitigate some of these effects. Furthermore, MP can be an appropriate criterion that can outperform ML under certain circumstances (Steel and Penny 2000). MP is precisely ML when character changes occur with equal probability but rates vary freely between characters in each branch. This Poisson-like model with “no common mechanism” can be particularly useful when there is limited knowledge about underlying mechanisms linking characters to each other (Steel and Penny 2000). Furthermore, the use of a huge character state space, such as with features that describe fold occurrence in genomes (even when rescaled by gap-recoding), provides the benefits of decreasing the likelihood of revisiting a same character state on the underlying tree and making MP statistically consistent (Steel and Penny 2000; Semple and Steel 2002). Note that this “homoplasy-free” condition that evolves characters in the tree without reversals or convergent evolutionary processes should be considered a phylogenetic analogue of the “infinite alleles” model of population genetics. While MP appears to be an appropriate criterion for an initial study of architectural evolution, we do not know how polarized multistate transformation series affect tree reconstruction. The comparison of directed and undirected characters yielded comparable topologies when reconstructing trees of architectures (Fig. 4) and proteomes (Caetano-Anollés and Caetano-Anollés 2003). Moreover, character polarization in a direction opposite to our assumptions of character transformation result in less parsimonious trees, supporting our model of character evolution. We can tentatively conclude that hypotheses of character polarity (unlike those of order) appear to have minimal impact on tree topology. One cautionary note is that our evolutionary model is minimalist and does not account for variation in evolutionary rates across characters and lineages and changes in the size of the protein world expected to have occurred during evolution. More complex models may be warranted in the future in order to account for every possible evolutionary event in the tree.
Character tracing along the branches of the tree of architectures suggests that the first folds to appear in evolution were those that were common to Eukarya, Archaea, and Bacteria (Figs. 1 and 2). These folds include a genetic core of universal genes exhibiting the same phylogenetic pattern as rRNA (Harris et al. 2003), most of which contained many protein families and were mesofolds and superfolds (sensu Coulson and Moult 2000) (Figs. 2 and 3). It is not surprising that ancient architectures were those commonly shared; they had more time to spread through vertical descent in the proteome world. What is amazing is the incredible resilience of these architectural designs, capable of surviving billions of years of evolutionary change. In contrast, the second derived half of the tree of architectures was mainly composed of folds that belonged to one or subsets of organismal domains, were patchily distributed among proteomes, and appeared in defined order. Most of these folds were unifolds associated with only one protein family. We believe they were the result of architectural innovation, reductive evolution, and HGT events.
The marked heterogeneities in fold distribution observed in the tree of architectures were unforeseen and suggest architectural innovation preceded organismal diversification. In fact, results suggest that 21% of folds had already been “discovered” by nature at the onset of organismal diversification and that an additional 10% were derivatives that evolved directly from them (in clades I and II) and remained widely distributed among organismal domains. Note that we defined the ancestral condition for folds in our model as being “popular” within a proteome (i.e., highly represented in relation to other folds) and not being “widely shared” between the proteomes of the organisms examined. Consequently, evolutionary patterns should not be considered artifacts stemming from tautology but true depictions of the spread of architectures through evolutionary transects. Note that all superfolds and highly represented mesofolds were expected to appear at the base of the tree and to be shared by all organismal domains. In fact, this is what was observed (Fig. 3). However, family expansions within folds occurred throughout the tree of architectures, suggesting that it is a phenomenon that is somehow unrelated to the representation of folds in proteomes and to the extent of fold distribution in life.
The very early appearance of architectures in the AB category arising from a world of common folds suggests the birth of a prokaryotic lineage common to Archaea and Bacteria and a sister-group relationship of these prokaryotic domains. This matches the topology of the universal tree of proteomes (Caetano-Anollés and Caetano-Anollés 2003). Immediately following the AB folds, a group of EB and EA folds with moderate to low f values appeared to be confined to separate clades in the tree. The emergence of these folds signals episodes of architectural loss in the prokaryotic ancestors of Archaea and Bacteria, induced perhaps by genome reduction events affecting different architectural sets in the two lineages. Interestingly, folds unique to Archaea and Bacteria also appeared during this diversification time frame, always coupled with a marked decrease in the f values of associated folds. Gene loss has been cited as the most important factor shaping genome content in prokaryotes, followed by HGT and gene genesis (Snel et al. 2002; Kunin and Ouzounis 2003; Daubin et al. 2003). There are good reasons to believe that architectural loss was a dominant force during this first organismal diversification episode that started 0.52 nd unit from the most ancestral fold. The episode was probably triggered by environmental influences and driven by the establishment of different selection strategies in the emerging organismal lineages. While organisms under K-selection probably took advantage of the carrying capacity of the environment, the early ancestors of prokaryotes resorted to r-selection pressures that favored rapid growth in periods of nutrient availability (Carlile 1982) or strategies that would favor colonization of hostile environments (Grime 1977). This forced the adoption of a more efficient and agile lifestyle, the expansion of effective population size by reducing organismal size, and the streamlining of genomic constitution (Conery and Lynch 2003b). Consequently, the patchiness of fold architectures observed during this period was probably the consequence of extensive and differential loss of genes in the emerging prokaryotic lineages, a process that occurred concomitantly with the discovery of new functions and architectures.
We found that protein novelties unique to organismal lineages emerged late and in defined order during evolution. Under this scenario, prokaryotes preceded eukaryotes. Note that folds unique to Eukarya originated quite late in the evolutionary tree, well after the diversification of Archaea and Bacteria. We believe that their massive appearance was linked to evolutionary novelties related to the emergence of multicellularity. The evolution of multicellularity probably involved modular assembly of domains from numerous extracellular matrix proteins and intracellular and extracellular signaling proteins (Chervitz et al. 1998). Most folds containing protein domains believed to be linked to multicellularity (Chervitz et al. 1998; Copley et al. 1999; Patthy 2003), such as those involved in programmed cell death (e.g., necrosis, apoptosis), appeared following prokaryotic diversification events and during the eukaryal diversification phase (Fig. 2). Interestingly, many folds that follow fold clusters unique to prokaryotes were common EAB architectures. Most of these folds describe domains that support extracellular signaling functions (Table 2) and could have been recruited to combine with domains unique to Eukarya. Perhaps these folds represent adaptations to innovations stemming from the prokaryotic world, and if so, they could support a symbiotic origin of multicellularity. There appears to be strong yet circumstantial evidence of microbial symbiosis influencing the evolution of multicellular organisms (McFall-Ngai 2001). Prokaryotes have the ability to behave as multicellular organisms, such as in quorum sensing and microbial cell differentiation phenomena, and multicellular eukaryotes can share pathways of responses, communicate, and establish symbiotic interactions with prokaryotes. For example, extensive responses can be triggered by bacterial quorum sensing signals in plants and animals, and in turn, plant quorum sensing “mimics” can interfere with inter-bacterial communication (Bauer and Mathesius 2004). Interestingly, responses induced by quorum sensing N-acyl homoserine lactones in the legume Medicago truncatula (Mathesius et al. 2003) involved proteins that could be assigned to 30 fold architectures, 87% of which were EAB folds and most of which were basal (within groups I and II) in the tree of architectures (in preparation). Only 13 folds were derived and these were clearly associated with fold clusters unique to prokaryotes (groups IV and VI) or with folds linked to multicellularity (groups IX). Consequently, eukaryotic responses to bacterial communication signals involving folds of recent origin appear to follow instances of prokaryotic innovation.
The suggestion that the ancestors of Archaea and Bacteria preceded those of Eukarya tells little about the nature of the common ancestor of diversified life. Our results suggest however that the proteomes of these ancestors shared already a quite diverse arrangement of molecular architectures. These architectures appear functionally versatile, since folds at the base of the tree of architectures harbor many enzymatic functions (Caetano-Anollés and Caetano-Anollés 2003). Our view is consistent with a primitive proto-eukaryote (Glansdorff 2000; Poole et al. 1998) responsible for “crystallizing” diversified life (Woese 2000). It is also consistent with a limited role of HGT (Glansdorff 2000). This does not mean that HGT had not been rampant, especially within prokaryotes; organisms actually share conservatively only a very small proportion of gene sequences (Gogarten et al. 2002). Instead, the existence of clear evolutionary patterns in the data suggests that HGT had minimum homogenizing effect at the high levels of structural organization characteristic of protein folds. While fold architectures were lost or invented, HGT shuffling must have had little influence on the birth–death–innovation kinetics driving their accumulation.
Rivera and Lake (2004) recently proposed eukaryotes emerged from a fusion of archaeal and bacterial genomes. Our evolutionary tracings are compatible with this prokaryotic fusion hypothesis, since architectures unique to Archaea and Bacteria arose earlier than those unique to Eukarya. However, the ring graph used to infer the putative fusion event was unrooted, and alternative explanations should be considered. For example, differential loss of genetic repertoires could result in a proto-eukaryote “fission” that would open the “ring of life” to streamlined archaeal or bacterial prokaryotes. This could explain the rise of prokaryotic fold diversity associated with gene loss in our trees. In any event, fusions and fissions ultimately represent cataclysmic or progressive homogenizing forces that rival HGT and act as genetic scaffolds for the generation of structural diversity.
Conclusions
The patterns of molecular and organismal diversification observed here are based on demography of fold architecture in proteomes. Consequently, evolution is not described as changes in structural character states (sensu Caetano-Anollés 2002) but rather as the accumulation of variants within a structural “neighborhood.” Within this framework, evolution of protein architecture can be explained by a verbal model that invokes the metaphor of a discrete multidimensional fitness landscape (Wright 1932; Kauffmann 1993; Gavrilets 1997) and describes the sequence space of the protein world. The ruggedness of this landscape (i.e., the frustration of the system) is dynamic and determines the nature of adaptive diffusive walks toward highly evolved molecular functions that occur during evolution and result from the general mapping of sequence into structure. These adaptive walks represent sets of character states that depict molecular transformations within the structural neighborhoods and lead to optimal phenotypes. Depending on the connectivity of the system, protein architectures can be trapped in local optima at different levels or can escape toward new adaptive peaks by changes for example in the phenotypic dimensionality of the landscape. This could occur when the landscape is altered, for example, by domain “shuffling” (sensu Lupas et al. 2001), HGT, architectural loss, fusions and fissions, or organismal diversification. Intuitively, shuffled protein segments expressing little evolutionary lock-in (probably common at the onset of structural diversification) will be less “evolvable” than those achieving higher lock-in levels and modularity (the ability to sustain integrity across varying genetic contexts) (Hansen 2003). Consequently, modularity embodied in domains capable of combining effectively to produce new proteins increases phenotypic dimensionality, reducing the ruggedness of the global landscape. HGT has a similar but less pronounced effect as entire genes are shuffled into different genetic contexts. In contrast, gene loss and organismal diversification (embodied in the emergence of major and minor lineages) decrease phenotypic dimensionality by forfeiting opportunities of innovation or by restricting the exchange of protein modules through recombination or genetic exchange, respectively. Our structural demography studies establish phylogenetic links between patterns describing molecular and organismal diversification that can be used to portray the complexities of the proposed adaptive landscape.
References
LW Ancel W Fontana (2000) ArticleTitlePlasticity, evolvability, and modularity in RNA J Exp Zool (Mol Dev Evol) 288 242–283 Occurrence Handle10.1002/1097-010X(20001015)288:3<242::AID-JEZ5>3.0.CO;2-O Occurrence Handle1:CAS:528:DC%2BD3cXnsFChs70%3D
L Aravind R Mazumder S Vasudevan EV Koonin (2002) ArticleTitleTrends in protein evolution inferred from sequence and structure analysis Curr Opin Struct Biol 12 392–399 Occurrence Handle10.1016/S0959-440X(02)00334-2 Occurrence Handle1:CAS:528:DC%2BD38Xlt1Shs78%3D Occurrence Handle12127460
WD Bauer U Mathesius (2004) ArticleTitlePlant responses to bacterial quorum sensing signals Curr Opin Plant Biol 7 429–433 Occurrence Handle10.1016/j.pbi.2004.05.008 Occurrence Handle1:CAS:528:DC%2BD2cXlsVWhtbk%3D Occurrence Handle15231266
G Caetano-Anollés (2002) ArticleTitleEvolved RNA secondary structure and the rooting of the universal tree of life J Mol Evol 54 333–345 Occurrence Handle11847559
G Caetano-Anollés D Caetano-Anollés (2003) ArticleTitleAn evolutionarily structured universe of protein architecture Genome Res 13 1563–1571 Occurrence Handle10.1101/gr.1161903 Occurrence Handle12840035
M Carlile (1982) ArticleTitleProkaryotes and eukaryotes: Strategies and successes Trends Biochem 7 128–130 Occurrence Handle10.1016/0968-0004(82)90199-2 Occurrence Handle1:CAS:528:DyaL38XhvFGmsLw%3D
SA Chervitz L Aravind G Sherlock CA Ball EV Koonin SS Dwight MA Harris K Dolinski S Mohr T Smith S Weng JM Cherry D Botstein (1998) ArticleTitleComparison of the complete protein sets of worm and yeast: Orthology and divergence Science 82 2022–2028 Occurrence Handle10.1126/science.282.5396.2022
C Chothia AM Lesk (1986) ArticleTitleThe relation between the divergence of sequence and structure in proteins EMBO J 5 823–826 Occurrence Handle1:CAS:528:DyaL28XitVals7o%3D Occurrence Handle3709526
C Chothia J Gough C Vogel SA Teichmann (2003) ArticleTitleEvolution of the protein repertoire Science 300 1701–1703 Occurrence Handle10.1126/science.1085371 Occurrence Handle1:CAS:528:DC%2BD3sXksVKisLc%3D Occurrence Handle12805536
RR Copley P Bork (2000) ArticleTitleHomology among (βα)8 barrels: implications for the evolution of metabolic pathways J Mol Biol 303 627–640 Occurrence Handle10.1006/jmbi.2000.4152 Occurrence Handle1:CAS:528:DC%2BD3cXotlyitb0%3D Occurrence Handle11054297
RR Copley J Schultz CP Ponting P Bork (1999) ArticleTitleProtein families in multicellular organisms Curr Opin Struct Biol 9 408–415 Occurrence Handle10.1016/S0959-440X(99)80055-4 Occurrence Handle1:CAS:528:DyaK1MXks1Wqs7s%3D Occurrence Handle10361098
AFW Coulson J Moult (2002) ArticleTitleA unifold, mesofold, and superfold model of protein fold use Proteins 46 61–71 Occurrence Handle10.1002/prot.10011 Occurrence Handle1:CAS:528:DC%2BD38XksFahtw%3D%3D Occurrence Handle11746703
V Daubin NA Moran H Ochman (2003) ArticleTitlePhylogenetics and the cohesion of bacterial genomes Science 301 829–832 Occurrence Handle10.1126/science.1086568 Occurrence Handle1:CAS:528:DC%2BD3sXmtVGqs7g%3D Occurrence Handle12907801
J Felsenstein (1985) ArticleTitleConfidence limits on phylogenies: An approach using the bootstrap Evolution 39 783–791
J Felsenstein (2004) Inferring phylogenies Sinauer Associates Sunderland, MA
D Frishman H-W Mewes (1997) ArticleTitleProtein structural classes in five complete genomes Nature Struct Biol 4 626–628 Occurrence Handle10.1038/nsb0897-626 Occurrence Handle1:CAS:528:DyaK2sXltlWgsLY%3D Occurrence Handle9253410
D Frishman K Albermann J Hani K Heumann A Metanomski A Zollner H-W Mewes (2001) ArticleTitleFunctional and structural genomics using PEDANT Bioinformatics 17 44–57 Occurrence Handle10.1093/bioinformatics/17.1.44 Occurrence Handle1:CAS:528:DC%2BD3MXit1Cjsbk%3D Occurrence Handle11222261
S Gavrilets (1997) ArticleTitleEvolution and speciation on holey adaptive landscapes Trends Ecol Evol 12 307–312 Occurrence Handle10.1016/S0169-5347(97)01098-7
M Gerstein (1997) ArticleTitleA structural census of genomes: Comparing bacterial, eukaryotic and archaeal genomes in terms of protein structure J Mol Biol 274 562–576 Occurrence Handle10.1006/jmbi.1997.1412 Occurrence Handle1:CAS:528:DyaK1cXht12nsg%3D%3D Occurrence Handle9417935
M Gerstein (1998) ArticleTitlePatterns of protein-fold usage in eight microbial genomes: A comprehensive structural census Proteins 33 518–534 Occurrence Handle10.1002/(SICI)1097-0134(19981201)33:4<518::AID-PROT5>3.0.CO;2-J Occurrence Handle1:CAS:528:DyaK1cXotVartbo%3D Occurrence Handle9849936
M Gerstein H Hegyi (1998) ArticleTitleComparing genomes in terms of protein structure: Surveys of a finite parts list FEMS Microbiol Rev 22 277–304 Occurrence Handle10.1016/S0168-6445(98)00019-9 Occurrence Handle1:CAS:528:DyaK1cXns1KqtrY%3D Occurrence Handle10357579
M Gerstein M Levitt (1997) ArticleTitleA structural census of the current population of protein sequences Proc Natl Acad Sci USA 94 11911–11916 Occurrence Handle10.1073/pnas.94.22.11911 Occurrence Handle1:CAS:528:DyaK2sXntFSms7o%3D Occurrence Handle9342336
N Glansdorff (2000) ArticleTitleAbout the last common ancestor, the universal life-tree and lateral gene transfer: A reappraisal Mol Microbiol 38 177–185 Occurrence Handle10.1046/j.1365-2958.2000.02126.x Occurrence Handle1:CAS:528:DC%2BD3cXotVGqtrc%3D Occurrence Handle11069646
JP Gogarten WF Doolittle JG Lawrence (2002) ArticleTitleProkaryotic evolution in light of gene transfer Mol Biol Evol 19 2226–2238 Occurrence Handle1:CAS:528:DC%2BD38Xps12hsL4%3D Occurrence Handle12446813
A Grant D Lee C Orengo (2004) ArticleTitleProgress towards mapping the universe of protein folds Genome Biol 5 107 Occurrence Handle10.1186/gb-2004-5-5-107 Occurrence Handle15128436
JP Grime (1977) ArticleTitleEvidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory Am Nat 111 1169–1194 Occurrence Handle10.1086/283244
TF Hansen (2003) ArticleTitleIs modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability Biosystems 69 83–94 Occurrence Handle10.1016/S0303-2647(02)00132-6 Occurrence Handle12689723
JK Harris ST Kelley GB Spiegelman NR Pace (2003) ArticleTitleThe genetic core of the universal ancestor Genome Res 13 407–412 Occurrence Handle10.1101/gr.652803 Occurrence Handle1:CAS:528:DC%2BD3sXit1Wgt7w%3D Occurrence Handle12618371
A Harrison F Pearl R Mott J Thornton C Orengo (2002) ArticleTitleQuantifying the similarities within fold space J Mol Biol 323 909–926 Occurrence Handle10.1016/S0022-2836(02)00992-0 Occurrence Handle1:CAS:528:DC%2BD38Xot1Knu7w%3D Occurrence Handle12417203
LH Hartwell JJ Hopfield S Leibler AW Murray (1999) ArticleTitleFrom molecular to modular cell biology Nature 402 C47–C52 Occurrence Handle10.1038/35011540 Occurrence Handle1:CAS:528:DyaK1MXnslKms70%3D Occurrence Handle10591225
H Hegyi J Lin D Greenbaum M Gerstein (2002) ArticleTitleStructural genomics analysis: Characteristics of atypical, common, and horizontally transferred folds Proteins 47 126–141 Occurrence Handle10.1002/prot.10078 Occurrence Handle1:CAS:528:DC%2BD38XislGhur8%3D Occurrence Handle11933060
MA Huynen E Nimwegen Particlevan (1998) ArticleTitleThe frequency distribution of gene family size in complete genomes Mol Biol Evol 15 583–589 Occurrence Handle1:CAS:528:DyaK1cXislensLw%3D Occurrence Handle9580988
GP Karev Y Wolf AY Rzhetsky FS Berezovskaya EV Koonin (2002) ArticleTitleBirth and death of protein domains: A simple model of evolution explains power law behavior BMC Evol Biol 2 18 Occurrence Handle10.1186/1471-2148-2-18 Occurrence Handle12379152
GP Karev Y Wolf EV Koonin (2003) ArticleTitleSimple stochastic birth and death models of genome evolution: Was there enough time for us to evolve? Bioinformatics 19 1889–1990 Occurrence Handle10.1093/bioinformatics/btg351 Occurrence Handle1:CAS:528:DC%2BD3sXot1CrsLc%3D Occurrence Handle14555621
SA Kauffmann (1993) The origins of order Oxford University Press New York
V Kunin CA Ouzounis (2003) ArticleTitleThe balance of driving forces during genome evolution in prokaryotes Genome Res 13 1589–1594 Occurrence Handle10.1101/gr.1092603 Occurrence Handle1:CAS:528:DC%2BD3sXls1Kruro%3D Occurrence Handle12840037
V Kunin I Cases AJ Enright V de Lorenzo CA Ouzounis (2003) ArticleTitleMyriads of protein families, and still counting Genome Biol 4 401 Occurrence Handle10.1186/gb-2003-4-2-401 Occurrence Handle12620116
D Lee A Grant D Buchan C Orengo (2003) ArticleTitleA structural perspective on genome evolution Curr Opin Struct Biol 13 359–369 Occurrence Handle10.1016/S0959-440X(03)00079-4 Occurrence Handle1:CAS:528:DC%2BD3sXkvVKns78%3D Occurrence Handle12831888
E Limpens C Franken P Smit J Willemse T Bisseling R Geurts (2003) ArticleTitleLysM domain receptor kinases regulating rhizobial Nod factor-induced infection Science 302 630–633 Occurrence Handle10.1126/science.1090074 Occurrence Handle1:CAS:528:DC%2BD3sXotlWqt7o%3D Occurrence Handle12947035
J Lin M Gerstein (2000) ArticleTitleWhole-genome trees based on the occurrence of fold and orthologs: Implications for comparing genomes on different levels Genome Res 10 808–818 Occurrence Handle10.1101/gr.10.6.808 Occurrence Handle1:CAS:528:DC%2BD3cXkt1eisLk%3D Occurrence Handle10854412
L Lo Conte SE Brenner TJP Hubbard C Chothia A Murzin (2002) ArticleTitleSCOP database in 2002: Refinements accommodate structural genomics Nucleic Acids Res 30 264–267 Occurrence Handle10.1093/nar/30.1.264 Occurrence Handle1:CAS:528:DC%2BD38Xht12rsr8%3D Occurrence Handle11752311
AN Lupas CP Ponting RB Russell (2001) ArticleTitleOn the evolution of protein folds: Are similar motifs in different protein folds the result of convergence, insertion, or relics of an ancient peptide world? J Struct Biol 134 191–203 Occurrence Handle10.1006/jsbi.2001.4393 Occurrence Handle1:CAS:528:DC%2BD3MXms1alt78%3D Occurrence Handle11551179
M Lynch JS Conery (2000) ArticleTitleThe evolutionary fate and consequences of duplicate genes Science 10 1151–1155 Occurrence Handle10.1126/science.290.5494.1151
M Lynch JS Conery (2003a) ArticleTitleThe evolutionary demography of duplicate genes J Struct Funct Genomics 3 35–44 Occurrence Handle10.1023/A:1022696612931 Occurrence Handle1:CAS:528:DC%2BD3sXhsFKqsLo%3D
M Lynch JS Conery (2003b) ArticleTitleThe origins of genome complexity Science 302 1401–1404 Occurrence Handle10.1126/science.1089370 Occurrence Handle1:CAS:528:DC%2BD3sXptVGjsrs%3D
WP Maddison (1991) ArticleTitleSquared-change parsimony reconstructions of ancestral states for continuous-valued characters on a phylogenetic tree Syst Zool 40 304–314
WP Maddison DR Maddison (1999) MacClade: Analysis of phylogeny and character evolution, version 3.08. Sinauer Associates Sunderland, MA
U Mathesius S Mulders M Gao M Teplitski G Caetano-Anollés BG Rolfe WD Bauer (2003) ArticleTitleExtensive and specific responses of a eukaryote to bacterial quorum sensing signals Proc Natl Acad Sci USA 100 1444–1449 Occurrence Handle10.1073/pnas.262672599 Occurrence Handle1:CAS:528:DC%2BD3sXhtF2nsrY%3D Occurrence Handle12511600
MJ McFall-Ngai (2001) ArticleTitleIdentifying ‘prime suspects’: Symbioses and the evolution of multicellularity Comp Biochem Phys B Biochem Mol Biol 129 711–723 Occurrence Handle10.1016/S1096-4959(01)00406-7 Occurrence Handle1:STN:280:DC%2BD3MznvVyqsw%3D%3D
E Mossell (2003) ArticleTitleOn the impossibility of reconstructing ancestral data and phylogenies J Comp Biol 10 669–678 Occurrence Handle10.1089/106652703322539015
A Murzin SE Brenner T Hubbard C Clothia (1995) ArticleTitleSCOP: A structural classification of proteins for the investigation of sequences and structures J Mol Biol 247 536–540 Occurrence Handle10.1006/jmbi.1995.0159 Occurrence Handle1:CAS:528:DyaK2MXltVGgsr4%3D Occurrence Handle7723011
N Nagano CA Orengo JM Thornton (2002) ArticleTitleOne fold with many functions: The evolutionary relationships between TIM barrel families based on their sequences, structures and functions J Mol Biol 321 741–765 Occurrence Handle10.1016/S0022-2836(02)00649-6 Occurrence Handle1:CAS:528:DC%2BD38Xms1Wmurg%3D Occurrence Handle12206759
S Nee EC Holmes RM May PH Harvey (1994) ArticleTitleExtinction rates can be estimated from molecular phylogenies Phil Trans R Soc Lond B Biol Sci 344 77–82 Occurrence Handle1:STN:280:ByiD3s3js1A%3D
CA Orengo AD Michie S Jones DJ Jones MB Swindells JM Thornton (1997) ArticleTitleCATH: a hierarchic classification of protein domain structures Structure 5 1093–1108 Occurrence Handle10.1016/S0969-2126(97)00260-8 Occurrence Handle1:CAS:528:DyaK2sXmt1Wgs74%3D Occurrence Handle9309224
L Patthy (2003) ArticleTitleModular assembly of genes and the evolution of new functions Genetica 118 217–231 Occurrence Handle10.1023/A:1024182432483 Occurrence Handle1:CAS:528:DC%2BD3sXksFWntLw%3D Occurrence Handle12868611
D Penny MD Hendy AM Poole (2003) ArticleTitleTesting fundamental evolutionary hypotheses J Theor Biol 223 377–385 Occurrence Handle10.1016/S0022-5193(03)00099-7 Occurrence Handle12850457
H Philippe J Laurent (1998) ArticleTitleHow good are deep phylogenetic trees? Curr Opin Genet Dev 8 6161–623 Occurrence Handle10.1016/S0959-437X(98)80028-2
A Poole DC Jeffares D Penny (1998) ArticleTitleThe path from the RNA world J Mol Evol 46 1–17 Occurrence Handle1:CAS:528:DyaK1cXks1WisA%3D%3D Occurrence Handle9419221
J Qian NM Luscombe M Gerstein (2001) ArticleTitleProtein family and fold occurrence in genomes: Power-law behavior and evolutionary model J Mol Biol 313 673–681 Occurrence Handle10.1006/jmbi.2001.5079 Occurrence Handle1:CAS:528:DC%2BD3MXotVyntb8%3D Occurrence Handle11697896
MC Rivera JA Lake (2004) ArticleTitleThe ring of life provides evidence for a genome fusion origin of eukaryotes Nature 431 152–155 Occurrence Handle10.1038/nature02848 Occurrence Handle1:CAS:528:DC%2BD2cXntlGks78%3D Occurrence Handle15356622
A Rokas PWK Holland (2000) ArticleTitleRare genomic changes as a tool for phylogenetics Trends Ecol Evol 15 454–459 Occurrence Handle11050348
A Rzhetsky SM Gomez (2001) ArticleTitleBirth of scale-free molecular networks and the number of distinct DNA and protein domains per genome Bioinformatics 17 988–996 Occurrence Handle10.1093/bioinformatics/17.10.988 Occurrence Handle1:CAS:528:DC%2BD3MXot1Gnsr4%3D Occurrence Handle11673244
C Semple M Steel (2002) ArticleTitleTree reconstruction from multi-state characters Adv Appl Math 28 169–184 Occurrence Handle10.1006/aama.2001.0772
B Snel P Bork MA Huynen (2002) ArticleTitleGenomes in flux: The evolution of Archaeal and Proteobacterial gene content Genome Res 12 17–25 Occurrence Handle10.1101/gr.176501 Occurrence Handle1:CAS:528:DC%2BD38XksV2rsQ%3D%3D Occurrence Handle11779827
E Sober M Steel (2002) ArticleTitleTesting the hypothesis of common ancestry J Theor Biol 218 395–408 Occurrence Handle12384044
M Steel D Penny (2000) ArticleTitleParsimony, likelihood, and the role of models in molecular phylogenetics Mol Biol Evol 17 839–850 Occurrence Handle1:CAS:528:DC%2BD3cXjvFygsrY%3D Occurrence Handle10833190
DL Swofford (1999) Phylogenetic analysis using parsimony and other programs (PAUP*), version 4. Sinauer Associates Sunderland, MA
DL Swofford WP Maddison (1987) ArticleTitleReconstructing ancestral character states under Wagner parsimony Math Biosci 87 199–229 Occurrence Handle10.1016/0025-5564(87)90074-5
K Thiele (1993) ArticleTitleThe holy grail of the perfect character: The cladistic treatment of morphometric data Cladistics 9 275–304 Occurrence Handle10.1006/clad.1993.1020
JL Thorley RDM Page (2000) ArticleTitleRadCon: phylogenetic tree comparison and consensus Bioinformatics 16 486–487 Occurrence Handle10.1093/bioinformatics/16.5.486 Occurrence Handle1:CAS:528:DC%2BD3cXlvVKqt7c%3D Occurrence Handle10871273
SH White (1994) ArticleTitleGlobal statistics of protein sequences: implications for the origin, evolution, and prediction of structure Annu Rev Biophys Biomol Struct 23 407–439 Occurrence Handle10.1146/annurev.bb.23.060194.002203 Occurrence Handle1:CAS:528:DyaK2MXitVartA%3D%3D Occurrence Handle7919788
M Wilkinson JL Thorley P Upchurch (2000) ArticleTitleA chain is no longer than its weakest link: double decay analysis of phylogenetic hypotheses Syst Biol 49 754–776 Occurrence Handle10.1080/106351500750049815 Occurrence Handle1:STN:280:DC%2BD38zntVOisw%3D%3D Occurrence Handle12116438
CR Woese (2000) ArticleTitleThe universal ancestor Proc Natl Acad Sci USA 95 6854–6859 Occurrence Handle10.1073/pnas.95.12.6854
CR Woese O Kandler ML Wheelis (1990) ArticleTitleTowards a natural system of organisms: Proposal for the domains Archaea, Bacteria, and Eucarya Proc Natl Acad Sci USA 87 4576–4579 Occurrence Handle1:STN:280:By%2BB1c%2FktFw%3D Occurrence Handle2112744
YI Wolf SE Brenner PA Bash EV Koonin (1999) ArticleTitleDistribution of protein folds in the three superkingdoms of life Genome Res 9 17–26 Occurrence Handle1:CAS:528:DyaK1MXhtVWltLc%3D Occurrence Handle9927481
YI Wolf IB Rogozin NV Grishin EV Koonin (2002) ArticleTitleGenome trees and the tree of life Trends Genet 18 472–479 Occurrence Handle10.1016/S0168-9525(02)02744-0 Occurrence Handle1:CAS:528:DC%2BD38Xmtleltbo%3D Occurrence Handle12175808
S Wright (1932) ArticleTitleThe roles of mutation, inbreeding, crossbreeding and selection in evolution Proc Sixth Int Congr Genet 1 356–366
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We would like to thank Jay Mittenthal (University of Illinois) and Dietz Bauer (Ohio State University) for valuable comments and suggestions.
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Caetano-Anollés, G., Caetano-Anollés, D. Universal Sharing Patterns in Proteomes and Evolution of Protein Fold Architecture and Life. J Mol Evol 60, 484–498 (2005). https://doi.org/10.1007/s00239-004-0221-6
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DOI: https://doi.org/10.1007/s00239-004-0221-6