Genomics Meets Phylogenetics - Auckland

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P1: FHAJuly 27, 200012:44Annual ReviewsAR104-03Annu. Rev. Genomics Hum. Genet. 2000. 01:41–73Copyright c 2000 by Annual Reviews. All rights reservedGENE FAMILY EVOLUTION AND HOMOLOGY:Genomics Meets PhylogeneticsJoseph W. Thornton1 and Rob DeSalle21Department of Biological Sciences and Center for Environmental Researchand Conservation, Columbia University, New York, New York 10027;e-mail: jt121@columbia.edu;2Division of Invertebrates, American Museum of Natural History, New York,New York 10024; e-mail: desalle@amnh.orgKey Words orthology; gene duplication; exon shuffling; lateral gene transfer;concerted evolution; molecular evolution; maximum likelihood; parsimony;evolution of novelty Abstract With the advent of high-throughput DNA sequencing and wholegenome analysis, it has become clear that the coding portions of the genome areorganized hierarchically in gene families and superfamilies. Because the hierarchyof genes, like that of living organisms, reflects an ancient and continuing process ofgene duplication and divergence, many of the conceptual and analytical tools used inphylogenetic systematics can and should be used in comparative genomics. Phylogenetic principles and techniques for assessing homology, inferring relationships amonggenes, and reconstructing evolutionary events provide a powerful way to interpret theever increasing body of sequence data. In this review, we outline the application ofphylogenetic approaches to comparative genomics, beginning with the inference ofphylogeny and the assessment of gene orthology and paralogy. We also show how thephylogenetic approach makes possible novel kinds of comparative analysis, includingdetection of domain shuffling and lateral gene transfer, reconstruction of the evolutionary diversification of gene families, tracing of evolutionary change in protein functionat the amino acid level, and prediction of structure-function relationships. A marriageof the principles of phylogenetic systematics with the copious data generated by genomics promises unprecedented insights into the nature of biological organization andthe historical processes that created it.A PHYLOGENETIC APPROACH TO GENE FAMILIESBurgeoning DNA sequence data have made clear that the coding portionsof the genome are organized hierarchically into families and superfamilies. (Traditionally, a gene family has been defined as a group of genes all of whose membershave 50% pairwise amino acid similarity, and a superfamily as an alignablegroup of genes with similarity below this threshold (48); in this review, we use1527-8204/00/0728-0041 14.0041

P1: FHAJuly 27, 20004212:44Annual ReviewsTHORNTON AR104-03DESALLEthe term “gene family” to encompass both types of groups.) Of the genes in thebacterium Escherichia coli, 50% are members of identified gene families (63),and the proportion of gene family members in eukaryotes may be in the same rangeor even higher (16, 108). The hierarchy of genes, like the nested organization ofliving organisms, has been produced primarily by processes of lineage splitting(gene duplication) and divergence (48, 89), so the concepts and analytical toolsused in phylogenetic systematics are also applicable for reconstructing the evolutionary relationships among genes in genomes. Just as these techniques allowthe overwhelming diversity of taxa in nature to be systematized into a concise andhistorically meaningful conceptual framework, they represent a powerful way toorganize and interpret the ever-increasing body of gene sequence data.Comparative biological analysis can be carried out only in the context of aphylogeny (49, 85). A sound classification of gene family relationships is thereforea prerequisite for virtually all types of inference about the evolution of genesand the proteins for which they code. With a reliable gene phylogeny in hand,we can predict the structure and function of uncharacterized proteins, infer themechanisms by which new genes appeared and took on novel functions, reconstructthe biochemical pathways and gene complements of ancestral organisms, analyzecoevolutionary relationships and dynamics among proteins, and understand linksbetween genomic change and morphological innovation (63, 64).Despite the power of phylogenetics for comparative analysis, a clear understanding of its principles has been lacking from most studies of genomes and genefamilies. Our purpose in this review is to present phylogenetic principles and techniques as they can be applied to issues in comparative genomics and to highlightconcerns about several widely used approaches that conflict with these principles.We cannot hope to present all of the voluminous literature on gene family evolutionand comparative genomics; instead, we cite those works that we believe exemplifythe opportunities and hazards of the various modes of inference that are available toresearchers in the field. In this section, we review the fundamentals, strengths, andweaknesses of the major approaches to tree building and evolutionary inference.ParsimonyThe goal of the phylogenetic approach to gene families is to recover the nestedhierarchy of relationships among genes and test hypotheses about the evolutionaryprocess, based on the hierarchical distribution of amino acid or nucleotide characters in DNA or protein sequences. Although phylogenetic methods—cladisticparsimony in particular—have not been dominant in the field of gene family studiesand comparative genomics (but see 2, 17, 47, 92, 93, 104, 114, 124), their advantages vis-à-vis the more popular similarity-based (phenetic) approach are compelling. Since the 1980s, biological systematists have widely accepted the superiority of phylogenetic to phenetic methods, for both theoretical and practical reasons(29, 30, 58, 119); today, virtually no systematist would classify taxa by quantitativemeasures of pairwise similarity. The new field of comparative genomics should,

P1: FHAJuly 27, 200012:44Annual ReviewsAR104-03GENOMICS MEETS PHYLOGENETICS43in our view, take account of the conceptual foundations, practical experience, andtechnical tools developed by systematics researchers over the last several decades.The central assumption of phylogenetic systematics is no less valid for genes ina superfamily than it is for species in a genus: If genes have evolved by duplicationand divergence from common ancestors, the genes will exist in a nested hierarchyof relatedness, and these relations will be manifest in a hierarchical distributionof shared derived characters (synapomorphies) in the gene sequences (30, 52). Onthis theoretical foundation, the most parsimonious gene family tree—the one withthe fewest parallel and reverse character changes—is the phylogenetic hypothesisthat best explains the distribution of shared character states as the result of commoninheritance.Parsimony methods can be computationally demanding. To find the most parsimonious tree, the number of amino acid or nucleotide changes required by everypossible topology must be calculated. As the number of genes or taxa (T ) in theanalysis increases, the number of possible unrooted treesPincreases in faster-thanT(2i 5) (33). Withexponential fashion, according to the formula N (T ) i 3just 10 genes, the number of trees is 2 million, and it exceeds 8 trillion with15 genes, so exhaustive searches are not possible for most gene families. Veryefficient heuristic strategies have been developed, however, to evaluate huge numbers of topologies and explore tree space without becoming trapped in nonoptimal“islands” (88, 118). These algorithms, along with fast computers, have made parsimony analyses of hundreds of genes tractable, with reasonable confidence thatthe most parsimonious tree has been found (101, 113).The major concern about parsimony methods is that they can be unreliablewhen applied to certain combinations of grossly unequal branch lengths. Sequencesthat have diverged greatly from each other, due to rapid evolutionary rates or verylong periods of time, can become saturated with changes, resulting in similarityat some portion of sites by chance alone. When two sequences at the end ofsuch “long branches” are combined with other sequences that are not saturated,shared character states produced by saturation may cause the first two sequences togroup together, even if they are not closely related (32, 51). To avoid this problem,care must be taken to avoid anciently diverged sequences or characters that havenot been subject to strong selection (such as third positions of protein-codingDNA sequences), to break up long branches with denser taxon sampling, andto use amino acid characters—which are less saturable than nucleotides—whenlong-branch attraction might be a concern. With the exception of this generallycorrectable problem, parsimony methods provide a phylogenetic technique thatcan be applied in a wide variety of circumstances with a minimum of assumptions.PheneticsFundamentally different from the parsimony framework are phenetic approaches,which classify genes or proteins based on a single quantitative measure of pairwisesimilarity. These metrics represent the observed or corrected fraction of amino

P1: FHAJuly 27, 20004412:44Annual ReviewsTHORNTON AR104-03DESALLEacids or nucleotides that are identical between two aligned sequences. Trees areconstructed by assuming that more similar genes shared a common ancestor morerecently than less similar genes.Methods of this type, such as the neighbor-joining, unweighted-pair-group(UPGMA), minimum-evolution, and Fitch-Margoliash techniques, have beendominant in studies of gene family phylogeny to date (examples include 4, 7–9,11, 16, 18, 70, 106). Reliance on phenetic methods has become particularly acuteas whole-genome sequences have become available: numerous computationallysophisticated informatics techniques, all based on phenetic criteria, have been implemented with the stated goal of recovering evolutionary and functional relationships among genes in genomes (16, 54, 64, 83). For example, the influential clustersof orthologous groups (COG) method for establishing gene orthology (121) andseveral recent proposals to predict protein function and interactions from wholegenome sequences (26, 76, 77, 97) all rely on pairwise similarity scores found inBasic Local Alignment Search Tool (BLAST; National Center for BiotechnologyInformation, Bethesda, MD) searches or other intergenomic comparisons.Phenetic comparisons have the advantage of computational efficiency. Mostsuch methods are algorithms for constructing a single tree rather than evaluatinga large ensemble of possible topologies, using an optimality criterion, so they canrapidly produce a similarity-based tree from very large numbers of sequences. Thisis an important advantage when very large numbers of genes are being evaluated,as is often the case in comparative genomics.But there are conditions under which phenetic approaches to tree building failto recover evolutionary relationships, and these occur with some frequency inthe evolution of gene families. Ohno’s model of gene duplication predicts thatnew genes diverge rapidly after their duplication because of the relaxed selection pressures caused by functional redundancy (assuming that a higher “dose”of the gene product gives no selective advantage); if the copy takes on a newfunction, evolutionary rates are then expected to slow considerably as new selective constraints are imposed (89). An alternative model—the subfunctionalizationhypothesis—proposes that, after duplication of a gene with multiple functions,both of the resulting paralogs diverge in sequence until the capacities of the ancestral gene product are gradually allocated between its descendants, at which timeselection constrains further sequence change (39). Both views are consistent withthe extreme sequence divergence of nonfunctional pseudogenes, the intermediatedegree of divergence among paralogous genes with different functions, and the lowdivergence among orthologous sequences that have identical functions (74, 125).Whenever either of these processes holds, the following distance-based approacheswill be inappropriate for gene family reconstruction:1. UPGMA- and BLAST-based methods assume that divergence rates areidentical in all lineages, which is often not the case in gene familyevolution. For example, actin genes have evolved much more quickly incertain sea urchin lineages than in other taxa (61), and divergence rates

P1: FHAJuly 27, 200012:44Annual ReviewsAR104-03GENOMICS MEETS PHYLOGENETICS45among paralogs in the nuclear receptor superfamily vary substantially(69, 124). When rates are variable, these methods will yield inaccuratephylogenies (74).2. The pairwise similarity scores on which all phenetic techniques relyinclude not only phylogenetically informative synapomorphies but alsoshared ancestral characters (symplesiomorphies) and unique derived ones(autapomorphies). As a result, distances between closely relatedfast-evolving sequences—recently diverged paralogs, for instance—will beinflated by autapomorphies, and these methods will fail to recover theserelationships. In turn, they will cluster slowly evolving sequences (such asanciently diverged orthologs with a conserved function) together, evenwhen they are distantly related, because such sequences retainsymplesiomorphies that reduce the pairwise distance between them(Figure 1; see color insert).3. The neighbor-joining technique for tree construction and theminimum-evolution and Fitch-Margoliash methods for tree evaluation areless subject to distortion by unequal rates. They can recover evolutionaryrelationships, however, only when pairwise distances between genes areadditive—that is, when distances between any pair of sequences are equalto the sum of the distances on the branches that connect them to theircommon ancestor. This assumption is often violated when sequences aresubject to multiple changes at the same amino acid or nucleotide site.There are methods to correct for multiple changes, but they are not reliablewhen the frequency of multiple hits varies among sites or is higher in somelineages than in others (48). In gene families in which paralogs diverge atdifferent rates, multiple hits are more likely in some genes than in others.Multiple hits are also more likely on the deep internal branches of a treeimmediately after gene duplication events, when new paralogs exploresequence space more freely than they do after selection constrains theirnew or allocated functions more narrowly. And in any coding sequence,multiple hits are more likely at sites that are not critical to conservedaspects of function than at those subject to stronger selective constraints.4. All phenetic methods require that distances between sequences beaccurately calculated, a condition that can be difficult to satisfy when thedifferences between sequences are very large, sequences are short, ordivergence rates vary substantially among sites in the sequence (74). All ofthese problems can occur in gene family reconstruction. Distances areoften great, reflecting the fact that paralogous genes in many gene familieshave diverged considerably since their duplication hundreds of millions ofyears ago. These paralogs often contain only relatively short regions thatare conserved enough to allow multiple alignment, making distancecalculations subject to considerable error. And, as noted above, rates oftenvary considerably among positions in functional proteins.

P1: FHAJuly 27, 20004612:44Annual ReviewsTHORNTON AR104-03DESALLEFigure 1 Cladistic and phenetic reconstructions of a gene family phylogeny. A. Evolutionary scenario of gene duplications (marked with dark circles) and cladogenesis (unmarkednodes) that generates a hypothetical gene family. Each group of colored branches leads toa group of similar orthologs. B. Species tree for the process in A. C. Gene phylogeny forthe same process, correctly inferred by using the parsimony criterion. D. UPGMA tree forthe process in A, assuming no homoplasy and 10-fold–higher rates of sequence change onbranches on which gene duplications lead to new paralogous genes (marked with horizontalbars on the phylogeny in C) than on branches leading to conserved orthologs. This tree doesnot accurately represent evolutionary relationships.

P1: FHAJuly 27, 200012:44Annual ReviewsAR104-03GENOMICS MEETS PHYLOGENETICS47For all of these reasons, pairwise distance methods cannot be relied on toaccurately reconstruct evolutionary relationships among gene family members.Even under conditions that do not violate the assumptions of phenetic techniques,parsimony has two additional advantages over phenetics. First, distance methodscollapse character information into a single quantitative measure of similarity.By preserving the information in individual amino acid or nucleic acid states,parsimony methods make possible a detailed examination of the processes bywhich molecular characters evolved and brought about novel aspects of proteinfunction. Second, phenetic methods have a tendency to create a false sense ofcertainty. If the data in fact offer equal support to a number of topologies, thosedistance methods that are algorithms for tree construction rather than evaluation(neighbor joining and UPGMA in particular) will present just one phylogeny asthe “true” tree. In contrast, the cladistic approach evaluates many trees by usingthe parsimony criterion, and it allows the degree of support for any phylogenetichypothesis to be evaluated relative to others. When several topologies are equallysupported, all can be presented as most parsimonious trees, avoiding the arbitraryresolution of phenetic methods.Maximum LikelihoodMaximum likelihood (ML) is a third method for phylogenetic inference, the advantages and disadvantages of which continue to be debated (56, 109). This technique(34; reviewed in 82) selects the tree that, given an explicit model of sequence evolution, is most likely to have generated the sequence data observed. ML is usefulbecause it is not subject to long-branch attraction, and it can take advantage ofany generalizable knowledge about the patterns and dynamics of sequence evolution (119). ML algorithms are considerably more computationally demandingthan even parsimony analyses, so reasonably thorough heuristic searches for theML tree may become intractable before the number of orthologs and paralogs necessary for most gene family analyses is reached. Eventually, as computer speedscontinue to increase, this limitation is likely to be overcome.The reliability of ML methods depends on the realism of the evolutionarymodel; the tree that maximizes the likelihood of the data under an incorrect modelof sequence evolution will not necessarily be the ML tree under a different and moreaccurate set of assumptions. Models of amino acid and codon evolution are not aswell developed or validated as those for noncoding nucleotide sequences, and noneadequately account for the nonindependence of sites in a protein or the fact that theprobability of change from one type of amino acid to another is likely to be differentand not necessarily predictable at different sites in the protein (see 82). Indeed,there are fundamental questions about whether any system that models proteinevolution as a site-by-site probabilistic process can ever adequately capture thepatterns produced when complex and nonlinear selection pressures act on threedimensional protein conformations in ways that vary among sites and lineages.For example, the transformation frequencies that characterize the probability of

P1: FHAJuly 27, 20004812:44Annual ReviewsTHORNTON AR104-03DESALLEchange from one amino acid to another are likely to be different at various sites inthe protein, depending on whether side-chain volume, hydrophobicity, electrostaticpotential, or ability to form disulfide bonds is the primary selective parameter atthat site. Furthermore, the transformation frequencies at any one amino acid siteare likely to be different for each paralog in a family, if paralogs bind to differentligands or cofactors, or if they display slight differences in folding that bringdifferent residues into contact with each other (e.g. 120).The degree to which likelihood methods are robust to these violations of theirmodels’ assumptions is unknown. The reliability of ML for reconstruction of relationships among coding sequences—particularly those in gene families—is thuscurrently in question, and cladistic parsimony remains for now the most useful andtheoretically sound approach to inferring gene family phylogenies. As we discussbelow, however, once a phylogeny is generated by using parsimony, the statisticalnature of ML makes it a useful method for testing specific evolutionary hypotheses, such as those concerning dates of gene duplications or rates of sequence divergence (102).HOMOLOGY, PARALOGY, AND ORTHOLOGYHomology vs SimilarityHomology is the central concept in comparative and evolutionary biology (85).Meaningful biological comparisons must contrapose entities that are different versions of the same thing, and it is precisely this form of sameness that the termhomology is intended to capture. Since Darwin, whether characteristics of organisms are “versions of the same thing” has been a matter of evolutionary history.In the classic phylogenetic definition, homology means “derived from an equivalent characteristic of the common ancestor” (78). The vertebrae of mice and teleostfish are homologs because the two structures descended consistently from thevertebrae of their common ancestor 400 million years ago. Homology is the opposite of analogy, which describes the relationship among features that are similarbecause of convergent or parallel evolution rather than common descent: the wingsof birds and of bats are analogous, because their common ancestor had no wings.Characters can therefore be similar without being homologous, and they can behomologous without being identical.In 1987, an eminent group of biologists pointed out a fundamental differencebetween homology and similarity (100): sequences can be more or less similar,but homology is a strictly either-or proposition. Thus, if proteins X and Y haveidentical amino acids at 30 out of 40 aligned sites, we can say that they are 75%similar, but it is meaningless to say that they are 75% homologous. While mostjournals in systematics and evolutionary biology now attempt to maintain thecorrect terminology, our survey of three major molecular biology journals—Cell,

P1: FHAJuly 27, 200012:44Annual ReviewsAR104-03GENOMICS MEETS PHYLOGENETICS49Development, and the EMBO Journal—for the most recent year indicates that thisimproper conflation of homology and pairwise percent similarity continues to beused in 50% of papers in which gene sequences are compared.Homology says absolutely nothing about similarity of function. Unrelated proteins have been shown to converge to serve identical functions—a phenomenoncalled nonorthologous gene displacement—demonstrating that functional similarity can be analogous rather than homologous (62). Conversely, phylogeneticallyhomologous proteins can diverge to serve subtly or grossly different purposes indifferent organisms, as is the case with the FtzF1-alpha gene product that regulates embryonic segmentation in Drosophila melanogaster, and its similar orthologSF-1, which controls the expression of steroidogenic enzymes in vertebrates (124).Orthologs and ParalogsFor sequence data, there are two major kinds of homology (96). Fitch definedorthologs as genes in different genomes that have been created by the splitting oftaxonomic lineages, and paralogs as genes in the same genome created by geneduplication events (36). In the hypothetical case of Figure 1c, gene A in taxon 3and gene A in taxon 4 are orthologs, whereas genes 4C and 4D are paralogs.These categories are analogous to the terms true homology and serial homologyin morphological systematics, where the former refers to the same structure intwo different organisms and the latter refers to structures within one individualthat evolved by repetition of a single feature in an ancestral organism, such assegments, vertebrae, or limbs (96).Distinguishing orthologous from paralogous genes is central to comparativegenomics. It is only orthologs that can be said to be versions of the same gene intwo different organisms, and mistaking a paralog for an ortholog is to follow a redherring in the genome. Indeed, the fundamental activity of comparative genomicsis to track the presence, structural characteristics, function, and map position oforthologs in multiple genomes. Orthology identification must be accurate for thesetypes of inference to be reliable.There are fundamental problems with the ways that orthologs are currentlyidentified in comparative genomics, which almost always involve finding the mostsimilar pairs of genes between genomes based on pairwise similarity (16, 62, 63)The COG approach, for example, considers gene X from species 1 and gene Yfrom species 2 to be orthologs if X has a higher percentage of similarity to Y thanto any other gene from species 2 and vice versa. Consider the relationship of 3Cand 4C in Figure 1c. The COG framework would call these two genes orthologs,based on their close and unsurpassed sequence similarity. But homology is bydefinition a phylogenetic relationship, not a phenetic one. In a phylogenetic sense,4C is no more closely related to 3C than 4D is; 4C and 4D are equally orthologousto 3C, as reflected in the common ancestry of 3C with 4C and with 4D at the samenode on the tree. This problem affects the orthology not only of recent paralogs

P1: FHAJuly 27, 20005012:44Annual ReviewsTHORNTON AR104-03DESALLEbut also of ancestral genes. In the COG framework, gene A in the stem species 1would be considered an ortholog of the other As in the tree and a paralog of allother members of the gene family. In fact, 1A is equally related phylogeneticallyto every other member of the gene family in the analysis. This ambiguity remainsunresolved no matter how similar the sequence of 1A is to the other As or howdifferent it is from all the Bs, Cs, and Ds on the tree.Orthology as defined in most comparative genomic frameworks is thus an inappropriately phenetic concept. Choosing the one most similar gene out of severalphylogenetic orthologs is not unreasonable to make functional predictions; if gene4C is very similar in sequence to gene 3C, but its paralog 4D (which is equallyorthologous to 3C) has diverged considerably, then it is likely that 4C and 3Cshare a conserved function (63, 99, 121). But for reasoning about the evolutionary process, orthology based on phenetic similarity will lead to false conclusions,because other unrecognized orthologs may be lurking elsewhere in the genome.Phenetic orthology, for example, has been the foundation of most comparativemapping exercises (15, 84), but there is no reason that the conserved member of aduplicated pair—rather than the more divergent one—must occupy the same mapposition as the ancestral gene. Similarly, the presence of phenetic orthologs in pairsof distantly related organisms and their absence from more closely related oneshas been used to infer lateral gene transfer among taxonomic lineages (6, 86), butthe presence of unrecognized orthologs has the potential to explain these patternswithout invoking horizontal transfer. In addition, reports that attempt to reconstructthe minimal protein sets of ancestral organisms based on the presence or absenceof phenetic orthologs in descendant species (62, 66) will omit true members ofthat set whenever less similar orthologs are not recognized. Genes that do not formmonophyletic groups of orthologs but are closest to each other in a phenetic senseshould be called phenologs, not orthologs, and phenology should not be mistakenfor true orthology in the evolutionary sense.Homology as HypothesisIn a phylogenetic context, a statement that two features or genes are homologous isnot an observed fact but a hypothesis about the evolution of characters, which mustbe evaluated in the context of a phylogenetic tree (1, 96, 100, 122). The processof assessing homology for morphological features has multiple stages (12, 20).First, a hypothesis of homology (a primary homology statement) is formulatedbased on such criteria as topographical location on the organism and similarity ofthe character state. Second, information from all available characters is used toinfer a phylogeny and evaluate whether the character is analogous or homologous(a secondary homology statement). This second stage requires more taxa (or genes)and characters than the ones being evaluated for homology, because the test of thehomology hypothesis is based on the congruence of this feature with a body of otherphylogenetically informative characters. At minimum, we require the two taxa orsequences that contain the character being evaluated, an outgroup to polarize the

P1: FHAJuly 27, 200012:44Annual ReviewsAR104-03GENOMICS MEETS PHYLOGENETICS51Figure 2 Homology assessment must include more characters and taxa than those beingtested. By definition, the hypothesis that state A for character number 1 (red) is homologousin species A and B (blue) implies that the common ancestor of A and B (blue circle) hadstate A. Testing this hypothesis requires enough taxa to support the reconstruction that theancestor did not have state A under at least some combinations of character states and enoughcharacters to resolve the phylogeny of these taxa. Each tree shows the most parsimoniousphylogeny for the data given, with the most parsimonious reconstruction of state changesfor character 1. Gain of the state A is represented as a filled box and losses as open boxes.Trees A and B show that the outcome of homology assessment for character 1 in speciesA and B depends on the state of that characte

concerted evolution; molecular evolution; maximum likelihood; parsimony; evolution of novelty Abstract With the advent of high-throughput DNA sequencing and whole-genome analysis, it has become clear that the coding portions of the genome are organized hierarchically in gene families and superfamilies. Because the hierarchy

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