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PLoS ONE. 2008; 3(10): e3500.
Published online Oct 22, 2008. doi:  10.1371/journal.pone.0003500
PMCID: PMC2567038

Complete Genome Viral Phylogenies Suggests the Concerted Evolution of Regulatory Cores and Accessory Satellites

Keith A. Crandall, Editor

Abstract

We consider the concerted evolution of viral genomes in four families of DNA viruses. Given the high rate of horizontal gene transfer among viruses and their hosts, it is an open question as to how representative particular genes are of the evolutionary history of the complete genome. To address the concerted evolution of viral genes, we compared genomic evolution across four distinct, extant viral families. For all four viral families we constructed DNA-dependent DNA polymerase-based (DdDp) phylogenies and in addition, whole genome sequence, as quantitative descriptions of inter-genome relationships. We found that the history of the polymerase gene was highly predictive of the history of the genome as a whole, which we explain in terms of repeated, co-divergence events of the core DdDp gene accompanied by a number of satellite, accessory genetic loci. We also found that the rate of gene gain in baculovirus and poxviruses proceeds significantly more quickly than the rate of gene loss and that there is convergent acquisition of satellite functions promoting contextual adaptation when distinct viral families infect related hosts. The congruence of the genome and polymerase trees suggests that a large set of viral genes, including polymerase, derive from a phylogenetically conserved core of genes of host origin, secondarily reinforced by gene acquisition from common hosts or co-infecting viruses within the host. A single viral genome can be thought of as a mutualistic network, with the core genes acting as an effective host and the satellite genes as effective symbionts. Larger virus genomes show a greater departure from linkage equilibrium between core and satellites functions.

Introduction

Evidence from complete viral genomes suggests that viruses differ from many other evolving lineages in terms of the quantity of genetic material they appropriate from other organisms as opposed to material they evolve themselves through genetic duplication and expansion events [1], [2], [3], [4]. Extensive gene transfer has lead to two alternative sets of hypotheses for the origins of viruses [5]. One set consists of escaped gene or escaped transcript hypotheses, which hypothesize that viruses are of cellular origin, and that they emerge through a form of retrograde evolution or “devolution”. The best known of these is the Green-Laidlaw hypothesis [6], [7] that views viruses as degenerate organisms. A variant is the Galatea hypothesis [8] that envisages a bacterial nucleoprotein as the viral precursor. The second set of hypotheses treat viruses as relics of primitive forms of life, typically prebiotic, nucleic acid replicators from the RNA world that became co-adapted to primitive, and subsequently, more recent forms of cellular life. It has also been proposed that DNA viruses could have infected RNA cells promoting the transition from the RNA to the DNA world [9], eventually leading to the emergence of eukaryotic cellular organization from prokaryotic cells (i.e., eukaryogenesis) [10]. These theories differ with respect to the antiquity of viruses, their mechanism of origination, and their possible role in shaping cellular diversity. A characteristic of all virus origin theories is that virus genomes are thought of as considerably more fluid than those of eukaryotes and even prokaryotes, with their genetic material frequently swapped in and out during the course of evolution. In this paper we seek to show that the history of a considerable fraction of DNA virus genes can be predicted by the history of their DNA dependent DNA polymerase (DdDp), which can be traced back to host genomes. We suggest that these findings add support to an interpretation of virus evolution based on extensive sampling of host genomes by itinerant viruses. In particular, the DNA evidence suggests that viruses emerge from a core reaction network centered about the DdDp enzyme, followed by a concerted evolution of members of this network responding to a set of variable, cellular environments.

Results

Viral assemblages

Phylogenetic trees for the DdDp and phenograms, which are trees obtained from a complete core-less genome sequence (genomes without DdDP), were obtained through a comparison of 20 complete adenovirus genomes shown in Table 1. All principal groups of adenovirus (Mastadenovirus, Atadenovirus, Aviadenovirus and Siadenovirus) were recovered in all trees used for the assemblage (Figure 1). Table 2 indicates the observed number of codivergence events, events in which DdDp phylogenies and core-less phylogenies share an equivalent branching event. The codivergence values were optimized by means of a heuristic search using the TreeMap application that simultaneously estimates the required duplication, exchange and sorting events (reconstructions not shown) that maximize the number of codivergence events. The probability of the observed number of codivergence events given a suitable null expectation after 10 thousand randomizations of trees in each assemblage, ranged from 0.010 to 0.047 (Table 2). This suggests that the DdDp phylogeny is a strong predictor of the history for a large set of phylogenetically informative satellite genes encoded in the viral genome.

Figure 1
Assemblage of an adenovirus DNA-dependent-DNA polymerases (DdDp or cores) maximum likelihood tree (inferred with phyml) rooted at the Siadenovirus (left) and a phenogram clustered with the neighbor-joining method implemented in the Weighbor program for ...
Table 1
Sources, names, labels and sizes for complete genomes from selected representatives from four DNA virus families.
Table 2
Codivergence events of DdDp (core) and “core-less” genomes (satellite) assemblages for four DNA viral families.

For the 26 genomes in the baculoviruses dataset, the trees shown in Fig. 2 recover the principal associations within this clade (i.e., Group I and II of the NPV and GVs) based on the polyhedrin gene [11], genome content [12] and concatamers of several orthologues [12], [13], and reaffirm the proposed position of the root of the tree [13], [14], [15]. An interesting anomaly is Heliothis zea virus (Hz-1), from the Nudiviridae family, that infects Spodoptera and has a DdDp that is highly divergent from those of Group II NPV and the baculoviruses in general [13], [14], but nevertheless clusters in the “core-less” phylogeny, shown at the right in Figure 2, as a sister taxa of the SfltNPV infecting Spodoptera littura. This suggests that viruses from distinct families can converge to infect closely related hosts. But by and large, the high congruence observed between both DdDp and the tree derived from core-less genomes, suggests that the history of the complete genomes recapitulates that of DdDp during adaptive radiation in insects. The probabilities for the observed codivergence events in any assemblage were less than 0.0012 (Table 2).

Figure 2
Assemblage of a baculovirus DNA-dependent-DNA polymerase (DdDp or cores) maximum likelihood tree rooted at the Heliothis zea nudivirus Hz-1 (left) and a phenogram clustered with the neighbor-joining method implemented in the Weighbor program for baculovirus ...

For the 31 herpesviruses, the DdDp trees showed a good agreement with those obtained from complete genomes. The three subfamilies Alphaherpesvirinae, Betaherpesvirinae and Gammaherpesvirinae were recovered for all trees using all methods, albeit with varying degree of support (data not shown). The agreement between DdDp and complete genomes corresponded to the results of a 46-taxon composite topology based on multiple genes [16]. The separation of the Gammaherpesvirinae into two subgroups, Betaherpesvirinae into two subgroups, and Alphaherpesvirinae into three subgroups agreed with both the 46-taxon composite topology, and a study on the gene content phylogeny of the herpes viruses using clusters of orthologous groups (COGs) of genes [1]. The ictalurid IcHV-1 infecting the Channel catfish has similar gross morphology, limited sequence conservation, and almost the same capsid architecture as that found in herpesvirus [17]. As expected, its DdDp did not nest within the three herpes subfamilies. Nevertheless the ATPase subunit of the DNA packaging enzyme complex (terminase) [18], [19] is likely to have promoted convergence with herpesvirus infecting tetrapods, (shown at the right in Figure 3). In general, as with the baculoviruses, the herpesvirus DdDP is a good predictor for the history of the rest of the genome and the probabilities for the observed number of codivergence events in all assemblages were less than 0.0008 (Table 2).

Figure 3
Assemblage of a herpesvirus DNA-dependent-DNA polymerase (DdDp or cores) maximum likelihood tree rooted with the Ictalurid virus (IcHV-1) infecting the Channel catfish (right) and a phenogram clustered with the neighbor-joining method implemented in the ...

For the 22 poxviruses (Table 1), all core-less genome trees are congruent with the DdDp tree (Figure 4). All methods recovered the separation of insect infecting (entomopox) and vertebrate-infecting (chordopox) groups. The orthopox monophyletic group within the chordopox was in good agreement with a tree constructed with 34 sets of othologous genes [2]. Moreover, the associations recovered within the orthopox are in agreement with that obtained from a set of 92 gene families [2]. Overall, the trees in Fig. 4 support earlier studies conducted with clusters of orthologues and gene content analyses [20], [2], [21]. The most notable discrepancy is the close association of the Electromelia and cowpox viruses to the variola virus as a sister group to the camelpox virus. The trees obtained for the orthopox virus support the hypothesis of infection of peri-domestic animals, allowing for independent cross-species transmission into the human species. Intra-group resolution among orthopox has poor statistical support but tends to agree with a previous study proposing that this group of viruses have had a zoonotic origin [2]. The pox viruses has the largest genome (Table 1) and the best agreement between the histories of core and satellites (Table 2), with the probability of observing the reported number of codivergences less than 0.0001 for all assemblages.

Figure 4
Assemblage of poxvirus trees rooted with the insect-infecting entomopoxvirus and a phenogram clustered with the neighbor-joining method implemented in the Weighbor program for poxvirus “core-less” genomes, i.e., without the DdDp (including ...

The dynamics of gene gain and loss

Our data strongly supports the notion that viral genomes are organized around a core set of genes, such as those encoding the replication machinery. We also investigated the gene accretion process by studying gene-content variation among distinct viral families. We considered the number of gene gain events along a lineage or branch of a phylogeny versus the branch length (considering only those branches that experience gene gain and loss events) for complete genome phylogenies estimated with BlastPhen for both, baculoviruses [15] and poxviruses [2] (Figures 5a and 5b). We found that the rate of gene gain in the poxviruses was around four times the rate of gene gain in the baculoviruses. The rate of gene loss in both of these groups was significantly lower than the rate of gain, and while most losses are concentrated in shorter branch lengths, the loss rate remains effectively independent of the time over which we have been able to estimate branch lengths. The ratio of gain to loss of baculoviruses was approximately 7, whereas the ratio for the poxviruses was around 30. However the variance in these estimates was very large (Figure 5) with coefficients of determination (r2) on the gain regressions of 0.40 for 835 gene-gain events in baculovirus and, 0.55 for 261 gene-gain events in poxvirus. The values were significantly larger than the range of 10−2 for the coefficients of determination of loss regressions, with values of 0.006 for 135 gene-loss events for baculovirus and 0.07for 230 gene-loss events in poxvirus. The results suggest that 50% of the variability observed in gain events was explained by a linear model, while there was no detectable dependency of loss events on branch lengths (i.e., time). Moreover, the positive relationship of the gain event against time, as determined by Pearson's coefficient (r), for log-transformed gain-events versus log-transformed branch lengths was 0.73 for baculovirus and 0.49 for poxvirus, which further supports the idea that gain events are time ordered, and potentially, stereotypically sequential. The asymmetry of gain to loss indicates that for both of these groups, the genomes show a significant net expansion through time, with many new genes fixing in the populations. Interestingly, the insect-infecting entomopox viruses accreted 239 genes and lost only 5 genes along 2 lineages. On the other hand, significant genetic exchange took place along the lineages infecting higher animals (i.e., Chordopox and Orthopox genus) [2].

Figure 5
Gene accretion and loss in the baculovirus (5A and 5B) and poxvirus (5C and 5D).

Discussion

Phenomics sheds a light on DNA virus genome evolution

Extensive comparisons between a whole genome quantitative treatment, or “phenomic” approach to virus genomes, and a detailed sequence alignment approach based on a highly conserved polypeptide (i.e., the DdDp), suggests that un-annotated (FASTA formatted) DNA sequence data from complete viral genomes provides a source of useful information about viral phylogenies at a significantly reduced computational cost [14], [15]. The phenomics approach has an additional benefit of minimizing errors arising from ambiguities in the order of genes. An important result was the strong agreement (i.e., congruence) among the complete genome quantitative measures, or phenetic clusters (phenomics), and the DdDp maximum likelihood phylogenies for all 4 viral families studied. Moreover, this agreement had a strong historical, or homologous component, made evident by the frequent co-divergence of large stretches of the genomes, and a significant convergent component, realized through concerted gene gain events. This degree of agreement can seem surprising when we think of viruses as itinerant regulatory networks [25]. These are networks that expand their functional repertoires as they acquire new host genes along their evolutionary history (either directly from the host or indirectly from co-infecting viruses) and thereby come to increasingly resemble host genomes. We might expect therefore significant convergence among genomes that does not reflect true ancestor-descendent relationships (as in the case of the IcHV-1 to herpesviruses and Hz-1 to baculovirus). For most of the sequence data that we consider, we have observed congruence reflecting conservation at the level of the whole genome.

Given the potential for horizontal gene transfer among viruses and among viruses and host cells, in contrast to the stability of DdDp orthologues shared among viral families [13], [22] and host cells, the congruence becomes an issue in itself. It requires that we rethink the process of viral origination and evolution, outside of the traditional molecular systematics interpretation of patterns of adjacency on trees, which treat large-scale similarity as evidence of inheritance from con-specific ancestors. Here we shall argue that large-scale genomic similarity among viruses derives in part from homology among the repertoire of host genomes. This has two components: a core component, reflecting an early escape of the core, conserved polymerase gene from one of two host lineages, and a satellite component, reflecting the later concerted evolution of viruses in common host environments with an overlap in the pattern of genetic accretion. What is significant is that the accretion of satellite functions remains largely congruent with the polymerase core, and thus tends to retain the “true” phylogenetic signature as revealed by the core. This is in contrast to the expectation for convergence, which is typically thought to disrupt historical signatures by virtue of the diversity of the pool of potential, host environments.

In this work, we focused on DNA viruses because they use 2 types of DdDp (A and B) of known cellular origin [13], [22] and they range several orders of magnitude in genome size (from parvovirus with few thousands of base pairs to giant algae viruses with millions base pairs), which makes the process of gene accretion very interesting to investigate. On the other hand, RNA viruses (with the exception of retroviruses) possess an RdRp not shared with the cell and their genomes are limited to few tens of thousand base pairs (the largest being the coronaviruses with 30 Kbp genomes), which often can be aligned allowing conventional phylogenetic analysis to be performed without the need for “alignment-free” methods.

Origins of polymerase and congruent genomes

The adenoviruses have a Type A DdDp, which is related to the polymerase found in the bacteriophages, which suggests a prokaryotic origin for this group. On the other hand, the large DNA viruses from the remaining three families presented in this study have Type B DdDp, with orthologues in eukaryotic cells [13], [22]. The question naturally arises whether the evolutionary history of DdDp is representative of the behavior of a large set of genes in the genome or whether it is anomalous. It is possible after all that viruses have engaged in core-exchanges, promoting a false homology for the majority of genes in the genome. The strong agreement we observed between the sequence-based approach and the whole genome, phenomics approach, supported the idea that a large set of genes in each of the virus lineages have had congruent phylogenetic histories. By clustering all 99 genomes in this study with additional representatives of other DNA families (data not shown), it was observed that Type A and Type B DdDp families separate into distinct clusters, further establishing the role of the DdDp as a major discriminatory gene among viruses. Moreover, the adenovirus had the worst agreement of core and satellite phylogenies, whereas the largest genomes (poxviruses) had the best agreement. The fit of the average genome size (Table 1) to the codivergence probability values (Table 2) for all four viral families had a significant negative dependence (r2 = 0.865, linear, 0.994 exponential and, 0.971 power) which might indicate that smaller genomes experience reduced epistasis and thereby have a greater capacity to accrete novel gene functions.

Considering genomics cores and satellites as hosts and symbionts

We have examined congruence among polymerase-based trees and core-free, whole genome trees. A significant proportion of correlated branching patterns in these trees can be attributed to a shared evolutionary history–codivergence events. However, there are many, non-codivergence events (duplications, exchanges or switching, and sorting) required to maximize the incidence of co-divergences between the core and its satellites (when maximizing similarity through common descent). These could always indicate noise resulting from systematic errors in inference, but the high bootstrap values on the DdDp trees suggests otherwise. These events are perhaps better understood as a measure of gene flux in the viral GRN. If we assume that the DdDp is the crucial organizing component of the virus genome, we might think of satellite genes as essentially commensal or mutualistic components in relationship to the core. Thus a single genome, when significantly fluid, might be thought of as a mutualistic network. When considering the relationship of hosts and symbionts, co-divergence reflects a shared evolutionary history. When hosts undergo speciation with their symbionts, their reconstructed trees will tend to be congruent. However, symbionts can also gain access to new hosts by switching among them. This promotes a departure from congruence. It is also possible that when a host that is infected with multiple strains of symbionts undergoes speciation, that only a single symbiont is able to infect daughter species. Thereby only one lineage is preserved during sorting. Similarly if symbionts are duplicated and diverge within a host, and subsequently associate differentially with daughter species, this will promote departure from congruence. Incongruity between the core and the satellites could arise through all of these processes, but the identification of the effective host and effective symbiont, is a little less clear. Switching, sorting and duplication could refer equally to variation in the core and the satellites. New genes can accrete around an old core, or a core could displace another core and occupy the satellite niche of that core. These alternative accounts for incongruence can only be discriminated by examining the history of each lineage in detail. However, DdDp exchange among viruses is expected to be more common among closely related lineages, because as viruses diverge, the exchange of unrelated DdDp would require the concerted exchange of a number of cofactors. For this reason we might think of the core as the effective genomic host. Without considering the detailed histories, we can derive an approximate measure for genome flux at a coarse grain, by calculating the ratio of codivergence events (Cd) to all possible events (codivergence, sorting, duplication and switching) added together (nCd) for a viral group. We observed that the ratio Cd/nCd of codivergence (Cd) to non-codivergence (nCd) events was strongly related to the average viral genome size across the viral families (r2 = 0.84) (Figure 6). Hence larger genomes tend to show stronger linkage disequilibrium between the core and its satellites. This suggests stronger dependencies, or epistasis, between the core and the satellite as the number of functions increases.

Figure 6
Plot showing a positive correlation (r2 = 0.84) between the average genome in each viral family versus the ratio (Cd/nCd) of codivergence (Cd) over non-codivergence (nCd) events.

Core and satellite functions

The hypothesis of a conserved, co-evolving set of genes derives additional support from gene content studies on DNA viruses. For example, baculovirus gene content studies show that a core set of 30 genes are shared among baculovirus, some of which, such as helicase ac96, 38K(ac96) maintain similar relative positions in the genomes [4]. One third (10) of these conserved genes are involved either in replication (4) or transcription (6), whereas many others are an assortment of structural proteins (9), and auxiliary (1) or unknown function (10). This indicates that most conserved functions shared among known baculovirus are related to the replicative/multiplicative core. Our whole genome trees (data not shown) agree with previous phylogenies of the baculovirus based on single genes [11] or with clusters of orthologues [4], [13]. Likewise, in the poxviruses, a core set of 34 genes involved in replication and viral assembly were found to be present in the 20 genomes studied. The orthopox genomes have an additional set of 52-shared genes [2]. In the latter study, it was proposed that poxvirus genomic evolution takes place under heterogeneous rates of gene gain and loss along distinct lineages, whereas a core set of 34 genes defines the functional or metabolic identity of the virus family. In the case of the herpes virus, the core set of genes has been studied in detail. Like baculoviruses and poxviruses, the largest core set of genes: 57% of the 14 COGs including all herpes genomes (excluding the ictalurid herpesvirus) are related to DNA metabolism [1]. An additional study on the frequency distribution of distinct functional classes of 19 herpes genomes suggests that most of the core sets of shared genes are involved in nucleotide metabolism and DNA repair, structural support (capsid and tegument) and replication [23]. In each of these case studies, it can be observed that a core set of genes and functions – typically related to replication and encapsidation – are conserved whereas auxiliary functions, possibly of contextual adaptive value, can be gained and lost in time as viruses co-evolve with their preferred hosts. However, as this core increases in size, through a translocation of satellite functions into the core network, it is expected to restrict the host species the virus is able to successfully infect. In a similar vein, transposon inactivation studies seeking to determine the minimal genome of a parasitic mycoplasma (M. genitalium) [59], indicate a high level of redundancy in gene functions related with replication, DNA repair and transcription, with other cellular functions largely lost. A more inclusive analysis of parasitic mycoplasma species [60] suggests that gene products available directly from the host may become neutralized when liberated from normalizing selective constraints, whereas functions under-represented in the host, tend to be preserved. This suggests that the core/satellite distinction provides a general framework for dichotomizing the genome. The core constitutes a minimal, conserved set of functions, whereas the satellites encode functions, which are strongly environmentally mediated. This dichotomy is somewhat similar to essential and non-essential genes, but stresses the conserved, phylogenetic value of the core, and the shifting and dynamical nature of the accessory genes as they move in and out of the core's orbit.

Distinct viral families may share satellite functions

The similarities in the gene accretion process among different DNA viruses infecting insects suggested that the host genetic regulatory network (GRN) creates a common selection pressure for convergence by means of satellite functions of locally adaptive value. The baculovirus, poxvirus, iridovirus, nudivirus and Ascovirus families possess B-type DdDp adjacent to cellular B-type DdDp, distinct from those in possession of A-type DdDp [24], [13], [14]. Hence insect-infecting viruses, carrying a B-type DdDp, are likely to have accreted GRNs around regulatory cores emerging from B-type cellular hosts [25]. These 5 families in addition to infecting closely related hosts share an unusually high number of supplementary or satellite genes. For example, baculovirus share 25 genes with entomopox, 13 with the nudivirus and 14 with iridovirus (list available from the authors). The information available on these shared genes suggests that almost half are of unknown function, several are involved in apoptosis, several have tissue-specific roles, a small fraction are involved in RNA metabolism (e.g., the sharing of both subunits lef-4 and -5 of the RdRp of the baculovirus with nudivirus), one is a putative helicase homologue, and that some are enzymes involved in cell regulation (e.g., methyl-tranferases and protein kinases). None appear to have homology to capsid proteins or are directly involved in DdDp activity (data available from the authors). Thus in addition to genome similarity derived from common descent (homology) there is additional similarity that evolves as the result of an overlapping host range (analogy) which serves to reinforce, rather than diminish, the historical signal.

Some regions of the viral genomes are prone to high levels of recombination and gene shuffling. For example, highly repetitive genomic regions (hr) are known to be associated with loci of genome instability and divergence among baculoviruses [26]. One of the features of these unstable regions is that they tend to flank genes that have experienced duplications in several baculoviruses. These regions constitute nearly 34 distinct genes [14], and are loosely grouped as Baculovirus Repeated Open reading frames (BRO genes) [27]. BRO genes suggest a putative mechanism for satellite function accretion, whereby features such as hr facilitate xenologous gene exchange. Baculoviruses share at least 17 distinct BRO lineages with the ascoviruses that also infect insects. The mechanism by which genes may be acquired could entail direct exchange among distinct viruses while infecting the same host [47] or direct acquisition from the host. The first alternative is perhaps less likely, as it requires superinfection to take place at the cellular level. This is generally considered a less frequent event than independent encounters between different non-persistent viruses (such as most of the ones we addressed in our study) with phylogenetically related hosts. This remains an important empirical issue to be resolved.

Are viral genomes examples of hypercyclic organization?

Why should a set of genes remain so closely linked through evolutionary time? Eigen and Schuster's hypercycle [28] provides a principle of organization, by which a stable replicative, metabolic core (to include in the case of viruses those elements essential for horizontal transmission), exhibit continuity and coherence through evolutionary time. The hypercycle refers to that set of proteins constituting a closed catalytic network. By invoking the hypercycle structure of networked, genetic dependencies we go some way towards explaining the finding that the complete genomes of viruses have histories which are well represented by their replicative core, the central hub of which is polymerase. We suggest that by treating the whole genome as a quantitative variable and deriving a genomic phenotype (phenome), we are able to observe epistatic effects among genes that lead to linkage disequilibrium among members of the genomic core. This core cannot be perturbed without rendering the virus defective, and hence a large group of genes exhibit concerted evolution and, as suggested by Figure 7, it is possible that the larger the genome the greater the correlation across the genome. Nevertheless, the process of gene gain and loss at the periphery of the genomic core is likely to promote adaptive plasticity through gene exchange in locally variable, selective environments. This might be expected to weaken the historical signal from the core, whereas in our study, this process often serves to amplify the core history by virtue of overlapping host affinity.

Figure 7
Plot of phenetic distance (1/S′) versus genetic distance (K) for baculoviruses.

Phenomics and genome evolution

Virus phenomes prove to be informative much the same way that eukaryotic phenotypes can be informative, by indicating tightly coupled genetic dependencies. These dependencies can be used, when cross validated against homology, to reveal the non-overlapping evolutionary histories of complete virus genomes. In particular, the apparent independent origin of the adenoviruses from ancestral prokaryotic hosts (both sharing A-type DdDp), from that of the baculoviruses, herpesviruses, and poxviruses from eukaryotic hosts (all sharing a B-type DdDp). In general, these data are in support of the “escaped gene” hypothesis, which posits distinct virus lineages as having independent origins. We have gone further in suggesting that multiple genes might have escaped simultaneously, or as is more likely, in concert and convergently, centered about distinct polymerase catalytic cores. We investigated the gene accretion process around the catalytic cores, and demonstrated that this process shows an asymmetry in gene gain and loss in favor of gain. The gain bias can lead viral genomes to become analogous in gene content when infecting related hosts.

Congruence between two levels of genetic coarse-graining supports the hypothesis that distinct DNA virus families evolve their genomes around replicative cores derived from independent and divergent cellular origins. Crucially, the loss of genomic synteny (i.e., collinearity) in evolutionary time, even when a core set of shared genes is maintained, suggests that a virus should be defined in terms of a core set of genes essential for survival that exist in a constant state of flux upon a changing background of conserved, host gene-regulatory-networks. The virus is effectively a rather diffuse organism and includes elements of the host. This dependency can promote the persistence of a phylogenetic signal in the face of significant gene transfer when the transfer process reinforces host preferences imposed through constraining core functions.

Methods

Complete genome sequences

The viral families and genomes used are shown in Table 1. The choice of viral families was based on the criterion that these families cover one order of magnitude in viral genome size, with adenovirus genomes of around 30 Kbp and poxvirus 200 Kbp. We used: (i) 20 adenovirus with a maximum length of 45,063 bp and a mean size 33,762 bp, (ii) 24 baculovirus together with a the H. zea nudivirus (Hz-1) (25 genomes total) with a maximum length of 178,733 and a mean size 156,040 bp, (iii) 31 herpesvirus with a maximum length of 241,087 and a mean size of 156,236 bp and, (iv) 22 poxvirus with a maximum length of 359,853 bp and a mean size of 190,509 bp.

Local similarity

Our rationale was to have a measure of overall genome similarity, which avoids the need for complete genome alignments [14], [15], [49], [50]. This is because, as complete genomes diverge in time, rearrangements increase differences in gene order among taxons leading to significant alignment problems. This tendency is compounded by an increase in differences in gene content, with a net loss of shared orthologues. The loss of collinearity and orthologues through evolutionary time tends to render exhaustive alignment useful or practical only for closely related genomes. This is the case even when large amounts of information from orthologous genes are available for non-collinear, closely related genomes. Because most information at the DNA level is maintained in the coding regions of shared orthologues, it leads to little loss of information to use the translated peptides to conduct local similarity searches [55], [56], this have lead to development of several alignment-free distance and similarity metrics as well as phylogenetic reconstruction methods [49], [50], [51], [52], [53], [54]. For each combination of paired genomes, rather than attempt alignment, we used the portable implementation of tblastx [29] available in the NCBI Blast distribution (http://www.ncbi.nlm.nih.gov/blast/download.shtml). We installed BLAST on our computer cluster, including 60 Opteron cores running LAM/MPI 7.1 (Local Area Multicomputer / Message passing interface) (http://www.open-mpi.org/). For each pair of genomes, bit scores S[29], [30] were calculated for all local high-score pairs (HSP), that were collected to obtain a global genomic similarity either by central tendency statistics (mean, average and mode) or by comparing S′ distributions (see details below). Since S′, being a measure of similarity is obviously inversely proportional to evolutionary distance we assumed 1/S′ as a measure of evolutionary distance (k), following the approximate relation k[congruent with][var phi]/S′ (with an unknown proportionality factor [var phi]. For the tblastx program, preliminary evaluation with limited sets of sequences indicated that both BLOSUM62, BLOSUM45 and several other transition matrices provided similar results and that collecting either 200, 500, 2000 or 40000 HSPs would not change significantly the S′ distributions. We also observed that using the low complexity filtering option [31] did not alter significantly our results. Other tblastx parameter values used were: Open Gap = 4, Gap extension penalty = 2, GapX_dropoff = 50, expect = 10 and word size = 3 (http://www.ncbi.nlm.nih.gov/blast/producttable.shtml). The tblastx outputs were parsed with BlastPhen (script in PERL available from the authors upon request) and the distributions of S′ were obtained for each pair of genomes. These distributions were subjected to a range of data analyses prior to clustering and their efficacy in generating informative genome clusters evaluated. Some statistics we calculated included moments of the distance distributions, and Kullback-Leibler or Chernoff distances among distributions. Reconstructions based on these whole genome distance metrics were then compared to phylogenetic reconstructions based on the DNA polymerase (DdDp) genes, the alignments of several orthologues or, in some cases, fully aligned complete genomes, indicated that central tendency measurements such as the mean and median, used during this work, would be sufficient to convey the main associations among genomes (data not shown).

Clustering metrics and their properties

Most clusters obtained for the four viral families were in good agreement with pre-established phylogenies and demonstrate a strong correlation with known viral biology. The statistic S′ is a measure of similarity, which takes into account: (i) distance estimation based on amino acid transition matrices (e.g., BlOSUM, PAM, etc.) and (ii) a “penalty” system that considers indels (gaps caused by insertions or deletions during HSP evaluation). It is clear however that S′ compounds 2 types of quantities that share neither an explicit mechanism nor metric, since indels should not be treated in the same way as amino acid replacements matrices in evolutionary models of change. Nevertheless, the strong agreement among topologies built by using 1/S′ (reciprocal of distance or similarity) and those built from amino acid sequences of viral DNA polymerases, support the notion that 1/S′ is proportional to genetic distance k, possibly approaching k[congruent with][var phi]/S′, where the proportionality factor ([var phi]) should vary according the level of congruence of the tree obtained for proteons or single genes and full genome dendograms. For example, under the circumstance that both the proteon (or gene) tree and the genome dendogram are identical with identical branch lengths along all branches, [var phi] will be 1 and k will be 1/S′.

In the case of the baculovirus, a plot of phenetic distance (1/S′) versus genetic distance (K) had an excellent agreement (r2 = 0.85) with the genome-based clusters, further supporting k[congruent with][var phi]/S′ (Figure 7). K was obtained using phylogenetic ML reconstruction with the Tree-Puzzle version 5.2 program [58] estimated from a 16803 amino-acids long protein concatamer for the 29 genes conserved in 24 complete baculovirus genomes. 1/S′ was estimated using BlastPhen. Most of the data fits the regression function supporting the use of 1/S′ as a measure of genetic diversity [14], [15]. Nevertheless, since 1/S′ lacks correction for superimposition when K increases above 1.5, 1/S′ can overestimate K when comparing closely related baculovirus genomes. Hence tblastx can inflate HSP from non-coding regions [15]. Having stated these reservations, given k[congruent with][var phi]/S′, [var phi] averaged 82.91 for the baculovirus data. It follows than, that when comparing 1/S′ distribution moments such as mean, median and mode, the relationship with genetic distance is positive, because both 1/S′and k grow in time. The use of estimators of central tendency for the distance distributions (i.e., low order statistics) is useful and renders a strong agreement with gene phylogenies for all datasets. Nevertheless, these low order statistics may loose information, since potentially important data provided by the distribution shape is lost.

Inferring DdDp trees

The DdDp gene was used for phylogenetic inference to deduce the relationships among distantly related DNA viruses and among DNA viruses of the same family [22], [32], [13]. This has proved to be the best choice for an orthologue shared by all members of the viral families. Therefore, the peptide (amino acids) sequences encoded by the DNA-dependent-DNA-polymerase (DdDp) gene of each taxon were aligned with ClustalW [33]. These were used for phylogenetic reconstructions and non-parametric bootstrap using maximum likelihood (ML) method implemented in phyml (Guindon and Gascuel, 2003) using the WAG model plus gamma and invariant (WAG+Γ+I) as the best-fit model of the viral DdDp evolution that was determined using the Bayesian information criterion (BIC) implemented in ProtTest version 1.2.6 [34]. The robustness of each tree component was accessed by 500 non-parametric bootstrap iterations with the phyml program under the WAG+Γ+I model of protein evolution. Tree topology, the shape parameter (α) of the gamma (Γ) distribution of variable rates and, the proportion of invariable sites (I) were optimized during successive iterations. Moreover, we used Bayesian inference (BI) to calculate the posterior probability of each node of the DpDp trees using the parallel version of MrBayes v. 3.1.2 [35] running 20 million generations along 10 chains. Posterior probabilities of each node were calculated with the TreeAnnotator v. 4.7.7 [36] from 20 thousand trees sampled with a 10% burn-in, which was well after the sampling regime was stable around the log-likelihood (-lnL) maxima as determined by inspect the run trace behavior with TRACER v1.4 [37].

Nevertheless, since the DdDp genes within a viral family tend to maintain similar biochemical functions provided by collinear clusters of short conserved domains [22], embedded in regions that will vary dramatically among different lineages, one could argue that rate heterogeneity in time (heterotachy) could induce systematic errors during parametric ML inference [38], [for a critique see 39]. Therefore, we used a non-parametric parsimony method, as an alternative to try reducing potential bias during phylogenetic inference and as source for comparison with parametric methods such as maximum likelihood and Bayesian inference. Nevertheless, the use of parsimony under no assumption of weighted character-state change violates the assumption of a biochemical basis for preferential amino acid substitution process in time. To deal with this, accepted-mutation stepmatrices were calculated directly from the DdDp gene alignments for each viral family using the amp program (program ftp://oeb.harvard.edu/rice/amp.tar.Z.), to try establishing a realistic estimate of the minimum number of character state changes using parsimony.

Tree reconciliation

To investigate the dependence of the history of the GRN functions [25] encoded in a viral genome to the replicative core, we reconciled the DdDp phylogeny with that of the rest of the viral genome (i.e., by making a “core-less” genome excluding the DdDp from the complete genomic sequence) and evaluated how the DdDp tree predicts the phylogeny for the complete collection of satellites for each genome. Trees from complete genomes were obtained by clustering 1/S′ distance matrices using the Weighted Neighbor Joining method (WB) [40] and compared with clusters obtained with the neighbor joining (NJ), UPGMA, Fitsch (F) and Kitsch (K) methods from the PHYLIP package [41]. Trees for core (DdDp) and core-less genomes containing satellite functions were reconciled using the TreeMap V1.0 program that maximizes the number of codivergences, which are cospeciation events indicated by congruent nodes present in both DdDp and core-less genomes trees (i.e., joint cladogenesis) using parsimony optimality criteria [42], [48], [49]. Tree reconciliation envisages mapping the core-less genome tree to that of the DdDp tree to estimate the historical associations between these two phylogenies. If both the DdDp and “core-less” genome trees are identical (i.e., had perfectly congruent trees), the DdDp tree is a sufficient predictor for the genome. However, since both phylogenies are not necessarily identical, the reconciliation process makes uses additional events to maximize the number of co-divergences [48], [49]. These events include: (i) duplication, a generic term to cover a situation where tree reconciliation requires that a core-less-genome lineage speciates independently from the DdDp lineage or that we failed to find it; (ii) exchange or switching event which are synonyms that cover the situation where tree reconciliation requires that satellite functions associate with a number of distinct DdDp lineages and; (iii) sorting, where tree reconciliation requires that a core-less branch fails to follow a DdDp branching event. Moreover, it was necessary to evaluate whether the observed congruence structure and associated deviations could be explained by chance. We did this by investigating the departure from a null model capturing the number of codivergent events in the assemblage of reconciled trees for both DdDp and core-less genomes. The number of codivergent nodes expected by chance in the reconstructed assemblage was estimated by randomizing both core and satellite trees 10 thousand times with TreeMap applying the proportional from distinguishable method for generating random trees, and using the machine system clock as a random seed generator.

The dynamics of gene gain and loss events

In addition to the study of the highly conserved core of DNA viruses and its relationship to the genome as a whole, we also considered evidence that viral protein-coding genes are acquired from host cells. These “satellite” functions are an essential source of evolutionary novelty for RNA viruses [43] and DNA viruses. We consider independent gain and loss events in the Baculoviruses [44], [13], [45], [14], Poxviruses [2] Herpesviruses [1] and Ascoviruses (see below). To investigate the quantitative aspect of convergence of gene acquisition among DNA viruses, we inferred gene gain and loss events with MacClade 4.07 [46], reconstructed along the branches of dendograms calculated from complete genomes. For this purpose we used gene-content tables for DNA viruses built by Herniou et al., [3], [4] that we further extended and modified by Lauzon et al., [45], Oliveira et al., [14] and Wolff et al., [15].

Acknowledgments

We would like to thank, Elisabeth Herniou, Hilary Lauzon, Alexandra Garcia-Maruniak and Juliana V. Oliveira for updating and providing the baculovirus gene content data, and Andrés Moya and two other anonymous referees for their thorough, constructive and detailed contributions.

Footnotes

Competing Interests: The authors have declared that no competing interests exist.

Funding: This work was funded by both the Santa Fe Institute (USA) and FAPESP (Brazil), project number 07/55282-0. PMAZ has a CNPq PQ Productivity Scholarship.

References

1. Montague MG, Hutchison CA., III Gene content phylogeny of herpesviruses. Proc Natl Acad Sci USA. 2000;97:5334–5339. [PMC free article] [PubMed]
2. McLysaght A, Baldi PF, Gaut BS. Extensive gene gain associated with adaptive evolution of poxviruses. Proc Natl Acad Sci USA. 2003;100:15655–15660. [PMC free article] [PubMed]
3. Herniou EA, Olszewski JA, Cory JS, O'Reilly DR. The genome sequence and evolution of baculovirus. Annu Rev Entomol. 2003;48:211–234. [PubMed]
4. Herniou EA, Olszewski JA, O'Reilly DR, Cory JS. Ancient coevolution of baculovirus and their hot isnsects. Journal of Virology. 2004;78:3244–3251. [PMC free article] [PubMed]
5. Morse SS. Towards an evolutionary Biology of Viruses. In: Morse SS, editor. The Evolutionary Biology of Viruses. New York: Raven Press; 1994. pp. 1–27.
6. Green R. On the nature of filterable viruses. Science. 1935;82:443–445. [PubMed]
7. Laidlaw PP. Virus Diseases and Viruses. Cambridge: Cambridge University Press; 1938.
8. Andrewes C. Viruses and Evolution. Birmingham: Birmingham University; 1967.
9. Forterre P. The two ages of the RNA world, and the transition to the DNA world: a story of viruses and cells. Biochimie. 2005;87:793–803. [PubMed]
10. Bell PJL. Viral Eukaryogenesis: Was the Ancestor of the Nucleus a Complex DNA Virus? J Mol Evol. 2001;53:251–256. [PubMed]
11. Zanotto PMA, Kessing BD, Maruniak JE. Phylogenetic interrelationships among baculoviruses: evolutionary rates and host associations. Journal of Invertebrate Pathology. 1993;62:147–164. [PubMed]
12. Herniou EA, Luque T, Chen X, Vlak JM, Winstanley D, et al. Use of whole genome sequence data to infer baculovirus phylogeny. Journal of Virology. 2001;75:8117–8126. [PMC free article] [PubMed]
13. Garcia-Maruniak A, Maruniak JE, Zanotto PMA, Doumbouya AE, Liu JC, et al. Sequence analysis of the genome of the Neodiprion sertifer nucleopolyhedrovirus. Journal of Virology. 2004;78:7036–51. [PMC free article] [PubMed]
14. Oliveira JVC, Wolff JLC, Garcia-Maruniak A, Ribeiro BM, Castro MEB, et al. The genome of the most widely used viral biopesticide: Anticarsia gemmatalis nucleopolyhedrovirus (AgMNPV-2D). Journal of General Virology. 2006;87:3233–3250. [PubMed]
15. Wolff JLC, Valicente FH, Oliveira JVC, Zanotto PMA. The Analyzes of the Genome of Spodoptera frugiperda Nucleopolyhedrosisvirus (SfMNPV-19) and the high genomic heterogeneity in Group II NPV. Journal of General Virology. 2008;89:1202–1211. [PubMed]
16. McGeoch DJ, Dolan A, Ralph AC. Toward a Comprehensive Phylogeny for Mammalian and Avian Herpesviruses. Journal of Virology. 2000;74:10401–10406. [PMC free article] [PubMed]
17. Booy FP, Trus BL, Davison AJ, Steven AC. The capsid architecture of Channel catfish virus, an evolutionarily distant herpesvirus, is largely conserved in the absence of discernible sequence homology with Herpes Simplex Virus. Virology. 1996;215:134–141. [PubMed]
18. McGeoch DJ, Rixon FJ, Davison AJ. Topics in herpesvirus genomics and evolution. Virus Research. 2006;117:90–104. [PubMed]
19. Rao VB, Black LW. Cloning, overexpression and purification of the terminase proteins gp16 and gp17 of bacteriophage T4. Construction of a defined in-vitro DNA packaging system using purified terminase proteins. J Mol Biol. 1988;200:475–488. [PubMed]
20. Senkevich TG, Koonin EV, Bugert JJ, Darai G, Moss B. The Genome of Molluscum Contagiosum Virus: Analysis and Comparison with Other Poxviruses. Virology. 1997;233:19–42. [PubMed]
21. Delhon G, Tulman ER, Afonso CL, Lu Z, de la Concha-Bermejillo A, et al. Genomes of the Parapoxviruses Orf Virus and Bovine Papular Stomatitis Virus. Journal of Virology. 2004;78:168–177. [PMC free article] [PubMed]
22. Braithwaite DK, Ito J. Compilation, alignment, and phylogenetic relationships of DNA polymerases. Nucleic Acids Research. 1999;21:787–802. [PMC free article] [PubMed]
23. Alba MM, Das R, Orengo CA, Kellam P. Genomewide Function Conservation and Phylogeny in the Herpesviridae. Genome Research. 2001;11:43–54. [PMC free article] [PubMed]
24. Chia-Hsiung C, Su-Mei L, The-Yuan C, Yu-Yun H, Dan-Ping W, et al. Analysis of the complete genome sequence of th Hz-1 virus suggests that it is related to members of the Baculoviridae. Journal of Virology. 2002;76:9024–9034. [PMC free article] [PubMed]
25. Krakauer DC, Zanotto PMA. Protocells: Bridging Nonliving and Living Matter. Boston: MIT Press; 2007. Viral Individuality and Limitations of the Life Concept. In: Steen Rasmussen; Mark Bedau; Norman Packard; David Krakauer; Peter Stadler; David Deamer. (Org.).
26. Possee RD, Rohrmann GF. Baculovirus genome organization and evolution. In: Miller LK, editor. The Baculoviruses. New York: Plenum; 1997. pp. 109–140.
27. Bideshi DK, Renault S, Stasiak K, Federici BA, Bigot Y. Phylogenetic analysis and possible function of brolike genes, a multigene family widespread among large double-stranded DNA viruses of invertebrates and bacteria. Journal of General Virology. 2003;84:2531–2544. [PubMed]
28. Eigen M, Schuster P. In The hypercycle: A principle of Natural self-organization. Section XVIII Continuity of Evolution. Berlin: Springer-Verlag; 1979. pp. 86–87.
29. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–410. [PubMed]
30. Pertsemlidis A, Fondon JW., III Having a BLAST with bioinformatics (and avoiding BLASTphemy). Genome Biol. 2001;2:1–10. [PMC free article] [PubMed]
31. Wootton JC, Federhen S. Statistics of local complexity in amino acid sequences and sequence databases. Comput Chem. 1993;17:149–163.
32. Chen F, Suttle CA. Evolutionary Relationships among Large Double-Stranded DNA Viruses That Infect Microalgae and Other Organisms as Inferred from DNA Polymerase Genes. Virology. 1996;219:170–178. [PubMed]
33. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;11:4673–4680. [PMC free article] [PubMed]
34. Abascal F, Zardoya R, Posada D. ProtTest: Selection of best-fit models of protein evolution. Bioinformatics. 2005;21:2104–2105. [PubMed]
35. Altekar G, Dwarkadas S, Huelsenbeck JP, Ronquist F. Parallel Metropolis-coupled Markov chain Monte Carlo for Bayesian phylogenetic inference. Bioinformatics. 2004;20:407–415. [PubMed]
36. Drummond AJ, Rambaut A. “BEAST: Bayesian evolutionary analysis by sampling trees.” BMC Evolutionary Biology. 2007;7:214. [PMC free article] [PubMed]
37. Rambaut A, Drummond AJ. 2007. Tracer v1.4, Available from http://beast.bio.ed.ac.uk/Tracer. [PMC free article] [PubMed]
38. Kolaczkowskim B, Thornton JW. Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous. Nature. 2004;431:981–984. [PubMed]
39. Spencer M, Susko E, Roger AJ. Likelihood, Parsimony, and Heterogeneous Evolution. Molecular Biology and Evolution. 2005;22:1161–1164. [PubMed]
40. Bruno WJ, Socci ND, Halpern AL. Weighted neighbor joining: a likelihood-based approach to distance-based phylogeny reconstruction. Mol Biol Evol. 2000;17:189–197. [PubMed]
41. Felsenstein J. PHYLIP - Phylogeny Inference Package (Version 3.2). Cladistics. 1989;5:164–166.
42. Page RDM. Maps between trees and cladistic analysis of historical associations among genes, organisms, and areas. Cladistics. 1994;6:119–136.
43. Romano CM, Ramalho RF, Zanotto PMA. Tempo and Mode of ERV-K Evolution in Human and Chimpanzee Genomes. Archives of Virology. 2006;151:2215–2228. [PubMed]
44. Hawtin RE, Arnold K, Ayres MD, Zanotto PMA, Howard SC, Gooday GW, et al. Identification And Preliminary Characterization Of A Chitinase Gene Inthe Autographa Californica Nuclear Polyhedrosis Virus Genome. Virology. 1995;212:673–685. [PubMed]
45. Lauzon HAM, Garcia-Maruniak A, Zanotto PMA, Clemente JC, Herniou EA, et al. Genomic comparison of Neodiprion Sertifer and Neodiprion lecontei nucleopolyhedrosisviruses and identification of potential hymenopteran baculovirus specific ORFs. Journal of General Virology. 2006;87:1477–1489. [PubMed]
46. Maddison DR, Maddison WP. MacClade 4, release version 4.07. Suderland, Massachusetts: Sinauer Associates, Inc; 2005.
47. Monier A, Claverie J, Ogata H. Horizontal gene transfer and nucleotide compositional anomaly in large DNA viruses. BMC Genomics. 2007;8:456. [PMC free article] [PubMed]
48. Page RDM, editor. Tangled trees: Phylogeny, cospeciation, and evolution. University of Chicago Press; 2002.
49. Page RDM. Parallel Phylogenies: Reconstructing the History of Host-Parasite Assemblages Cladistics. 1994;10(2):155–173.
50. Qi J, Wang B, Hao Whole proteome prokaryote phylogeny without sequence alignment: a K-string composition approach. J Mol Evol. 2004;58(1):1–11. [PubMed]
51. Wu TJ, Burke JP, Davison DB. A measure of DNA sequence dissimilarity based on Mahalanobis distance between frequencies of words. Biometrics. 1997;53(4):1431–1439. [PubMed]
52. Otu HH, Sayood K. A new sequence distance measure for phylogenetic tree construction. Bioinformatics. 2003;19(16):2122–2130. [PubMed]
53. Ulitsky I, Burstein D, Tuller T, Chor B. The average common substring approach to phylogenomic reconstruction. J Comput Biol. 2006;13(2):336–350. [PubMed]
54. Wu TJ, Hsieh YC, Li LA. Statistical measures of DNA sequence dissimilarity under Markov chain models of base composition. Biometrics. 2001;57(2):441–448. [PubMed]
55. Höhl M, Ragan MA. Is multiple-sequence alignment required for accurate inference of phylogeny? Syst Biol. 2007;56(2):206–22. [PubMed]
56. Auch AF, Henz SR, Holland BR, Göker M. Genome BLAST distance phylogenies inferred from whole plastid and whole mitochondrion genome sequences. BMC Bioinformatics. 2006;7:350. [PMC free article] [PubMed]
57. Henz SR, Huson DH, Auch AF, Nieselt-Struwe K, Schuster SC. Whole-genome prokaryotic phylogeny. Bioinformatics. 2005;21(10):2329–2335. [PubMed]
58. Schmidt HA, Strimmer K, Vingron M, von Haeseler A. TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing. Bioinformatics. 2002;18:502–504. [PubMed]
59. Hutchinson CA, Peterson SN, Gill SR, Cline RT, White O, et al. Global transposon mutagenesis and a minimal mycoplama genome. Science. 1999;286:5447–2165. [PubMed]
60. Peterson SN, Fraser CM. The complexity of simplicity. Genome Biology. 2001;2(2):comment2002.1–2002.8. [PMC free article] [PubMed]

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