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Integrating Evolution and
Deveiopment: Tiie Need for
Bioinformatics in Evo-Devo
PAULA M. MABEE
This article is an overview of concepts relating to the integration ofthe genotype and phenotype. One ofthe major goals of evolutionary developmental
biology, or evo-devo, is to understand the transformation of morphology in evolution. This goal can be accomplished by synthesizing the data
pertaining to gene regulatory networks and making use ofthe increasingly comprehensive knowledge of phylogenetic relationships and associated
phenotypes. I give several examples of recent success in connecting these different biological levels. These examples help illuminate the "black box"
between genotype and phenotype and illustrate a few ofthe technical and bioinformatic challenges ahead. The key concept of modularity unites
genetic, developmental, and evolutionary approaches, because modules are the units of evolution. Primitive and derived network modules interact
in development to create the phenotype. To obtain a systems-level understanding of evolutionary phenotypic change, bioinformatics approaches
involving ontologies need to be applied, and new methods of visualization need to be developed.
Keywords: bioinformatics, genetics, modularity, evolution, development
T
WO of the most exciting and difficuit chaiienges in
biology are to understand how morphology, or anatomical form, is created in development and to determine how
such developmental morphology is transformed in evolution. These questions unite the emergent field of
"evo-devo," the interdisciplinary study of evolution and development, which has become one of the most rapidly moving fields in biology. A number of excellent reviews and
books document the history, knowledge base, and
syntheses in this field (Gilbert 2003, Minelli 2003, Holland
2004, Carroll 2005). As evo-devo scientists sharpen their
focus on comparing developmental morphology, it becomes more obvious that the comparisons must be "read"
within a phylogenetic framework (Baum et al. 2005). In
fact, tracing the pattern of evolutionary transformation of
the developing phenotype against the evolutionary tree of
life defines a significant problem set for evo-devo (Wagner
and Larsson 2003). In addition, the impact of various developmental mechanisms (Arthur 2004) can be assessed
against the pattern defined by the tree of life. Explaining the
pattern of evolutionary change in morphology in an integrative fashion requires a systems approach—synthesizing
knowledge from various biological levels encompassed in development (e.g., DNA sequence, regulatory networks,
cell-cell and tissue interactions), evolution (e.g., phylogenetic
relationships, population level), and ecology.
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The critical need to relate genotypic to phenotypic data, and
the difficulty of that endeavor, is echoed in many recent papers (Hall 2003, Smith 2003, Irish and Benfey 2004, Kuratani
2004). Despite the battery of tools being used to address the
question of how an organism is built (i.e., how the developing phenotype, the observable expression ofthe genotype, is
created in development), the connection between genotype
and phenotype remains poorly understood. Large-scale
genome sequencing has resulted in a "parts catalog" of complete information about what is in the genome, and yet the
"user's manual," the genetic and molecular basis and rules
by which morphology is assembled, is poorly understood
(Kitano 2002). It is a gargantuan leap to go fiom genotype to
phenotype using tools from developmental genetics. Progress
is being made by chipping away at genotype-phenotype relationships in the context of developmental genetics of model
species, and another jump, aided by tools of phylogenetics, has
been taken to connect development to the directionality and
timing of evolutionary change. But it is the tools of computation, of bioinformatics and ontologies (i.e., structured
Paula M. Mabee (e-mail: [email protected]) is an evo-devo biologist at the
University of South Dakota, Vermillion, SD 57069. She studies skeletal development and evolution in fishes. © 2006 American Institute of Biological
Sciences.
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vocabularies that make databases interoperable), that are
now required to merge the biological levels of knowledge. With
improved methods ofdata mining and analysis, evolutionary
questions can be answered with the desired mechanistic
depth. The purpose of this article is to explain a key concept—
namely, modularity—that spans the gap from genotype to
phenotype; to describe a few of the recent successes in connecting genotype and phenotype; and to champion the need
for bioinformatics tools to facilitate more rapid and higherlevel progress.
Modularity
Modules are a piece of common conceptual ground for evolutionary and developmental biologists because they span and
unite biological levels. Biologists working at different levels,
however, have different conceptions of modules: Geneticists
conceive of modularity in terms of gene organization and
function, developmental biologists consider the modularity
of gene regulatory networks, and many evolutionary biologists
think in terms of phenotypic "characters." Modularity at the
morphological level is somewhat intuitive: An organism is
composed of discrete parts, and these parts are semiindependent in development (figure 1) and evolution. Morphological modules as entities are assumed to have a developmental genetic integrity, and as such they are individuated
(Wagner 1989, Geeta 2003).
orphology
Developmental genes
Figure 1. The black box between genotype and phenotype
is filled here with the modules (represented by colored
cylinders) that function in development to create morphology (more generally, the phenotype). Blue and red indicate the evolutionary status ofthe modules, blue for
primitive and red for derived. Primitive and derived
modules interact here to form the typical mosaic of primitive and derived features (characters or modules) that
comprise an organism.
The limb bud is an excellent example of modularity at
both morphological and genetic levels. The paired limbs
have a clear morphological identity and a limited degree of
connectivity to the rest of the body at the morphological
level. At a genetic level, the regulatory cascades that control
limb development (Tabin et al. 1999) constitute a patterning
module that is conserved between forelimbs and hindlimbs.
Other examples of morphological modules include
302 BioScience • April 2006 / Vol. 56 No. 4
actinopterygian fish fins (Mabee et al. 2002), the mouse
mandible (morphometrically partitioned into submodules;
Klingenberg et al. 2003), and bird feathers (hierarchically
nested into metamodules in development; Prum and Dyck
2003).
Morphological modules, inasmuch as they represent units
of homology, may be considered equivalent to the morphological characters of phylogenetic systematics. Evolutionary
characters are the homologous structures that vary across
species, and multiple characters are analyzed together to generate phylogenies. Although systematists do not have "modularity" as a working concept and evo-devo biologists do not
think in terms of "characters," it is appropriate to consider these
terms equivalent, because both refer to an individuated entity that is genetically determined, homologous, and maintained across taxa. In working terms, because so little is
understood about the modules underlying the phenotype, systematists cannot use them as characters to infer phylogeny at
this time. As details about how modules are integrated are better understood, these data can be added to phylogenetic
analyses.
Underlying modularity at the morphological level—and
responsible for it—are integrated developmental networks (see
Schlosser and Wagner 2004). Modules have been defined, in
fact, as "networks of interacting elements behaving as relatively
independent units of development or function" (Schlosser and
Thieffry 2000). At the level of gene interactions, modules
may range from interactions among a few genes to large-scale
genetic networks, such as the complex regulatory gene network for endomesoderm specification in the sea urchin (figure 2; Davidson et al. 2002). This complex regulatory network
underlies the specification of a single cell type (endomesoderm). Glearly, the development of even a single aspect of morphology, which is controlled by many connected networks of
regulatory genes, must be staggeringly complex. Interestingly, though, the complexity of body plan development is said
to be matched by that of some human-engineered systems
(Gsete and Doyle 2002). A Boeing 777 generates a volume of
information similar to that in the human genome every
minute: 150,000 modules, organized through protocols into
networks (Csete and Doyle 2002). Genetic modules, or "cassettes" of genes used to effect a common Sanction (Rudel and
Sommer 2003), interact with one another to ultimately specify phenotypes. Thus, modules that themselves interact, that
vary semi-independently, and that together specify a modular morphology (figure 1) are an important link between
genotype and phenotype. One might say that such molecular modules, the gene interactive networks and their connecting protocols or rules (Ravasz et al. 2002), are the building
blocks of development and evolution.
The network properties of development are increasingly discussed in the literature of development and of bioinformatics (Ravasz et al. 2002, Qin et al. 2003, Anholt 2004, Cork and
Purugganan 2004). Development of the body plan is controlled by large networks of regulatory genes, and it essentially
flows from the correct temporal and spatial transcription of
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Figure 2. Regulatory gene network for endomesoderm specification (reproduced with permission from Eric Davidson;
http://sugp.caltech.edu/endomes/j. Forty genes are currently known to be involved in specifying this single cell type
(Davidson et al. 2002). Each short horizontal Une from which a bent arrow extends to indicate transcription represents the
as-regulatory element that is responsible for expression ofthe gene named in the domain shown. The architecture ofthe
network is based on perturbation and expression data, on data from ds-regulatory analyses for several genes, and on other
experiments referenced at the Web site cited above.
such genes, which require the correct use of the instructions
encoded in ds-regulatory DNA (Stern 2000, Revilla-iDomingo and Davidson 2003). Evolution of phenotype occurs through changes in these networks. Because gene
regulatory networks underlie the processes of both development and evolution, unraveling their architecture in appropriately chosen species will be the key to understanding how
genomes control development and how they evolve (Revillai-Domingo and Davidson 2003). The discovery of gene transcription modules from the rapidly accumulating gene
expression data has been aided recently by new analytical
methods in bioinformatics (Lee TI et al. 2002, Lee I et al. 2004,
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Kloster et al. 2005). Uncovering the modular architecture of
the transcriptional networks that comprise developmental programs (Rast 2003) and understanding the developmental
and evolutionary ramifications of their topology are critical
new areas in evo-devo.
Underlying the phenotype and the genetic networks is
another level of modularity at the genetic regulatory level. The
modularity of these genetic regulators or "switches" (Carroll
2005) is perhaps underappreciated by those working at higher
levels. The ds-regulatory DNA of many genes is organized into
independent modules that direct or repress transcription in
specific tissues at particular times in development. The modApril 20061 Vol. 56 No. 4 • BioScience
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ules themselves are constructed from multiple binding sites
for individual transcription factors (Arnone and Davidson
1997). Modular regulatory switches are used for building
modular animals (Carroll 2005), and switches are thus the critical connection to evolution: Such regulation permits evolutionary change to occur in one part ofa structure, independent
of other parts. Ultimately, modularity at the genetic level of
regulation is the secret to modularity at the morphological
level, and modularity is the key to building complexity (Carroll 2005), as evidenced by the overwhelming number of
evolutionary modifications that consist of specializing, tinkering with, and modifying the number of existing modular
parts.
How do modules evolve?
Modularity has been argued to be the most critical aspect of
order in living organisms and their ontogenies, and the attribute that most strongly facilitates evolution (Raff 1996).
Modularity brings together development and evolution in several respects (Schlosser and Wagner 2004). First, we know that
in development, the modularity of gene regulation is translated into semi-independent genetic regulatory networks,
and these specify the discrete features ofthe phenotype. Until the advent of molecular systematics, such features, or characters of the phenotype, were almost exclusively used by
systematists to infer phylogenetic relationships. The inferential methods of phylogenetic systematics assume character independence, and the likelihood of inferring false relationships
is increased by correlated features. Formulation ofa character thus might be seen as equivalent to delineating a phenotypic module that has been maintained and modified by
evolution. Essentially, developmental modularity underlies the
evolutionary modularity of systematic characters. Because at
some level the entire phenotype is correlated, the empirical
problem becomes that of determining levels of correlation.
With enough developmental data on the underlying networks and appropriate informatics, character dependencies
can be evaluated, and these can be built into the methods of
analysis. This is a nontrivial undertaking that relies on heavy
use of phylogenetic and developmental model databases, interoperability, tools, and other programmatic infrastructure.
Phylogenetic information about how individual species are
related is necessary for determining which modules are ancestral and which are derived (Maddison and Maddison
1992), for learning whether a new morphology is due to the
gain or loss ofa module, and for finding out whether a module has been derived once or many times. Development can
be represented as a mosaic of primitive and derived developmental modules interacting to form primitive or derived
morphological features. Phylogenetic hypotheses are thus
critical in interpreting the patterns of evolution of modules—at any hierarchical level. Without a phylogenetic fi-amework, even the most basic questions cannot be framed in a
meaningful way. Applying phylogenetic methods to gene
networks and other modules will yield answers to vital questions in thefieldof evo-devo (box 1). Fortunately, a great deal
304 BioScience • April 2006 / Vol. 56 No. 4
of progress has been made toward understanding the phylogenetic relationships of major animal and plant groups, as well
as the relationships within many smaller clades.
Ontologies: Connecting evolutionaiy
and developmental anatomy
The phenotype figures prominently in the work of developmental biologists, who place major emphasis on connecting
gene expression and function to phenotypic effects. Mutagenesis (i.e., isolating single gene mutants that have discrete
effects on body pattern) has been a primary approach in understanding normal development and in identifying the
body-patterning and "toolkit" genes of evolution (Greenspan
2001, Carroll 2005). The control gene eyeless, for example, was
identifiedfi-omthe mutants v^thout eyes in Drosophila. Other
approaches include the removal or misexpression of a gene
product during development, followed by examination of
the effects on the resultant phenotype (Stern 2000). The data
resulting from these approaches are voluminous, and discovering the complex gene networks that connect sequence
to phenotype will require long-term bioinformatics efforts and
new tools.
Ontologies, or controlled vocabularies, are one approach
that is used to connect databases. Ontologies formally represent hierarchical relationships between defined biological
concepts, such that the vocabularies can be used by both humans and computers to exchange and explore information
(HoUoway 2002, Blake 2004). Controlled vocabularies in the
form of words or phrases are used in ontologies to bring different meanings or synonyms together under a single clear
term; free text is avoided, and the data can be computed.
Bio-ontologies clarify scientific discussions by providing a
shared vocabulary to communicate results and to explore
data. They also make data exploration, inference, and data
mining computationally possible.
The development of ontologies was instigated by molecular biologists to promote collaborations between the medical informatics and bioinformatics ontology communities
(Holloway 2002). The Gene Ontology (GO) project (Harris
et al. 2004) is perhaps the most widely known in the molecular world, consisting of structured, controlled vocabularies
(> 17,500 terms) and classifications that cover several domains
of molecular and cellular biology {www.geneontology.org). It
consists of three ontologies at different hierarchical levels
(molecular function, biological process, and cellular component attributes of gene products). These ontologies are
orthologous (relatable) to one another and thus interoperable (i.e., they can work together). Use ofthe GO is widespread,
and many biological resources, such as UniProt (Apweiler et
al. 2004) and Protein Data Bank, annotate their data in GO
terms (Wolstencroft et al. 2005). The result is that by searching for all proteins associated with a particular GO term,
data fi'om many different sources can be retrieved automatically and efficiently. A single GO term has associated with it
all known synonyms, and thus all related terms are searched
simultaneously.
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Box 1 . Evolution and development: Developmental network-, phenotype-, and systems-level questions.
The pattern of phylogenetic history is critical for tests, predictions, and investigations in evolutionary developmental biology,
or evo-devo. Patterns of character evolution must be synthesized across the full spectrum of biological levels to answer the
major questions below and those subsumed within them. Because of the complexity and volume of these data, new methods
of visualization and analysis will be required to fuUy grasp the patterns of such evolutionary changes.
Developmental network-level questions:
• What is the pattern of evolution of gene regulatory network modules?
• What are the specific changes that have occurred in a particular gene network as it is transformed in evolution, and exactly
where have these have occurred?
• Where exactly does the remodeling of developmental pathways occur? (cis-acting elements? Protein function?)
• What is the frequency and nature of parallel co-option of genetic networks? (Co-option appears to occur frequently in
evolution, as evidenced by the parallel independent co-option of Pax-6, DU, and tinman to pattern eyes, limbs, and hearts,
respectively, in insects and vertebrates; Jockusch and Ober 2004.)
• Developmental pathways may be redeployed in other tissues (heterotopy) or at other developmental times (heterochrony),
or both. How often and under what circumstances does this happen?
• What are the specific bases for constraint in gene networks?
• What are the network properties that promote resilience or enhance evolvahility? (The interactivity among genes indicates
that there might be considerable flexibility in the capacity ofthe genome to respond to diverse conditions; Greenspan 2001.)
• How is constraint at the morphological level related to that at the network level?
• Which sites in a gene network are most conserved or constrained, and which are most labile?
• What types of changes are most common? (Cork and Purugganan [2004] predict that genes functioning early in a genetic
pathway are subject to stronger stabilizing selection than downstream loci, since mutations in these genes are likely to have
greater pleiotropic effects and affect all downstream phenotypes.)
Phenotype-level questions:
• What is the developmental basis for the phenotypic characters (modules) of evolution?
• Is there a "signature" modular composition to the morphological characters (modules) of systematics?
• What are the probabilities of different types of changes within modules?
• Are new linkages between submodules made to produce modular characters?
• Are sets of modules (at different or similar biological levels) correlated evolutionarily? (By mapping multiple modules
simultaneously, the patterns of character association can tested for correlation. Levels of correlation can be quantified.)
Systems-level questions (requiring information from ecology and environment as well):
• What accounts for major novelties?
• Generally, what accounts for homology?
• Can we generalize about homoplasy? Why are some characters susceptible to parallel evolution?
• Why are there trends or "metapatterns" in evolution?
More recently, researchers have developed phenotypic ontologies (Bard 2005) within model organism communities (zebrafish, mouse, fly), motivated by the desire to answer the
developmental questions of how phenotype is generated
from genes. Phenotypic effects of different treatments for a
model species (e.g., mouse) can be compared and analyzed
together with genetic sequence data to piece together the
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underlying genetic architecture. There are now about 15
anatomical ontologies, many of which are linked to organism
databases (Bard 2005) (e.g., Zebrafish Anatomical Ontology, http://zftn.org; Drosophila database [Flybase], http://
flybase.bio.indiana.edu; and Edinburgh Mouse Atlas Project,
http://genex.hgu.mrc.ac.uk). These bio-ontologies ofthe phenotype (Gkoutos et al. 2004), or "anatomies" (Bard 2005), have
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sprung up without significant input from comparative evolutionary morphologists or systematists.
To address many evolutionary questions (box 1), species'
phenotypes must be compared. Unlike the phenotypes contained in developmental databases, the phenotypes of evolutionary biology are not the result of short-term laboratory
perturbation or mutation, but rather the result of millions of
years of natural mutation. Phenotypic variation across species
is much broader than that observed within a mutated model
species. To understand the evolutionary changes in the developmental genetic and epigenetic inputs that are involved
in building and individuating species, phenotypic ontologies
for individual species must be connected to each other, as well
as to ontologies at lower biological levels (e.g., GO). Evolutionary comparisons operate at a high tier in systems biology,
namely, at the level of continuity (and modification) of the
phenotype across the tree of life. Similarity among phenotypes
is due to the continuity of inherited information (i.e., homology; Van Valen 1982, Roth 1984). Homology is an evolutionary concept; homologous features are similar because
they are inherited from a common ancestor—that is, they are
similar because of their common genealogy. Homology, a
common ground for developmental and evolutionary biologists, is central to all comparative biology (Bock and Cardew
1999). It is central in phylogeny reconstruction, because characters have to be homologues to achieve a correct hypothesis of evolution using phylogenetic methods. The invisible
evolutionary threads connecting organisms are those of continuity—of homology—and thus ultimately ontologies must
be connected through homologous features or modules.
The developmental genetic basis of
convergent morphological change
There is some evidence—and it is becoming almost a central
tenet of evo-devo—that the evolution of regulatory elements
is the primary source of phenotypic change during the course
of evolution. Regulatory elements, including cis elements,
enhancers, promoters, and trans regulatory factors, in combination with a host of other proteins that are part ofthe transcriptional machinery (Brivanlou and Darnell 2002), activate
or repress gene transcription. Recent molecular advances in
several model systems have demonstrated the correlation of
morphological variation with variation in regulatory regions.
These regulatory elements appear to be highlyflexible,and
evolutionary changes in their roles and expression domains
seem fairly common. Regulatory mutations in key developmental control genes appear to provide a general mechanism for selectively altering expression in specific structures
while preserving expression at other sites required for viability
(Carroll 2000, Stern 2000, Tautz 2000). Several recent studies of evolutionary changes in the regulatory elements underlying phenotypic evolution (Gompel and Carroll 2003,
Sucena et al. 2003, Shapiro et al. 2004) provide examples of
making the much-needed connection from genot)^e to phenotype. Interestingly, these studies come from examining
the development of parallel or convergent similarity within
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small clades, as opposed to the focus on novel morphologies
(e.g., the pentameral echinoderm body plan from a bilateral
ancestor; Arenas-Mena et al. 2000, Peterson et al. 2000, Popodi
and Raff 2001) that has characterized much ofthe research
in evo-devo.
Convergence describes cases in which similar derived morphologies are produced from different ancestral morphologies, though the terms convergence and parallelism are used
interchangeably in several subdisciplines of biology (Wiens
et al. 2003), including evo-devo. Regulatory changes are involved in the convergent loss of features (Sucena et al. 2003,
Shapiro et al. 2004). The evolutionary modification of "just
a few developmental hotspots" (Richardson and Brakefield
2003) may underlie parallel evolutionary changes at the phenotypic level (Richardson and Brakefield 2003, Sucena et al.
2003). The examples below demonstrate such modifications,
and they provide an opportunity to describe how advances
in bioinformatics can significantly enhance understanding of
these processes.
Morphological convergent reduction in pelvic fins of three-
spine sticklebacks. Marine threespine sticklebacks, Gasterosteus aculeatus, have given rise to freshwater stickleback
populations that have evolved complete or partial loss ofthe
pelvic skeleton in less than 10,000 generations. Shapiro and
colleagues (2004) identified a major chromosome region
controlling loss of pelvic structures in a natural population
of sticklebacks. They found an altered pattern of expression
for the gene Pitxl in some tissues but not in others, consistent with a regulatory mutation that disrupts expression in
both the prospective pelvic region and the caudal fin. Their
data suggest that crs-acting regulatory mutations in Pitxl are
a major cause of pelvic reduction in this rapidly evolving system; the coding regions of Pitxl are unchanged between
populations with and without pelvic fins. In this and many
other cases in which evolutionary changes have been traced
to regulatory alterations in major developmental control
genes, the actual DNA sequences responsible for tissuespecific expression differences are unknown. Regulatory
mutations are much more difficult to identify at the molecular level, because the regulatory regions can be located far
from the gene of interest. The upshot is that it appears that
similar regulatory mutations in key developmental control
genes are responsible for the parallel morphological changes
in fins during stickleback evolution.
With the appropriate bioinformatics resources in place, researchers could quickly find all of the examples of evolutionary loss of the pelvic fins in fish evolution. They could
quickly access and examine images of pelvic fins in the most
recent ancestors of thefishesthat lost them, and they could
see stages (when they exist) in the gradual loss of such structures. The genetic underpinnings of pelvic fin loss in sticklebacks, which is known, could be compared with that in
examples of natural mutation, and in fact the stickleback
genes and regulators could be used as candidates to examine
in otherfishes.By connecting the breadth of parallel evoluwww. biosciencemag. org
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tionary phenotypes with the developmental genetic depth
from a well-understood system, important, overarching evodevo questions (box 1) can be answered.
Morphological convergent loss of larval hairs in Drosophila.
The pattern of trichomes (larval "hairs") on the dorsal and
lateral cuticle varies among the larvae of various Drosophila
species; "thin" trichomes have been lost to varying degrees in
four species in the D. virilis clade. When the presence or absence of thin trichomes is mapped (Sucena et al. 2003) on a
phylogeny for the group (Spicer and Bell 2002), at least three
evolutionarily independent losses of trichomes are required.
It had been previously determined that the transcription
factor Svb (shavenbaby/ovo, svb/ovo) acts to switch cells between naked cuticle and the production of trichomes (Payre
et al. 1999). Using in situ hybridization, Sucena and colleagues (2003) demonstrated that the expression of svb is
strictly correlated with the pattern of trichomes on the cuticle along the anterior-posterior axis. In other words, in naked
species (those lacking thin trichomes), svb transcription is absent and the corresponding cells differentiate naked cuticle.
Combining this finding with data from intraspecific crosses,
Sucena and colleagues (2003) concluded that the transcriptional enhancer that promotes svb expression in the domain
that produces thin trichomes is turned off in the lineages that
lack the trichomes. Regulatory changes in svb expression are
involved in all cases of parallel loss in the D. virilis clade.
Although determining exactly how the cis-regulatory region
of svb has been altered will require identification and characterization ofthis genomic area in these species—which, as
in the case ofthe stickleback, is technically difficult—it is clear
that parallel changes in the regulation ofthe svb/ovo gene underlie all independent cases of morphological convergence.
Like the stickleback fish example, this demonstrates that
apparently identical developmental genetic mechanisms may
underlie parallel evolutionary changes. Although evolutionary biologists have long recognized that identical developmental genetic mechanisms may not underlie homology
(Roth 1988), the finding that identical developmental regulatory changes may, in fact, underlie parallelism underscores
the need for phylogenetic information in determining homology (Sucena et al. 2003). If only the developmental genetic
information, and not the phylogenetic information, were
available for the D. virilis species group, the absence of trichomes at the first larval stage would be considered homologous, because changes in regulation ofthe same gene (svb)
produce this pattern. Phylogeny, however, indicates that this
loss must have happened independently, at least three times.
Sucena and colleagues (2003) point out that the modular nature of svb regulatory regions permits alterations in part of
the cuticular pattern without pleiotropic consequences (Stern
2000, Sucena and Stern 2000). Caution is advisable even
when phylogenetic history and morphology indicate a common (homologous) developmental basis. There are now several examples of morphology, such as vulva position in
nematodes (Sommer 2000, Rudel and Sommer 2003) and
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wing polyphenism in ants (Abouheif and Wray 2002), in
which differently modified gene networks underlie apparent
homology.
As in the first example, with the appropriate bioinformatics resources in place, researchers could quickly query for
data across insect taxa to find other examples of such phenotypic changes in evolution. They could promptly examine
images of morphological features, and again, the well-known
set of genes and regulators could be searched for parallel use
at a broader level.
Pigment and trichome patterns in flies. On a phylogeny for 13
Drosophila species, Gompel and Carroll (2003) mapped the
pattern of melanie pigment and trichomes on the pupal abdominal segments against aspects oibab2 gene regulation. They
inferred from phylogeny and character distribution that, ancestrally, Bab2 repressed pigment formation and also regulated
trichome development in the abdominal epidermis. In the evolution ofthe group, males of several species evolved repression
of bab in the posterior abdomen (allowing pigment to form).
Convergence in pigment pattern was achieved through
similar, independent regulatory changes in bab. Additionally,
from the phylogeny they could see a spectrum of speciesspecific modulations of Bab2 expression. This showed them
that bab controls pigment and trichomes independently, and
that their evolution can be uncoupled. They suggested that
changes in cis to bab2, in regulatory elements responding
directly or indirectly fo one or more body plan regulators, were
responsible for the diversification of Bab2 expression.
Like the first two examples, this detailed study of fruit
fiies adds potential developmental genetic depth to fhe multitude of parallel changes across insects. Ontologies would provide a link across these databases and enable comparative
morphologists and developmental geneticists to exchange
information.
Many of the examples of convergent evolution in evodevo have involved the loss or reduction of a particular morphological feature within a small clade, and they show that
parallel changes in genetic regulatory networks underlie the
loss of morphological features such as eyes in cavefish, wings
in ants, and pelvicfinsin sticklebackfishes(Carroll et al. 2001,
Abouheif and Wray 2002, Jenner 2004, Yamamoto et al. 2004,
Tian and Price 2005). Convergence in regulatory gene expression domains, which is probably more common than is
generally acknowledged, can arise for several different reasons
(Wray 2002). The ease with which a morphological feature
may be lost is consistent with the evolutionary idea that the
loss ofa morphological feature is more probable than gain (reviewed in Jenner 2004), which in systematics has been formalized as Dollo's law (Farris 1977). If these empirical
developmental data are representative, they support the use
of this assumption in phylogenetic inference.
Conclusions
Understanding the interplay between developmental and
phylogenetic constraints in the evolution ofthe phenotype is
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an overarching goal ofthe field of evo-devo. The examples of
convergent evolution related above have shown that parallel
changes in genetic regulatory networks underlie the loss of
morphological features. Whether this will prove true across
disparate phylogenetic levels will require additional empirical research—research at the genomic and phenotypic levels
of multiple species—and informatics to facilitate data mining and analysis. Practically, shared ontologies for homologous parts, characters, or modules ofthe phenotype must be
referenced, so thaf growing databases in genomics and evolution can connect and help address evolutionary questions
(box 1). Currently, developmental biologists and evolutionary morphologists individuate or parse the phenotype differently (Geefa 2003), but bioinformatics efforts are under way
to facilitate communication across disciplines. Better analytical
tools and more powerfiil methods of analysis wiU make it possible to understand the staggering level of complexity underlying phenotypes and the constraints of the developmental
genetic architecture thaf underlie phenotypic modules. Bioinformatics methods that facilitate visualization of multidimensional genotype and phenotype data simultaneously
(Tao et al. 2005) are crucial to biological research in this
postgenomic era.
Acknowledgments
I thank Matthew Greenstone for his encouragement in developing this overview, and Patricia Crotwell and anonymous reviewers for their suggestions.
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