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First published online April 20, 2007
Journal of Experimental Biology 210, 1567-1575 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.002592
Review Article |
Variable gene expression in eukaryotes: a network perspective
Department of Ecology and Evolutionary Biology, Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
e-mail: wittkopp{at}umich.edu
Accepted 23 January 2007
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Summary |
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 TOP Summary IntroductionThe structure of regulatory...Regulatory variation in a...Relating expression differences...References |
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Key words: gene regulation, transcription, cis-regulation, trans-regulation
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Introduction |
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TOPSummaryIntroductionThe structure of regulatory...Regulatory variation in a...Relating expression differences...References |
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; King and Wilson,
1975), it is only in the last decade that the tools necessary to
study changes in gene expression on a large-scale have become available
(Schena et al., 1995). These
tools, particularly DNA microarrays, have opened new areas of exploration for
comparative physiologists studying both model and non-model systems (reviewed
in Cossins et al., 2006).
With the rapid accumulation of studies analyzing transcript levels, it is
important to remember that transcription is only one step (albeit a critical
one) in converting genotypes into phenotypes; changes in transcript levels do
not always affect phenotypes and vice versa. The limitations of
transcript analysis have been discussed in detail
(Feder and Walser, 2005).
Despite these limitations, comparative studies of gene expression have
provided insight into the molecular mechanisms of ecological responses and
phenotypic evolution. These comparative studies can be divided into three
groups: comparisons within genotypes, within species and between species.
(1) Differences in gene expression can be created by environmental cues
without any genetic differences. Comparative studies of individuals exposed to
different environments reveal changes in gene expression associated with
physiological responses to external stimuli. The utility of this approach is
illustrated by a recent study of the eurythermic goby fish (Gillichthys
mirabilis) reared under multiple temperature regimes mimicking wild
conditions (Buckley et al.,
2006). Approximately 2% of the genes surveyed showed a change in
gene expression between treatments, with the specific genes showing altered
expression varying among tissues. Individual expression changes observed in
this study (and in similar studies) may be associated with positive
physiological adjustments that help an organism cope with its surroundings,
may reflect a stress response to adverse conditions, or may be consequences of
pleiotropy that have no direct role in adjusting to different temperatures.
Additional experiments are required to determine which expression changes fall
into which class.
(2) Under standardized environmental conditions, genetic polymorphisms can
cause expression differences within a species. Studies comparing genetically
distinct samples of yeast, fruit flies and fish reared under similar
laboratory conditions found that up to 25% of genes vary in their expression
level between individuals of the same species
(Brem et al., 2002;
Jin et al., 2001;
Oleksiak et al., 2002). These
expression differences include both neutral polymorphisms and variation that
contributes to phenotypic variation. For example, polymorphic gene expression
explains variation in cardiac metabolism of the teleost fish Fundulus
heteroclitus, demonstrating the adaptive potential of regulatory
variation (Oleksiak et al.,
2005). Putative adaptive changes in gene expression have also been
observed in experimental populations of microorganisms (e.g.
Cooper et al., 2003;
Ferea et al., 1999;
Riehle et al., 2003). Although
the number of case studies demonstrating phenotypic consequences for
regulatory changes is growing, the proportion of regulatory variation that is
advantageous, neutral and deleterious remains subject to debate (e.g.
Fay and Wittkopp, in press;
Gilad et al., 2006;
Lemos et al., 2005;
Ranz and Machado, 2006).
(3) Genetic divergence between species also creates differences in gene
expression. Regulatory evolution has been shown to contribute to divergent
traits such as body armor in stickleback fishes
(Colosimo et al., 2005), ear
shape in maize (Hubbard et al.,
2002) and pigmentation in fruit flies
(Wittkopp et al., 2003). Up to
25% of genes differentially expressed between closely related
Drosophila species show patterns of expression variation consistent
with lineage-specific selection (Rifkin et
al., 2003). Meta-analysis of comparative genomic expression data
concludes that regulatory evolution is characterized by strong stabilizing
selection with directional selection on some genes
(Lemos et al., 2005).
All changes in gene expression result from modifications to regulatory
networks. To understand the genetic and molecular mechanisms responsible for
expression differences, we must examine the structure of regulatory networks
and investigate how changes in these networks alter gene expression.
Elucidating the architecture of regulatory networks will reveal connections
among genes and is expected to uncover properties of regulatory networks that
make certain types of changes more or less likely to occur
(Wittkopp, 2005). Here, I
review basic structural features of eukaryotic regulatory networks and use
selected case studies to examine how networks vary between environments,
between genotypes, and between species.
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The structure of regulatory networks |
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TOPSummaryIntroductionThe structure of regulatory...Regulatory variation in a...Relating expression differences...References |
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).
Regulatory interactions
Molecular interactions between genes and gene products form the connections
that make up a regulatory network (Fig.
1A). Sequence-specific interactions between transcription factor
proteins and cis-regulatory DNA sequences provide the basic network
structure (Blais and Dynlacht,
2005). Binding sites for 106 transcription factors have been
mapped genome-wide in the baker's yeast Saccharomyces cerevisiae
(Lee et al., 2002). The number
of target genes for a given transcription factor ranged from 0 to 181 in this
experiment, with an average of 38 putative cis-regulatory targets per
transcription factor. Two-thirds of the transcription factors surveyed had
less than 40 targets each. Although full genomic surveys have not yet been
completed in metazoans, smaller scale studies have been conducted in
Drosophila melanogaster (Moorman
et al., 2006) and Caenorhabditis elegans
(Deplancke et al., 2006).
These studies suggest that the number of regulatory factors per gene may be
higher in multicellular eukaryotes than in yeast.
|
;
Li et al., 2004;
Uetz et al., 2000) reveal a
scale-free network structure (i.e. few genes with many connections and many
genes with few connections) similar to that seen for transcription factor
targets (Albert, 2005). In
animals, an additional layer of regulatory control is provided by small,
noncoding, microRNA molecules that affect transcript levels by altering
chromatin state or modulating mRNA stability
(Ke et al., 2003;
Stark et al., 2005).
Local motifs
Despite the many possible arrangements of regulatory factors and their
target genes, five common motifs (Fig.
1B) have emerged from analyses of transcriptional regulatory
networks in yeast (Lee et al.,
2002): (1) Feed-forward loop, which involves three genes. Gene A
regulates gene B, and together they regulate gene C. This motif is
over-represented in transcriptional networks
(Milo et al., 2002), and has
properties well-suited to transcriptional regulation
(Mangan and Alon, 2003). (2)
Single input module, which features a single transcription factor that
activates expression of a group of target genes. This type of motif is often
associated with genes that respond to exogenous signals
(Luscombe et al., 2004). (3)
Multiple input module, which describes cases where the same group of
regulatory factors controls expression of a battery of target genes. Genes
that regulate embryonic development in D. melanogaster display this
type of regulation (Erives and Levine,
2004). (4) Autoregulatory and feedback (multi-component) loops;
these describe cases in which a gene product regulates expression of the gene
encoding it, either directly (autoregulation) or through interaction with
other genes (feedback loops). These motifs provide stability to patterns of
gene expression (Becskei and Serrano,
2000). (5) Regulatory chain: cascades of regulatory interactions
in which gene A regulates gene B, which regulates gene C, which regulates gene
D, and so on. This motif contributes to the hierarchical structure of
regulatory networks. All five of these regulatory motifs are also found in the
regulatory networks of metazoans (e.g.
Davidson et al., 2003;
Levine and Davidson, 2005;
Stathopoulos and Levine,
2005).
Modules: trait-specific pathways
Groups of genes regulating the same phenotype can be placed together into a
pathway (Fig. 1C).
Historically, pathways have been defined genetically by ordering the action of
mutations that disrupt the same phenotype. When biochemical interactions
responsible for these genetic effects are identified, the pathways can be
integrated with transcriptional regulatory networks.
In multicellular animals, the two best-understood regulatory networks
– at both the genetic and biochemical levels – control mesoderm
development in sea urchins and embryonic patterning in D.
melanogaster embryos (Levine and
Davidson, 2005). These pathways share features thought to be
representative of developmental regulatory systems in general
(Davidson et al., 2003;
Stathopoulos and Levine,
2005). For example, developmental pathways have a hierarchical
structure with genes controlling the earliest regulatory events at the top and
genes controlling the final differentiation at the bottom. Different
functional classes of proteins act at different levels in these hierarchies,
with genes encoding transcription factors and signaling molecules near the top
and genes encoding enzymes and structural proteins at the bottom. Often,
regulatory interactions that initiate a developmental program are followed by
multi-gene feedback loops that maintain differentiated states. Both positive
and negative regulators operate in these pathways and contribute to the
robustness of developmental pathways. The wing development pathway of D.
melanogaster, depicted in Fig.
1C, illustrates all of these features.
Genomic networks
Regulatory factors that function in multiple pathways link trait-specific
pathways together to form a complex genomic regulatory network
(Fig. 1D). These common
regulators can create pleiotropy within the network. However, independent
control of gene expression in different pathways can minimize pleiotropic
effects and generate modularity. The modularity of regulatory networks is a
critical property that facilitates evolutionary change
(Carroll et al., 2001).
Like developmental pathways, genomic networks also have properties that
appear to be shared among eukaryotes. For example, all known regulatory
networks share a scale-free distribution with a small number of highly
connected genes (i.e. `hubs') and many genes with few connections
(Albert, 2005). Genomic
networks also have a hierarchical structure similar to individual
developmental pathways (Yu and Gerstein,
2006) (Fig. 1E).
Highly connected nodes occur at the top and middle of the hierarchy with
minimally connected `terminal nodes', which do not directly impact regulation
of other genes, at the bottom. The similarity of genetic architecture among
species may result from shared ancestry, selection for an optimal design, or
(most likely) both. Simulation studies have shown that the structure of
regulatory networks confers a robustness and stability in the face of genetic
and environmental perturbations known as `canalization'
(Hornstein and Shomron, 2006;
Siegal and Bergman, 2002).
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Regulatory variation in a network context |
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TOPSummaryIntroductionThe structure of regulatory...Regulatory variation in a...Relating expression differences...References |
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Environmental effects on gene regulation
A recent study investigating the effects of alcohol exposure on gene
expression in the fruit fly D. melanogaster illustrates the power of
using microarrays to understand physiological changes
(Morozova et al., 2006). 3% of
transcripts were found to have significant expression differences between
flies that were and were not exposed to ethanol. Genes involved in olfaction,
signal transduction, metabolism, transcription regulation, circadian rhythm
and pigmentation changed expression more often than expected by chance. Some
of these categories, such as olfaction and metabolism, fit prior expectations
for the types of physiological changes induced by ethanol. Other classes, such
as pigmentation genes, may represent secondary pleiotropic consequences of the
network structure or genes whose functions are incompletely characterized.
To determine the fraction of genes with altered expression that
functionally mediate the response to ethanol, mutant strains for 20 of the
affected genes were tested for ethanol tolerance
(Morozova et al., 2006). 15 of
the mutants had an ethanol sensitivity that differed from that of a control,
non-mutant strain. These data suggest that the majority of genes whose
expression is affected by ethanol exposure contribute to ethanol tolerance. Up
to 25% of the genes with altered expression, however, appear to be side
effects resulting from pleiotropic connections in the underlying regulatory
network. Pathways controlling expression of genes induced by external stimuli
(e.g. ethanol) have been shown to contain a small number of transcription
factors directly regulating expression of a collection of functionally related
genes (Luscombe et al., 2004).
This structure may contribute to the high specificity of expression changes
induced in response to ethanol.
Compared to networks regulated by exogenous cues, networks controlling
development and basic cellular processes tend to be controlled by more
regulators, with extensive interactions and feedback loops among the
regulators (Luscombe et al.,
2004). This structure provides a variety of mechanisms for
altering the output of regulatory systems. The developmental basis of
polyphenism in ants illustrates this point
(Abouheif and Wray, 2002).
Exposure to juvenile hormone causes genetically identical individuals to
develop into any one of three castes (i.e. reproductive, worker, soldier). Of
these, only the reproductive caste has wings. Using the regulatory networks
controlling wing development in D. melanogaster as a guide, patterns
of gene expression for wing developmental genes were compared between winged
and wingless castes. The point at which wing development is disrupted was
found to differ between the two wingless castes as well as between the two
developing wing discs within one caste. In one case, expression was disrupted
only in a gene located at the bottom of the network, whereas in the other
case, the developmental pathway was blocked at a much higher step in the
pathway (Fig. 2). Expression
changes affecting wing development do not interfere with other functions of
these pleiotropic proteins because of the modularity in regulatory
networks.
|
). To
determine the molecular mechanisms by which genetic variants affect gene
expression, the location an eQTL is compared to the location of the affected
gene. If the eQTL is located near the affected gene, the change is inferred to
act by modifying cis-regulation. If the eQTL is located elsewhere,
the genetic change is inferred to act by modifying trans-regulation.
Although discriminating between cis- and trans-acting
changes does not identify the precise change within the regulatory network, it
does separate changes at the gene itself from those residing in other
factors.
The majority of eQTL identified in studies ranging from yeast to humans had
trans-acting effects on gene expression (e.g.
Brem et al., 2002;
Cheung et al., 2005;
Monks et al., 2004;
Morley et al., 2004;
Schadt et al., 2003). Yvert et
al. characterized these trans-acting eQTL in yeast and found that
eQTL for different genes often map to the same genomic region
(Yvert et al., 2003). Assuming
the causative sites responsible for the coincident eQTL are all located within
the same gene, regulatory variation appears to be concentrated at highly
connected hubs in the network. Indeed, only 100–200 distinct genes are
estimated to account for all of the 1716 trans-acting eQTL identified
in this study. The authors hypothesized that transcription factors with many
target genes were responsible for these widespread effects; however, this
class of proteins was found not to be over-represented near clustered eQTL.
This is perhaps not surprising because a variety of biochemical classes can
serve as hubs in regulatory networks. For example, a mutation in a
receptor-associated G protein was shown to be responsible for one of the eQTL
`hotspots'.
The high frequency of trans-acting eQTL with widespread effects
could be due to a biased mutational process, or to a higher fitness for
changes in hubs relative to other types of genes. Studies of mutation
accumulation (MA) lines in the nematode Caenorhabditis elegans
suggest that the mutational process itself may produce many variants with
pleiotropic, trans-acting effects on gene expression
(Denver et al., 2005). MA
lines were created using single hermaphrodites to propagate independent lines
derived from the same starting genotype for many generations. This procedure
maintains all but the most severe mutations. After 280 generations, expression
was compared among four MA lines. 9% of the genes were found to have evolved
in expression differences in at least one line. By contrast, only 2% of genes
were found to have expression differences among distantly related C.
elegans strains isolated from the wild, demonstrating that natural
selection eliminates many regulatory mutations. In the MA lines, but not in
natural isolates, co-expressed genes were over-represented among genes with
altered expression. Expression differences for groups of co-expressed genes
were also observed in a similar mutation accumulation study of D.
melanogaster (Rifkin et al.,
2005). These findings suggest that only a few regulatory mutations
with effects on multiple downstream genes are responsible for the extensive
expression divergence observed. Some groups of coregulated genes changed
expression in multiple mutation accumulation lines, suggesting that the
portions of the network controlling expression of these genes are particularly
susceptible to regulatory mutations. Because selection is minimal in mutation
accumulation lines, the structure of the regulatory network is expected to
control the distribution of regulatory mutations within the genome.
Expression divergence between species
Regulatory networks evolve by changing which genes interact as well as by
changing how these genes interact (Babu et
al., 2004). For example, cis-regulatory sequences may
switch binding sites from one transcription factor to another, mutations in
protein–protein interaction domains or within the DNA binding regions
may abolish a connection in the network, or evolution of microRNA sequences
may generate new target genes. Gene duplications and deletions can also add
and subtract entire regulatory modules. Comparative studies of regulatory
networks controlling development have identified divergent steps as well as
sets of highly conserved regulatory interactions (reviewed in
Davidson and Erwin, 2006).
Differences in network structure between species may cause expression
divergence, but may also reflect silent changes characteristic of
developmental system drift (True and Haag,
2001). The presence of similar regulatory motifs and scale-free
properties in regulatory networks from diverse eukaryotes suggests that
regulatory connections are rearranged in a manner that has minimal effect on
the overall network architecture. Mutations affecting the kinetics of
individual regulatory interactions are expected to impact network output (i.e.
gene expression) without altering its structure.
Interspecific comparisons of gene expression can be used to identify
regulatory changes contributing to phenotypic divergence. Expression
differences that correlate with phenotypic diversity have been observed for
genes encoding transcription factors at the top of hierarchical pathways (e.g.
Abzhanov et al., 2004;
Averof and Patel, 1997;
Gompel and Carroll, 2003;
Sucena et al., 2003) as well
as genes encoding enzymes at the bottom of pathways (e.g.
Dickinson et al., 1984;
Wittkopp et al., 2002). For
all divergent phenotypes analyzed to date, only a subset of genes (often only
one gene) in the developmental pathway is compared between species.
Consequently, it remains unknown whether regulatory changes tend to cluster at
the top, bottom or middle of a pathway.
Distinguishing between cis- and trans-acting variants is
the first step for locating regulatory changes within a network. For a given
gene, a cis-regulatory change indicates that the variant is
associated with the gene surveyed. A trans-regulatory change
indicates that the primary difference is located in a gene functioning
upstream in the pathway. Transgenic and genetic studies comparing
interspecific expression differences have shown that both cis- and
trans-regulatory differences are common, regardless of where the gene
fits within the regulatory pathway (Table
1). Consistent with these data, allele-specific analysis of 29
genes with expression differences between two Drosophila species
found that 97% of genes with expression differences were affected by
cis-regulatory divergence and approximately half showed evidence of
trans-regulatory changes
(Wittkopp et al., 2004).
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Relating expression differences to underlying regulatory networks |
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TOPSummaryIntroductionThe structure of regulatory...Regulatory variation in a...Relating expression differences...References |
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(1) Many changes in gene expression are often induced by an environmental change. Some of the changes may be directly involved in the physiological adjustment while others may be secondary consequences of the regulatory network. Comprehensive descriptions of regulatory connections will help disentangle these types of changes by revealing connections between functional modules. However, genetic mapping and functional tests will ultimately be needed to identify the subset of genes for which expression changes impact the phenotype.
(2) Regulatory changes may be most stable when located in particular parts of a pathway. To test this hypothesis, complete pathways controlling divergent traits should be surveyed to locate all independent regulatory changes within the network. Locating regulatory variants will also make it possible to determine whether the connectivity of a gene within the network influences its propensity for change.
(3) The distribution of new regulatory mutations within a network appears
to differ from the distribution of regulatory variants in the wild
(Denver et al., 2005). Network
architecture is expected to influence how regulatory variation arises, while
the pleiotropic side effects of individual regulatory mutations are expected
to influence which changes survive the test of time. To fully appreciate the
impact of network architecture on evolutionary trajectories, properties that
promote particular changes within regulatory networks must be identified.
(4) Some functional classes of genes may be more susceptible than others to
regulatory mutations affecting their expression. Analyzing the distribution of
regulatory variants among genes with different gene ontology designations will
test this hypothesis. Such an analysis may also identify specific biological
functions with a propensity for regulatory changes (e.g. sperm expressed genes
in C. elegans) (Denver et al.,
2005). However, any analyses using current gene ontology
designations should be interpreted cautiously. At present, for most genes,
gene ontology assignments of functional classes and biological processes are
predicted solely based on sequence similarity and are awaiting genetic and/or
biochemical verification.
As discussed in this review, existing case studies provide some insight into these issues. However, we have a long way to go toward understanding how regulatory variation is distributed within genomic regulatory networks and how network structure influences patterns of variable gene expression. A combination of genetic and biochemical dissection of regulatory networks in model systems, computational analyses of network properties, and comparative studies of gene expression among non-model species will be needed to resolve these issues. Given the recent growth in these research areas, a comprehensive understanding of regulatory variation in the context of regulatory networks may soon be achieved.
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Acknowledgments |
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Footnotes |
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