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From Genotype × Environment to
Transcriptome × Environment: Identifying
and Understanding Environmental Influences
in the Gene Expression Underlying Sexually
Selected Traits
Jennifer C. Perry1 and Judith E. Mank2
1 Department
2 Department
London, UK
of Zoology, Edward Grey Institute, University of Oxford, UK
of Genetics, Evolution and Environment, University College
7.1 Introduction
One of the major goals of evolutionary biology is to link adaptive phenotypes to
their underlying genes. One complication in doing this is the genotype-phenotype
relationship is usually not constant, but instead varies with factors such as
developmental influences, abiotic conditions, and ecological variation, including
resources and risk of extrinsic mortality. Such genotype-by-environment interactions (GEIs) are typically identified by exposing individuals of a particular
genotype (for example, resulting from inbred lines or a specific breeding design)
to two or more environmental conditions, and testing for differences among
genotypes in the slopes of the reaction norms to environmental variation.
Like other traits, sexually selected traits can show GEIs, and this has interesting
implications for their evolutionary dynamics and equilibria. Although theoretical
and empirical study of GEIs in sexually selected traits is just beginning, several
recent studies have detected GEIs for a variety of sexually selected traits in a
diverse range of taxa (reviewed by Ingleby et al., 2010), suggesting that this
pattern may be common.
Genotype-by-Environment Interactions and Sexual Selection, First Edition.
Edited by John Hunt and David Hosken.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
Companion Website: www.wiley.com/go/hunt/genotype
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Recent developments in next generation sequencing technologies have revolutionized molecular and behavioral ecology and evolutionary genetics (Boake
et al., 2002; Springer et al., 2011), permitting new approaches to classic questions and expanding the set of questions we can ask. These new genomic tools
both pose new challenges and offer great opportunities to expand on traditional
GEI studies. In this chapter, we review the application of genomic approaches
to understanding GEIs in sexually selected traits. We focus on gene expression
because we believe recent transcriptomic methods offer the best opportunities for
new discoveries in this field. We address methodological concerns, outline best
practices for experimental design, and review predictions for transcriptome-byenvironment interactions (TEIs) in sexually selected traits.
7.2
Gene expression variation allows a static genome
to respond to varying environments
At the molecular level, phenotypes are the protein products of genes. Because
protein chemistry is complex and often difficult to work with, gene expression,
as measured by RNA abundance, is a commonly used proxy for protein levels.
The evolution of gene expression is an important mechanism of adaptive phenotypic change (Chan et al., 2010; Manceau et al., 2011), and gene expression is
therefore a key intermediary component of the genome-phenotype relationship.
Gene expression itself is a complex mix of regulatory control (Pickrell et al.,
2010) and epigenetic influences (Mikkelsen et al., 2007; 2008), though at the
moment, many studies ignore the cause of gene expression variation and simply
measure gene expression itself.
Gene expression regulation is the mechanism by which a single fixed genome
can generate different phenotypes, by varying the amount of protein from
encoding genes. Adaptively altering gene expression lets an individual alter
its phenotype to respond to varying environmental stimuli, allowing a static
genome to track changing environments. Gene expression assays have clearly
demonstrated that environmental influences are a major determinant of RNA
abundance in every organism so far assessed (Fendt et al., 2010; Harbison
et al., 2004; Kim et al., 2001; Musso et al., 2008; Nicolas et al., 2012; Smith &
Kruglyak, 2008). Based on this work, we know a good deal about the genes (Kim
et al., 2001; Musso et al., 2008) and gene networks (Fendt et al., 2010; Nicolas
et al., 2012) that respond to environmental influences in model organisms.
In addition to environmental variation, a second major influence on gene
expression is sex. In metazoans, sex explains the majority of gene expression
variation among samples, and male and female expression levels differ significantly for a large proportion of genes in many animals (Malone et al., 2006;
Mank et al., 2010; Ranz et al., 2003; Reinke et al., 2004; Rinn & Snyder, 2005).
These differences can be used to identify the suites of genes that underlie sexually
dimorphic phenotypes (Innocenti & Morrow, 2010; Mank, 2009). Beyond
this broad approach, some studies have actually identified the gene expression
patterns underlying sexually selected traits in animals (Kopp et al., 2000; Offen
et al., 2008). Given that many sexually selected traits are condition-dependent
(Andersson, 1986; 1994; Bonduriansky & Rowe, 2005; Cotton et al., 2004;
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David et al., 2000; Delcourt & Rundle, 2011; Hill, 1990; Punzalan et al., 2008)
and condition (defined as the resources an organism has available to invest
in trait expression, following Rowe & Houle, 1996) is often a function of
resource availability and environmental challenges, studying sex-biased gene
expression is a potentially useful approach to both identifying the actual genes
underlying condition-dependent traits as well as understanding how these genes
functionally respond to environmental cues.
Given that gene expression varies tremendously in response to the environment
and sex, studying gene expression is potentially a very useful approach to understanding GEIs in sexually selected traits, and to move from the study of GEIs to
TEIs. A TEI can be interpreted as variation in the relationship between genotype
and gene expression in response to environmental variation (i.e., a GEI for the
phenotype of gene expression; reviewed by Hodgins-Davis & Townsend, 2009)
(Figure 7.1).
The tools are now becoming widely accessible to assess TEIs. Measuring gene
expression has recently become far easier and cheaper, and it seems that every
week, another review paper heralds the next-generation sequencing revolution
and promises that this technology will transform how we do evolutionary
and ecological studies (excellent recent examples include Ekblom & Galindo,
2011; Rokas & Abbot, 2009; Springer et al., 2011; Stapley et al., 2010).
The names of companies such as Illumina, 454, SOLiD, and Oxford Nanopore
are increasingly whispered with expectant promise in the literature and at
conferences. The technology is rapidly changing, and it seems that the research
horizon is constantly expanding. To some extent, the next-generation chorus
Genotype a
Gene expression
Genotype b
Genotype c
Environmental variation
Fig. 7.1 A transcriptome-by-environment interaction (TEI), akin to a genotypeby-environment interaction for gene expression. TEIs are indicated by differing slopes
of the reaction norms describing how gene expression responds to environmental
variation, which may change the magnitude of differences in gene expression among
genotypes (compare genotypes a and c), or even a different ranking of genotypes
(compare a and b), along an environmental gradient.
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is true: next-generation sequencing now makes it possible to take a molecular
approach to the genome-phenotype relationship that is at the heart of evolutionary biology, and transcriptome analysis expands the research horizon to include
functional gene expression (Harrison et al., 2012). These methods also loosen
the restrictions to model organisms that have hindered studies of ecologically or
evolutionarily relevant species.
RNA-seq is one next-generation sequencing method, which, if done well, offers
precise estimates of gene expression for all the genes expressed in a sample (Wang
et al., 2009), also called a transcriptome profile. Transcriptome profiling has
largely replaced microarray studies due to its superior accuracy and increasing
cost effectiveness, and in addition to expression level, RNA-seq datasets also
contain information on isoform variation and nucleotide variation in coding
sequence. RNA-seq is named for exactly what it does: total mRNA from a sample is sequenced en masse, and the number of reads detected from a given gene
is proportional to its abundance in the sample and hence its expression level.
In many ways, the technology seems perfectly designed to examine how environmental influences act on the genome to produce phenotypic variation. For studies
of sexually-selected traits, this is particularly exciting, as it allows us to search
for the functional basis of sexually dimorphic phenotypes. Although hurdles
still remain in the bioinformatic analysis of these large datasets, next-generation
methods are standardized at this point and the laboratory protocols are no longer
considered risky.
There are a few barriers to adopting next-generation approaches to studying
TEIs in sexually selected traits. First, because the methods are new, there are
relatively few roadmaps for experimental design and analysis, aside from stock
software programs that often need to be adapted to specific questions. Second,
there is currently a lack of explicit theoretical predictions about how TEI should
manifest in RNA-seq datasets, making it unclear what to expect and therefore
complicating traditional hypothesis-driven approaches. Many studies at the
moment must either extrapolate from theories based on phenotypic predictions,
making many untested assumptions in the process, or accept that we have entered
a stage of discovery that is a necessary precursor to explicit hypothesis testing.
Finally, the traditional focus on the phenotypic level in many areas of evolutionary ecology also presents a barrier, as it makes many researchers shy away from
genomic and molecular methods as expensive redundancies to cheaper phenotypic studies. We address these concerns later in the chapter. We will also discuss
the very important, but still hazy relationship between the transcriptome and the
phenotype, and what we might expect about the relationship between TEIs and
sexually selected traits.
7.3
From GEIs to TEIs in sexually selected traits
Several examples of GEIs in sexually selected traits are known (reviewed by
Ingleby et al., 2010), including eye-stalks in stalk-eyed flies (David et al., 2000),
courtship songs in lesser waxmoths (Danielson-Francois et al., 2006), and wing
length in Drosophila melanogaster (Wilkinson, 1987).
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All three of these examples relate the expression of a sexual trait to variation in resource availability, and therefore to variation in condition. In fact,
condition-dependence is likely to be an important source of GEIs in sexually
selected traits for two reasons. First, these traits are expected to display heightened condition-dependence compared to other traits (Alatalo et al., 1988; Rowe
& Houle, 1996; Cotton et al., 2004; Bonduriansky & Rowe, 2005). This prediction arises because individuals in good condition should be better able to
pay the marginal costs of further trait exaggeration or should gain higher fitness returns compared to poor-condition individuals (Getty, 2006; Grafen, 1990;
Iwasa et al., 1991; Proulx et al., 2002; Rowe & Houle, 1996). There is abundant
evidence supporting the condition-dependence of sexually selected phenotypes
(Andersson, 1994; Cotton et al., 2004; Bonduriansky & Rowe, 2005; Punzalan
et al., 2008).
Second, condition-dependence is likely to be a rich source of GEIs in trait
expression. Condition-dependence can generate three forms of GEIs that arise
because condition itself is the product of both environmental and genetic effects
and their interaction. Genetic variation in the allocation of condition to trait
expression may interact with either the environmental component of condition
or any GEIs for condition itself. These forms of GEI should contribute relatively little to phenotypic variation, because genetic variation in allocation is
expected to be slight compared to the genetic variation underlying condition
(Rowe & Houle, 1996). A third type of GEI, of potentially greater influence,
arises when genetic variation, at the many loci of small effect that contribute to
condition, interacts with environmental variation (e.g., resource availability) to
determine condition. Such GEIs for condition have in fact been detected in lesser
waxmoths (Achroia grisella; Danielson-Francois et al., 2006) and (weakly) in
blue tits (Merilä et al., 1999; see Tomkins et al., 2004 for a general review).
If GEIs for condition are sufficiently strong, then they will contribute important genetic variation to condition-dependent sexually selected phenotypes; thus,
studies conducted under constant environmental conditions will miss this source
of genetic variation (e.g., Schielzeth et al., 2012), with implications for the genetic
architecture they detect for sexual traits.
To translate these ideas about GEIs in sexually selected phenotypes to
the transcriptomic level, several issues require attention. First, the predicted
condition-dependence of sexually selected traits depends on the presence of
differential costs (or benefits) to individuals in good or poor condition, and we
therefore need to understand differential costs of gene expression. Although
the cost of low levels of transcription are probably minor, at some level the
investment in the transcription of genes underlying sexually selected phenotypes
must come at the cost of transcription of genes for other important traits. This
is because the cell can only invest so much in total transcriptomic effort and
highly expressed genes will monopolize the ribosome at the expense of other
highly expressed and essential genes, such as homeostatic, immunological, and
housekeeping genes. Evidence here is mixed. In the first (to our knowledge)
study of condition and sexually dimorphic gene expression, Wyman et al.
(2010) detected significant condition-dependence in sex-biased gene expression
in D. melanogaster: flies reared on a low-quality diet expressed fewer male- and
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female-biased genes, and overall less sexual dimorphism in gene expression,
compared to flies reared on standard food. These results support the hypothesis that transcription is indeed differentially costly or beneficial depending
on individual condition, and particularly so for sex-specific transcription.
More generally, the fact that gene expression is regulated and curtailed when
not needed suggests that transcription is costly per se. On the other hand, transcription is noisy, and there is often wide variation in gene expression without
noticeable phenotypic effect. Additionally, in deep RNA-seq datasets, every part
of the genome, from coding to non-coding to outright junk, is expressed at some
level (reviewed by Kapranov & St Laurent, 2012). This suggests that expression
costs are not entirely straightforward and therefore that condition-dependent
costs or benefits to gene expression may be difficult to detect.
Second, to put condition-dependent changes in gene expression in context, it
will be necessary to understand the magnitude of condition-dependent variation
compared to transcriptional variation related to other factors. For example, gene
expression can vary tremendously among tissues and developmental stages, as
well as in response to the environmental and social context, with many examples
referenced throughout this chapter.
A third issue complicating the study of TEIs is that there are currently very
few sexually selected traits for which the genomic basis has been identified and
this is particularly true for female traits and preferences. As a result, it is currently difficult or impossible to identify sexually selected traits in whole transcriptome datasets. In contrast, genes that show sex-bias in expression can be
readily identified, but the relationship between sex-biased genes, which encode
male- and female-specific phenotypes and result from sex-specific selection, and
the genes underlying sexually selected phenotypes is not clear-cut. It is certainly
the case that not all sex-biased genes will have been sexually selected, but in many
cases sex-biased genes do have similarities to sexually selected traits: they evolve
rapidly in a sex-specific manner consistent with the predictions of sexual selection theories (Ellegren & Parsch, 2007) and show condition-dependence (Wyman
et al., 2010).
Taken as a whole, these results suggest that we can proceed with caution in
extrapolating predictions from theory developed for sexually selected traits to the
transcriptomic level, until formal theory for adaptive plasticity in gene expression
is developed.
7.4
Can we safely ignore the genomic basis of phenotypes?
In many areas of evolutionary and ecological research, there is a widespread
assumption that there is no need to adopt molecular approaches, as they
will simply mirror phenotypic results. Indeed, we have ourselves been asked
numerous times, often by eminent evolutionary biologists, why we bother with
genomic studies when phenotypic analysis is sufficient. This outlook has its roots
in the phenotypic gambit (Grafen, 1984), which proposes that the evolution of
adaptive traits is not constrained by their genetic architecture. The phenotypic
gambit has been used with great success to understand the adaptive nature of
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many behavioral traits (Krebs & Davies, 1997; Parker & Maynard Smith, 1990;
West, 2009; Westneat & Fox, 2010). However, we believe that the focus on the
phenotype, perhaps coupled with apprehension towards unfamiliar genomic
techniques, has held back progress in integrating molecular approaches into
evolutionary ecology (see Boake et al., 2002; Springer et al., 2011). This is
unfortunate, because molecular and genomic approaches offer the potential for
great gains in understanding the adaptive basis of phenotypes, and evolution
is, simply put, genetic change over time. To many readers, this will hardly need
emphasizing.
For many phenotypes in general, and sexually selected traits in particular, many
interesting insights into trait evolution can arise from understanding the underlying molecular basis. For example, in many cases theoretical predictions for
sexual traits depend on the molecular genetic basis underlying traits. Models of
sexual conflict predict different outcomes for the maintenance of polymorphism,
extent of genetic differentiation, and displacement of female traits from their
optimum values depending on patterns of dominance, the number of contributing loci, and sex chromosome linkage (Arnqvist & Rowe, 2005; Hayashi et al.,
2007; Haygood, 2004; Kirkpatrick & Hall, 2004). Furthermore, pinpointing
the physical genomic location and genetic mechanisms underlying phenotypic
traits can reveal the evolutionary mechanisms that lead to evolutionary change.
For example, the gene egr-1 underlies the female response to sexual signals in
both swordtail fish (Cummings et al., 2008) and túngara frogs (Hoke et al.,
2008), revealing possible convergence or constraint that would have been undetectable without knowledge of the genetic architecture. In another example, QTL
and introgression studies have shown that genes for sexual signaling and female
preference are physically linked in Hawaiian crickets (Laupala spp.). This linkage
may enable the very rapid speciation rates observed in this group, providing a link
between the genetic architecture of sexual traits and higher-level evolutionary
processes (Shaw & Lesnick, 2009; Wiley et al., 2012).
7.5 The first step is identifying the transcriptomic
basis of sexually selected traits
Understanding TEIs in sexually selected traits requires first identifying the transcriptomic basis of these traits (Figure 7.2). Examining the transcriptomic basis of
traits of interest is a relatively new approach, and offers some unique advantages
over traditional methods for identifying a trait’s genetic architecture. The two
main traditional methods, QTL and candidate gene studies, have certainly been
very useful in genetic studies of sexual traits. They have revealed that while some
sexually dimorphic traits can have relatively simply single- or oligo-locus controls (Hubbard et al., 2010; Pointer & Mundy, 2008; Schielzeth et al., 2012;
Steiner et al., 2009), others are far more complex (Foerster et al., 2007; Kruuk
et al., 2002).
However, there are some limitations to QTL studies that make transcriptomic
approaches attractive. One major limitation is that loci must be genetically variable to be detected, and QTL studies may therefore underestimate the number of
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Identify gene
expression underlying
trait
Characterize how
gene expression
responds to
environmental
variation
Identify genetic
variation in the form
of response
• QTL mapping
• Transcriptome sequencing from target tissue
• Comparison with a control tissue, such as
individuals not expressing the trait
• Experimental manipulation of environmental
variables
• Paternal half-sib breeding design
• Evaluate gene expression reaction norms
Fig. 7.2 A schematic workflow for using next generation technology to assess TEIs in
sexually selected traits.
sites underlying complex phenotypes if some loci lack functional genetic variation. Additional limitations include the difficulty of narrowing a significant
genetic region to a few candidate loci (Chenoweth & Blows, 2006), the bias
towards detection of genes of large effect (Lynch & Walsh, 1998), and the tendency of QTL analysis to overestimate effect sizes and underestimate the number
of contributing loci (reviewed by Chenoweth & McGuigan, 2010).
Gene expression studies offer an alternative means to estimate the number of
loci contributing to phenotypes. Comparing transcriptomes from two alternative morphs, such as male and female, quickly identifies all those genes that
differ in expression and are therefore likely to have phenotypic consequences
Transcriptomic approaches can also identify exactly how males and females use
genes differently, from expression level (Ellegren & Parsch, 2007), to sex-specific
alternative splice variants (Blekhman et al., 2010), and even sex-differences in
expression of different alleles (Gregg et al., 2010a; Gregg et al., 2010b). Thus,
one can gain a rich understanding of the complexity of sexual dimorphisms.
Another advantage is that gene expression studies allow the study of natural variation, as opposed to candidate gene approaches that involve deletion mapping
or inducing mutations (reviewed by Chenoweth & Blows, 2006).
The approach of comparing male and female transcriptomes in order to identify
the genes that contribute to sex-specific phenotypes has taken off in recent years,
and there is a cottage industry involved in using this approach to measure relative sex-specific selection (Connallon & Clark, 2010; 2011b; Ranz et al., 2003;
Zhang et al., 2004), evolvability (Gallach & Betran, 2011; Mank & Ellegren,
2009a; Mank et al., 2008b; Meisel, 2011) and sexual conflict (Connallon &
Clark, 2011b; Innocenti & Morrow, 2010; Mank & Ellegren, 2009b). This has
revealed a great deal about how sex-biased genes evolve, even if the connection
to the phenotype is still tenuous (Connallon & Clark, 2011a).
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There are, however, at least three important complications in gene expression studies of sexually selected traits that should be considered. First, as we
mentioned previously, sex-biased expression is not the same as sexually selected
expression. Sexually selected genes will no doubt show sex-biased expression,
but it is possible that not all sex-biased genes are the product of sexual selection. However, all sex-biased genes are thought to be, at some level, the product
of sexual conflict (Mank et al., 2013), because opposing sex-specific selection
is required to break down the correlation between male and female expression.
The distinction between sex-biased and sexually selected gene expression represents an important area for future work, and it will no doubt be possible to parse
them more carefully soon.
A second issue is that our understanding of the transcriptome-phenotype
relationship remains a bit hazy without a more complete understanding of how
variation in expression level contributes to phenotypic variation. It is a gross
over-simplification to state that the phenotype is the sum of all expressed genes.
For example, studies in yeast show that a large proportion of genes, if knocked
out, do not have discernible phenotypic effects (Giaever et al., 2002; Papp et al.,
2004), at least under laboratory conditions. Additionally, not all genes have
dosage effects (Papp et al., 2003; Pollack et al., 2002), meaning that increases or
decreases in expression have no obvious phenotypic consequences. The inexact
understanding of the transcriptome-phenotype relationship makes biological
replicates vital for differentiating non-functional gene expression noise from
meaningful variation. Biological replicates from the same population that
control for age and sex generally show transcriptomic correlations up to, and in
some cases over, 90% (Moghadam et al., 2012), indicating that there is both a
great deal of consistency in expression among classes and individual variation. In
sum, at this point, there is very little data available about the correlation between
expression and phenotypic variation, and this requires further scrutiny.
A third and more practical issue is that transcriptomes vary across time and
space within an organism. This is particularly true for sex-specific transcriptomes:
they differ by developmental stage (Mank et al., 2010; Perry et al. 2014; Reinke
et al., 2004) and within different organs of the body (Mank et al., 2008a; Parisi
et al., 2004; Yang et al., 2006). This is fundamentally different than genomes,
which are, baring point mutations that arise within a cell lineage, consistent
across all somatic cells in an individual. Unlike QTL studies which are based
on genomic DNA, transcriptome studies must target the exact site of a sexually
selected trait, and they must sample that site at the correct developmental stage.
We cannot emphasize enough the importance of developmental stage in studies
of the transcriptomic basis of sexual dimorphism. Although sexual dimorphisms
and sexually selected phenotypes are most evident in adults, a significant proportion of these traits are the product of developmental processes that amplify
into adult phenotypes. For example, the horn in adult male horned beetles is not
the product of adult transcription, but of larval differences between the sexes
(Kijimoto et al., 2010; Moczek, 2006; Snell-Rood et al., 2011). Similarly, the
transcriptomic basis of the sword in male swordtails is largely finished by the time
the sword manifests fully in the phenotype (Offen et al., 2008). We might expect
similar developmental components to be important in other sexually selected
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traits, such as eye-stalks in stalk-eyed flies (Warren & Smith, 2007), sexual size
dimorphism in animals with determinate growth trajectories (Badyaev, 2002),
and those sexually dimorphic traits influenced by maternal effects (Altmann &
Alberts, 2005; Hunt & Simmons, 2000). These examples illustrate that it is crucial to sample the individual at the stage at which sexual dimorphisms are being
programmed in the phenotype, rather than the adult stage when the dimorphism
is simply most evident.
In some cases, though, the important underlying gene expression will occur
in the reproductively mature adult stage when the trait manifests. This should
clearly be the case for traits that are produced or maintained in the adult stage,
such as mating behaviors (e.g., Ellis & Carney, 2010; see following), sexual chemicals (e.g., accessory gland proteins in D. melanogaster, McGraw et al., 2007;
sexual pheromones in leafroller moths, Albre et al., 2012), adult diet-dependent
traits (such as color pattern in guppies; plumage color in the house finch, Hill
& Montgomerie, 1994), and secondary sexual structures like antlers (Molnár
et al., 2007). For traits like these that develop in the adult, it will be necessary
to identify the time within the adult stage when the gene expression underlying
these phenotypes is active. For example, many sexually selected plumage traits in
birds are expressed during the breeding season and lost during the non-breeding
season. In such species, it is not immediately obvious which season is most relevant for evaluating gene expression. The breeding season is clearly a place to
start, but it may be the case that the important transcription events occur prior
to breeding as resources are acquired.
To further complicate the choice of a time point for evaluating gene expression,
gene expression in the adult stage can also be the product of events that occur
earlier in development. For example, juvenile diet has a stark effect on the nature
of sexually dimorphic gene expression in the entire transcriptome (Wyman et al.,
2010; see also McGraw et al., 2007). Ultimately, the optimal time point for sampling gene expression will vary with the system in ways that will be difficult to
predict, and so the best strategy will be to sample multiple time points during
trait development.
7.6
A note on gene expression and sexually selected behavior
Behavioral transcriptomics is a particularly exciting area. In contrast to morphological phenotypes, where the relevant gene expression may occur at earlier
developmental stages before the mature phenotype is expressed, the gene expression that underlies sex-specific mating behaviors may be confined to the adult.
This expectation is well supported in many cases; examples include behavioral
change related to change in an individual’s relative position within social hierarchies (Burmeister et al., 2005; Renn et al., 2008; White et al., 2002), as well as
other social interactions (Carney, 2007; Ellis & Carney, 2010).
However, even if the relevant time point is easy to identify, the appropriate
sampling location in behavioral transcriptomic studies is often not so straightforward. In vertebrates, most behavioral phenotypes have very limited expression
geography within the brain. The gene expression patterns underlying behaviors
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are confined to specific regions, or even neurons, within the forebrain. An excellent example is the song region in the zebra finch brain (Vu et al., 1994), where
gene expression patterns in four confined regions within the brain correlate with
male song in the breeding season (Heimovics & Riters, 2005). This specificity
can be surprising because the vertebrate brain looks from the outside like a
remarkably homogenous lump of tissue with little sign of its important and varied
functions. Different regions within the forebrain look largely identical without
staining methods to distinguish them. Because of this homogenous appearance, it
is tempting to simply collect the entire brain, or even the entire forebrain as this is
the location of higher-level processes, in individuals expressing different behavioral phenotypes. However, this approach will ensure that key transcriptomic
differences that have limited distribution will be diluted out by the sheer mass
of the other regions of the brain. For example, differences in male and female
transcriptomes are among the lowest for any somatic tissue when whole-brain
homogenates have been studied in birds, with the majority due to dosage effects
of the sex chromosomes rather than functional expression differences (Mank
et al., 2007; Naurin et al., 2011). This is not because male and female behaviors
do not differ, but dimorphic expression is washed out by noise from surrounding non-dimorphic or variably dimorphic tissue. In such studies, it is essential
to identify and isolate the location within the brain that causes the behavioral
phenotype. This can be accomplished by in situ hybridization when there is a
candidate gene available (see Fitzpatrick et al., 2005), or other staining methods
that identify different regions of the brain.
In invertebrates, the problem of expression geography within the brain may
be less severe. Several studies have successfully detected expression differences
related to behavior from whole-brain samples; for example, among castes in
eusocial wasps (Toth et al., 2007) and in response to interactions with females in
male D. melanogaster (Ellis & Carney, 2010), and transcriptome profiles from
whole-brain honey bee samples can correctly predict bee behavior (Whitfield
et al., 2003). Of course, knowing the precise location of a behavioral phenotype
within the insect brain might allow a more nuanced and accurate assessment
of expression differences between behavioral types. In situ hybridization studies
have in fact successfully isolated the expression of sexual genes to specific neurons within the brain in D. melanogaster (Ryner et al., 1996). This approach will
be more difficult in non-model organisms, but the increasing number of organisms with fully sequenced genomes means that finding a close relative with a
reference genome will become easier.
7.7 The next step is to understand how gene expression
responds to environmental influences
The next level of analysis involves understanding how gene expression responds
to environmental variation to influence sexually selected phenotypes. Work in
this area has already begun; although it remains preliminary and much more
is needed. For example, food limitation has a clear effect on sex-biased gene
expression (Wyman et al., 2010), as we might expect given the importance of
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diet in condition and the importance of condition for sexually selected traits.
Similarly, infection status affects both gene expression and plumage coloration
in house finches (Balenger, 2011). These initial studies suggest that TEIs may be
an important consideration, and show the potential for environmental variation
to affect the phenotype via the encoding genes.
Studies that evaluate how gene expression responds to environmental factors in
multiple individuals will undoubtedly reveal a great deal of variation. However,
without knowing each individual’s genotype, it will be impossible to distinguish
random variation (e.g., from the developmental environment) from genetic variation in the reaction norms for gene expression. Ultimately, a full understanding of
TEIs will require differentiating these sources of variation. Doing so will require
describing reaction norms for gene expression from individuals of known genotype or genotype classes (Figures 7.1 and 7.2). This will typically involve an
experimental breeding design (ideally a paternal half-sibling approach; reviews
by Ingleby et al., 2010, Chenoweth & Blows, 2006) and will require large sample
sizes, based on the small GEI effect sizes observed in phenotypic studies (Ingleby
et al., 2010). This need for many samples creates some difficulty for the study of
gene expression, given the costs of RNA sequencing; yet the costs are becoming
increasingly reasonable, especially with the ability to tag and pool large numbers
of individuals.
7.8
A few notes on technology and experimental design
By the time this volume is printed, next-generation sequencing methods will
likely have been subsumed by third-generation sequencing methods that will
solve many of the hurdles of transcriptome studies in non-model organisms.
For example, when choosing among current RNA sequencing technologies, one
must trade off extreme read depth, which gives the power to detect even lowly
expressed genes, with extended read length, which offers the advantage of easy
gene and genome assembly. Technologies currently on the cusp of commercialization will provide both. This is both heartening, as it will expand the research
horizon even further, and discouraging, because it means that anything we write
here will be obsolete by the time this volume sees the light of day. Because this
section will not age well with new technological developments, we shall focus on
experimental design and the most important question of the day: whether or not
to assemble and annotate a genome.
We have noted here some important elements of experimental design. Identifying the phenotypic and developmental location of the sexual phenotype of
interest is crucial, because although the genome is the same in each cell in an individual’s body, the transcriptome varies over tissue space and developmental time.
A second consideration is the need for biological replication, despite the currently
prohibitive cost of extensive replication. This can be done by assessing multiple replicates independently, which brings the advantage of tracking individual
variation. Alternatively, pooling multiple biological replicates in a sample without tagging offers another method that lowers the number of samples required,
because pooled variance is far less than individual variance. However, although
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this approach is cheaper, it lacks the potential to detect variance within experimental treatments. It is often cost-effective to combine these two approaches by
pooling multiple independently-tagged individuals within a sequencing run. This
provides both estimates of individual variance and reduced experimental cost.
There are relatively few species for which genomes have been assembled and
annotated, and few of these sequences are complete as most represent partial
drafts. This presents a conundrum to researchers interested in transcriptomic
studies of unsequenced organisms. Should they sequence, assemble, and annotate
a full reference genome, or not? If there is a draft available for their organism of
interest, should they use it despite the fact that it is incomplete? Draft genomes
may lack or misassemble a large proportion of genes (Zhang et al., 2012), and
expression estimates from these missing or incorrect genes loci will be strongly
affected. For organisms lacking a draft genome entirely, building your own is not
a trivial concern, because although sequencing methods have improved and costs
have dropped, assembly and annotation remains arduous. High-quality reference
genomes require several months (at least) of skilled bioinformatics work.
Building or employing a draft genome pays dividends in several ways. First,
a complete and high-quality genome simplifies and vastly reduces the computational power required for mapping of RNASeq data. More importantly, a reference genome makes it far easier to differentiate paralogs, polymorphism, and
splice variants in RNASeq data, and this is crucial to downstream analyses of
gene evolution. Finally, a high quality genome that includes positional information allows for the inclusion of linkage information.
We suggest that this should be an all or nothing approach. Because the easiest approach to transcriptome studies is to map RNA-seq data to a reference
genome, it is true that genome assembly and annotation is useful. However, a
poorly assembled or annotated genome is not very useful at all, as many reads
will be unmappable when the underlying genome has been misassembled, gene
expression estimates will be biased when paralogs have not been accurately identified, and many genes will simply be missing from the annotation. This suggests
that the genome should be constructed to a high quality, or it should not be done
at all and other, hybrid approaches should be taken. Two hybrid approaches are
particularly useful.
A first alternative method involves the construction of normalized transcriptomes from long-read (such as 454) next-generation sequencing. Current
long-read technologies do not offer the necessary sequencing depth to allow
estimates of expression level in complex eukaryotic transcriptomes. Instead,
their utility in this approach lies in their relatively long sequence lengths. When
coupled with mRNA normalization protocols that increase the abundance of
rare transcripts and decrease the abundance of common transcripts, normalized
454 transcriptomes can be used for the mapping of short-read RNA-Seq data
(Kunstner et al., 2010; Santure et al., 2011). This is quite a useful method at the
moment, although it carries the caveat that normalization is somewhat finicky,
and in many cases, increasing rare transcripts in an mRNA library actually ends
up increasing the proportion of transcription errors at the cost of real genes.
Because transcription is messy, low levels of mistakes occur in any mRNA
library, including transcripts of introns, chimeras, and non-coding DNA. These
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are in such a low proportion that they clearly are non-functional; however, the
normalization procedure cannot differentiate rare transcripts from erroneous
transcripts, and this can lead to a high proportion of non-coding sequence in
normalized libraries (Kunstner et al., 2010; Santure et al., 2011).
A second approach when an assembled genome is not available is also the
most computationally intensive at the moment, but the most direct and the least
expensive. In this method, short-read next-gen sequence data (such as Illumina)
from non-normalized mRNA libraries is first assembled into a de novo transcriptome (Grabherr, 2011), and this is then used to map the same data for
expression estimates. Because the comparison of tens-of-millions of short reads is
required to construct the transcriptomes, this ideally requires access to a suitable
super-computer or a high-spec computer cluster (in other words, we don’t recommend trying this on a desktop computer in most cases). If these are available,
then this approach offers several advantages, namely that the same dataset is used
to construct the transcriptome and estimate expression level and the transcriptome contains all the genes expressed from the mRNA library. The drawback are
mainly assembly time, as deep sequencing can result in transcriptome assemblies
requiring several months, even with the use of a super-computer.
These choices will quickly collapse once the trade-off between next-gen read
length (such as the 454 method) and sequence depth (from technologies such as
Illumina and SOLiD) is eliminated. This is clearly on the horizon, with methods that offer reads of up to several kilobases coupled with great read depth.
Commercial or nearly-commercial examples include the Pacific Bio and Oxford
Nanopore technologies. These currently experience high error rates, but if that
can be reduced, there will be little reason to not simply assemble a complete
genome for every organism and use RNASeq data generated by the same technology for both genome annotation (e.g., the identification of expressed elements)
and gene expression estimates. This will eliminate many of the bioinformatic
headaches of the moment, and expand, yet again, the research horizon. Stay
tuned for further developments.
7.9
Conclusion
In this chapter, we have discussed the utility of studying the gene expression
underlying sexual traits, particularly to understand how transcription responds
to environmental variation and how TEIS for trait expression can be evaluated.
Gene expression studies, supported by new and emerging RNA sequencing technologies, represent a powerful approach for identifying TEIs. Although several
experimental challenges exist, these methods potentially open the door to new
and exciting discoveries in evolutionary ecology in general and sexual selection
research in particular.
In particular, we see the need for effort in two key areas:
• Theoretical predictions for the implications of TEIs in sexually selected traits,
and the evolution of condition dependence in these traits, that extend to the
gene expression level. This will help us know how to design and what to look
for in transcriptome datasets.
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• A deeper understanding of the transcriptome-phenotype relationship coupled
with the identification of the transcriptomic basis of more sexually selected
traits. This requires that we characterize the relationship between transcriptional and phenotypic variance, and that we identify the anatomical and
developmental location of sexually selected traits.
Acknowledgments
We thank A. Pomiankowski and D. Hosken for helpful comments on this
manuscript, and the invitation from the editors to write it. Support from the
European Research Council (grant 260233), the Biotechnology and Biological
Sciences Research Council, and the Natural Sciences and Engineering Research
Council (Canada) are gratefully acknowledged.
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