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Trim Size: 170mm x 244mm Hunt c07.tex V2 - 06/06/2014 7 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 9:00 A.M. Page 169 Trim Size: 170mm x 244mm 170 Hunt c07.tex V2 - 06/06/2014 9:00 A.M. Genotype-by-Environment Interactions and Sexual Selection 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; Page 170 Trim Size: 170mm x 244mm Hunt c07.tex V2 - 06/06/2014 Transcriptome × Environment Interactions 171 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. 9:00 A.M. Page 171 Trim Size: 170mm x 244mm 172 Hunt c07.tex V2 - 06/06/2014 9:00 A.M. Genotype-by-Environment Interactions and Sexual Selection 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). Page 172 Trim Size: 170mm x 244mm Transcriptome × Environment Interactions Hunt c07.tex V2 - 06/06/2014 173 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 9:00 A.M. Page 173 Trim Size: 170mm x 244mm 174 Hunt c07.tex V2 - 06/06/2014 9:00 A.M. Genotype-by-Environment Interactions and Sexual Selection 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 Page 174 Trim Size: 170mm x 244mm Transcriptome × Environment Interactions Hunt c07.tex V2 - 06/06/2014 175 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 9:00 A.M. Page 175 Trim Size: 170mm x 244mm 176 Hunt c07.tex V2 - 06/06/2014 9:00 A.M. Genotype-by-Environment Interactions and Sexual Selection 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). Page 176 Trim Size: 170mm x 244mm Transcriptome × Environment Interactions Hunt c07.tex V2 - 06/06/2014 177 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 9:00 A.M. Page 177 Trim Size: 170mm x 244mm 178 Hunt c07.tex V2 - 06/06/2014 9:00 A.M. Genotype-by-Environment Interactions and Sexual Selection 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 Page 178 Trim Size: 170mm x 244mm Hunt c07.tex V2 - 06/06/2014 Transcriptome × Environment Interactions 179 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 9:00 A.M. Page 179 Trim Size: 170mm x 244mm 180 Hunt c07.tex V2 - 06/06/2014 9:00 A.M. Genotype-by-Environment Interactions and Sexual Selection 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 Page 180 Trim Size: 170mm x 244mm Transcriptome × Environment Interactions Hunt c07.tex V2 - 06/06/2014 181 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 9:00 A.M. Page 181 Trim Size: 170mm x 244mm 182 Hunt c07.tex V2 - 06/06/2014 9:00 A.M. Genotype-by-Environment Interactions and Sexual Selection 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. Page 182 Trim Size: 170mm x 244mm Transcriptome × Environment Interactions Hunt c07.tex V2 - 06/06/2014 183 • 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. 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