Covariation of Larval Gene Expression and Adult Body Size in Natural Populations of Drosophila melanogaster (original) (raw)

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Zoltán Bochdanovits, Herman van der Klis, Gerdien de Jong, Covariation of Larval Gene Expression and Adult Body Size in Natural Populations of Drosophila melanogaster, Molecular Biology and Evolution, Volume 20, Issue 11, November 2003, Pages 1760–1766, https://doi.org/10.1093/molbev/msg179
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Abstract

Understanding adaptive phenotypic variation is one of the most fundamental problems in evolutionary biology. Genes involved in adaptation are most likely those that affect traits most intimately connected to fitness: life-history traits. The genetics of quantitative trait variation (including life histories) is still poorly understood, but several studies suggest that (1) quantitative variation might be the result of variation in gene expression, rather than protein evolution, and (2) natural variation in gene expression underlies adaptation. The next step in studying the genetics of adaptive phenotypic variation is therefore an analysis of naturally occuring covariation of global gene expression and a life-history trait. Here, we report a microarray study addressing the covariation in larval gene expression and adult body weight, a life-history trait involved in adaptation. Natural populations of Drosophila melanogaster show adaptive geographic variation in adult body size, with larger animals at higher latitudes. Conditions during larval development also affect adult size with larger flies emerging at lower temperatures. We found statistically significant differences in normalized larval gene expression between geographic populations at one temperature (genetic variation) and within geographic populations between temperatures (developmental plasticity). Moreover, larval gene expression correlated highly with adult weight, explaining 81% of its natural variation. Of the genes that show a correlation of gene expression with adult weight, most are involved in cell growth or cell maintenance or are associated with growth pathways.

Introduction

The molecular basis of quantitative traits remains elusive (Tautz and Schmid 1998; Stern 2000). Quantitative traits are known to have complex genetic architecture, with many genes contributing to the phenotypic variation. Statistical population genetic studies have identified quantitative trait loci (QTLs) for a number of different traits, but limitations on the resolution of these techniques proved prohibitive for a detailed dissection of the molecular determinants (MacKay 2001). The outcome of phenotypic evolution depends on details of the genetic architecture; that is, the nature of the causative mutations and as a result the nature of the interactions between gene products. Recent studies show that noncoding variation might be important to evolutionary change. Natural variation in quantitative traits might involve differential gene expression, as underlined by sequence divergence in promoter regions (Purugganan 2000) and QTLs that map to noncoding sequences (MacKay 2001). The genes involved seem to possess important regulatory functions during development in both animals and plants (Oldham et al. 2000; Barrier, Robichaux, and Purugganan 2001). Moreover, variation in gene expression between natural populations has been proposed to underlie adaptation (Barrier, Robichaux, and Purugganan 2001; Oleksiak, Churchill, and Crawford 2002). However, a direct comparison of global gene expression and an adaptive quantitative phenotype has never been conducted. Such an analysis seems the logical next step in both quantitative genetics and genomics, merging the two fields. In this study, we attempt to quantify the amount of quantitative phenotypic variation explained (in a statistical sense) by variation in gene expression.

Analysis of global gene expression using microarrays has proved successful in comparing the mean transcriptional state of two or more distinct groups (White 2001). To obtain a more comprehensive picture of the biological variability in gene expression, recent studies have explored within-group and between-group variation of global transcription using analysis of variance (Jin et al. 2001; Oleksiak, Churchill, and Crawford 2002; Pletcher et al. 2002). However, a major challenge that remains is detecting a quantitative relationship between variation in gene expression and variation in an adaptive quantitative phenotype. If quantitative trait variation is caused by variation in gene expression (Purugganan 2000), rather than by protein evolution, a correlation between transcript levels and trait value could be expected.

Natural populations of Drosophila melanogaster occur over a wide geographic range and are known to be adapted to local conditions (Noach, De Jong, and Scharloo 1996; Gilchrist and Partridge 1999), most notably temperature. Many traits diverge between populations adapted to different climates, such as enzyme activity (Eanes 1999), development time (Robinson and Partridge 2001), critical larval weight for pupation (De Moed 2000) and adult body size (Robinson and Partridge 2001). Adult weight is the most extensively studied life-history trait in Drosophila, is known to be determined during larval development (Chakir et al. 2002), and is known to be affected by developmental temperature (Noach, De Jong, and Scharloo 1996). If adaptive natural variation in adult body size is attributable to changes in gene expression, a significant covariation between larval gene expression and adult weight is expected.

We examined global gene expression using Affymetrix GeneChips on staged third instar larvae of a tropical and a temperate population of D. melanogaster reared at 17.5°C and 27.5°C. From each population, we used five isofemale lines known to differ significantly in adult body size. Across the rearing temperatures, the “broad sense” heritability for adult weight was 0.24 for the temperate population and 0.39 for the tropical population. The experimental design equated to a two-way analysis of variance (ANOVA) with 20 measurements for global gene expression across the four population-temperature combinations and for adult weight. The aim was to detect four (possibly overlapping) classes of genes. Two-way ANOVA allows identification of genes whose expression differs between populations, differs between temperatures, or exhibits population by temperature interaction (PxT). In addition, we applied a nonparametric correlation test on gene expression and adult weight. This analysis identifies a quantitative association between larval gene expression and adult weight, determined as isofemale line means.

Materials and Methods

Fly Stocks

Five isofemale lines from Wenatchee (47.26°N, 120.20°W; Washington State, USA) and five isofemale lines from San José (9.59°N, 84.04°W; Costa Rica), collected in the summer of 2001, were kept on standard corn medium at 17.5°C until the start of the experiment. Isofemale lines descended from a single inseminated female and represent genetic variation within the population. Before the experiment, the lines were reared at the experimental temperature for one generation to rule out possible effects of maternal rearing temperature. Larvae were raised at low density under unlimited food conditions. The experiment was conducted at 17.5°C and 27.5°C on standard corn medium stained with 0.05% bromophenol blue. This medium has no effect on larval growth and allows for accurate staging of third instar larvae just before pupation at their maximum size (Andres and Thummel 1994). Adult males and females that emerged from the vials the larvae were previously collected from were weighed in groups of five to the nearest 0.01 mg on a Sartorius microbalance. Adult weight was averaged over the sexes for subsequent analysis.

Gene Expression Analysis

Larvae with dark-blue guts that stopped feeding and started to wander were manually collected from the surface of the medium with a paintbrush and were immediately frozen in liquid nitrogen. Approximately 20 larvae from each isofemale line were used for isolating mRNA with Qiagen Direct mRNA kit. From approximately 3 mg of mRNA, Bio-11-CTP–labeled and Bio-11-UTP–labeled aRNA were prepared using standard Affymetrix protocols. The labeled aRNA were applied to 20 Affymetrix Drosophila Genome Arrays. Hybridization and scanning was performed on Affymetrix Fluidics Station 400 and GeneArray® Scanner at the Leiden Genome Technology Center. The raw data were subjected to global normalization per GeneChip on Microarray Suite 5.0 before further analysis. The data acquired from these procedures are relative measures of gene expression independent from the original larval weights.

Data Analysis

We detected the expression of 7,652 of more than 13,500 genes (56%) represented on the Drosophila Genome Array on at least one GeneChip. There was a systematic difference in the percentage of genes detected between samples reared at 17.5°C and 27.5°C. In the samples reared at 17.5°C of the same isofemale lines, a smaller percentage of all genes was detected. This may be expected, as in ectotherms the “rate of living” is negatively correlated with ambient temperature. For two samples reared at 27.5°C, we found the opposite pattern due to very low percentage (∼ 9%) of genes detected. These two arrays were excluded from further analysis. For the remaining arrays, at 17.5°C, between 13% and 30%, and at 27.5°C, between 16% and 41% of the genome has been detected. To allow for meaningful statistical analysis of the data, we selected only probes that have been detected on at least four microarrays in each of the four population-temperature combinations. This filtering resulted in 1,134 probes for further analysis. SAM (Statistical Analysis of Microarrays) (Tusher, Tibshirani, and Chu 2001) was performed on an Excel plug-in. In all SAM analyses, a false positive rate of 1 was chosen, meaning that in the resulting set of significant genes, no more than one false positive is expected. The two-way ANOVA, the Spearman nonparametric correlation test, and the principal component analysis (PCA) were performed in SPSS 10.0. PCA summarizes correlated data in a small number of uncorrelated derived variables with little loss of information and allows for a graphical representation of a large data set. Hierarchical Cluster analysis of gene expression was performed in Cluster. Because only genes already found to be significant in SAM have been submitted to the ANOVA and due to the high level of correlation between the expression of the different genes, presumably as a result of coregulation, no Bonferroni correction was applied. Although the same rationale applies to the nonparametric correlation test as well, due to the large number of genes found to be significant at the 0.05 level, a Bonferroni correction was applied in this analysis. Due to this overtly conservative significance threshold on a reasonable sample size (18 isofemale line means), it is unlikely that the results would be seriously biased by experimental variation between GeneChips. The measure of “broad sense” heritability in body size was calculated using an ANOVA approach. An estimate of the isofemale between-line variance is a measure of “broad sense” genetic variance. The sum of the genetic variance and the within-line variance (the error variance) is an estimate of the phenotypic variance. The ratio of these two quantities gives a measure of heritability.

Results

Detection of Statistically Significant Differences in Gene Expression from the Two-Way ANOVA and Correlation Analyses

Normalized measures of relative gene expression were acquired for each isofemale line at both temperatures. After filtering of the data, 1,134 genes were allowed for analysis. To avoid common problems associated with the analysis of such a large number of variables in classical statistical procedures (Benjamini and Hochberg 1995), we first applied a permutation test to the data. Two types of permutation tests as implemented in SAM were used. The first SAM test allowed for comparison of two group means and therefore could not detect interactions between the main factors, population and temperature. This analysis was used as a preliminary test only, for allowing the data to the ANOVA. Four comparisons were conducted: comparison of the temperatures within the two populations and comparison of the populations within the two temperatures. The second SAM test was a quantitative test performed with isofemale line mean weight as covariate, an equivalent of a correlation test. These preliminary analyses resulted in a list of 275 probes significant in at least one test. These data were subjected to standard two-way ANOVA and correlation tests.

From the ANOVA, we identified 45 genes differing in gene expression between populations, 200 genes differing in gene expression between the temperatures (of which 41 in both populations), and 31 genes with different sensitivity for ambient temperature between the populations (i.e., exhibiting PxT interaction) (fig. 1). Seven genes exhibited both a population effect and a PxT interaction. Expression of 19 genes highly correlated with adult body weight (fig 2). Twelve out of the 19 “weight” genes also differed in expression between the geographical populations (see tables 1 and 2 in Supplementary Material online). To underline the validity of the statistical procedure used, note that from the 19 genes with a significant correlation—after Bonferroni correction—between their larval expression and adult body weight, 18 have already been detected by the SAM quantitative analysis (data not shown). Such an overlap between the results from the different analyses clearly demonstrated the robustness of the procedure.

Factor Analysis of High Dimensional Gene Expression Data

Because expression of genes was highly statistically correlated, presumably due to coregulation, we performed several principal component analyses to reduce the number of independent variables. The first two principal components (PCs) of gene expression from the 45 “population” genes separated the populations and the rearing temperatures (fig. 3). PC1 demarcated the tropical population reared at 27.5°C versus the temperate population reared at 17.5°C (compare cr27.5 and w17.5 in figure 1_a_). One composite measure of gene expression was sufficient to differentiate between natural populations reared under native conditions and adapted to different geographical regions. PC2 distinguished the populations reared under nonnative conditions (compare cr17.5 and w27.5 in figure 1_a_). The first two PCs of gene expression from the 31 “interaction” genes were also sufficient to differentiate between the populations and temperatures (fig. 4). PC1 separated the rearing temperatures within the tropical strain (cr17.5 and cr27.5 in figure 1_c_), and PC2 separated the rearing temperatures within the temperate population (w17.5 and w27.5 in figure 1_c_). The expression of a small set of genes was sufficient to describe genetic differentiation between natural populations and developmental plasticity. Although these statistics do not necessarily imply causality, gene expression in these genes might well be involved in adaptation to geographical and developmental temperatures.

PC1 summarizing gene expression of the 19 “weight” genes (fig. 2) was subjected to a correlation test with adult weight. Mean adult weight of isofemale lines correlated up to 90% with the derived measure of gene expression (fig. 5). Note that isofemale lines from the tropical population at 27.5°C did not vary much in adult weight or gene expression; neither did isofemale lines from the temperate population at 17.5°C. Native conditions were not very conducive to the expression of genetic variation. However, considerable variation in both weight and gene expression was detected under nonnative conditions. It has been reported before that populations express more genetic variation when tested under stressful or nonnative conditions (Noach, De Jong, and Scharloo 1996; Hoffmann and Merila 1999). Our results are in line with these observations. In summary, difference in gene expression and adult weight was highly significant and strongly correlated both within the populations over temperatures (plasticity) and between the populations at both temperatures (genetic variation). Finding that significant correlation between larval gene expression and adult size was present not only across populations but also within populations showed that detecting “weight” genes was not an artifact of population differences, even considering the large overlap between the “population” genes and “weight” genes. In fact, PC1 derived from the “population” genes correlated with body size equally well (fig. 6), suggesting that geographical adaptation and variation in body size were very closely related.

Discussion

The search for the molecular determinants of adaptive variation in quantitative traits is just beginning, but it is already clear that this effort will merge developmental genetics with quantitative genetics (White 2001; Gibson 2002). A major challenge remains in deducing causal mechanisms from statistical associations. We described a pattern of coregulated gene expression sufficient to distinguish geographical populations and explained, in a statistical sense, variation in adult body size from variation in larval gene expression. One way to assess the genes' possible role in adaptation or in determining body size is to elaborate on their known cellular and biological functions. If variation in body size were a causal component of geographical adaptation, genes differently expressed between the populations might have functions such as cell growth, proliferation and morphogenesis relevant for establishing adult body size. The considerable overlap between the “population” and “weight” genes already suggested that this might be the case.

A total of 69 genes were detected for differing expression between the populations or for exhibiting PxT interaction. Of the 69 genes, 39 are of unknown function (http://flybase.bio.indiana.edu), and the remaining 30 could be assigned to a cellular function using the Gene Ontology database (http://www.ebi.ac.uk/ego). Of the remaining 30 genes, 16 (53%) have functions in “cell growth and maintenance,” including cell proliferation. In addition, Ras85D, a known oncogene involved in morphogenesis, was detected (Oldham et al. 2000). Four genes seem to be involved in energy metabolism. Of four other genes involved in cell communication, two are known stress response genes (Khush and Lemaitre 2000): Lectin-GalC1 was highest expressed in the temperate population reared at 27.5°C, but Drosomycine levels were highest in cold-reared tropical larvae. Both cases represented nonnative conditions for the geographical populations. Being reared under conditions not resembling the environment the genotypes are presumably adapted to might be experienced as stressful.

Nineteen genes were highly correlated with adult weight; 12 of these could be assigned to a cellular function. Eleven (92%) were involved in “cell growth and maintenance.” Several of these genes could be directly linked to morphogenesis. Scab is known to have major-effect mutants, altering cell movement and disrupting morphogenesis (Stark et al. 1997). Sec61β is involved in the protein translocation of Gurken and interacts with Ras85D (Valcarcel et al. 1999; Ford 2002). Talin is an integrin-binding molecule and contains an insulin receptor substrate domain. Integrins are known mediators of morphogenesis, and the insulin receptor pathway is known to regulate body size (Brown, Gregory, and Martin-Bermudo 2000; Oldham et al. 2000; Brogiolo et al. 2001). The gene eIF-4G codes for a eukaryotic initiation factor involved in protein biosynthesis and was suggested to regulate growth (Galloni and Edgar 1999). The gene eff is a dominant modifier of the polyhomeotic extra sex combs phenotype (Fauvarque et al. 2001). CG 10992 is a cathepsin B molecule. Cathepsin B is known to be involved in the activation of latent TGF-β in a parasite living in host macrophages (Somanna, Mundodi, and Gedamu 2002). Where molecular function could be assigned, it seemed that many genes detected in this study were modifiers of “larger” effect genes involved in fundamental stages of morphogenesis and in pathways affecting growth (Oldham et al. 2000). To confirm this intuitively appealing result, an additional analysis was performed. We seemed to detect an overrepresentation of genes involved in cellular growth. To address this issue a chi-square association test was performed; we tested whether falling into the category “cell growth and/or maintenance” increased the probability of being called significant in our analyses. Approximately 13,500 genes are represented on the Affymetrix GeneChip; 2,575 Drosophila genes are involved in cellular growth (according to GO). As 24 out of 76 genes called significant in our analyses belonged to that category, it could be shown that genes in cell growth are significantly (P = 0.005) overrepresented in our sets of candidate genes. That evolution of body size should involve alterations to basic cellular processes is not surprising. Studies on geographical variation have shown that body size variation can be the result of differences in cell numbers and/or cell size (De Moed, De Jong, and Scharloo 1997; Zwaan et al. 2000). Either mechanism must involve alterations to fundamental processes in cellular growth.

A large fraction of the genes detected in this study are potentially causative to the observed genetic variation in body size because they fall in a plausible functional category. However, an important question remains in deducing whether the expression profile of these 19 genes have changed independently from each other and thus contribute equally to the body size variation. Alternatively, a smaller number of causative changes may coordinately regulate the expression of these genes. Figure 2 shows the results of a cluster analysis on the expression data. Apart from the obvious dichotomy between gene expressions positively versus negatively correlating with body size, there are at least two additional subsets of genes showing very similar expression profiles. Although speculative, this result suggests that as few as three causative changes may coordinately regulate the expression of a small number of genes to induce large and presumably adaptive variation in a life history trait.

The pattern of gene expression at different environmental temperatures differentiated the geographical populations, pointing to gene regulation as a mechanism for adaptation (Crawford and Powers 1992; Barrier, Robichaux, and Purugganan 2001; Oleksiak, Churchill, and Crawford 2002). We found that expression of a small set of genes, already known to be involved in morphogenesis and growth control (Oldham et al. 2000), statistically explained up to 81% (R2 = square of correlation coefficient) of the variation in adult body size. Both the variation between natural populations and the developmental plasticity was explained. Differential larval gene expression was highly associated with geographical adaptation exemplified by the populations and with evolved developmental adaptation to temperature.

Supplementary Material

Two tables of genes found significant are available online.

Wolfgang Stephan, Associate Editor

Fig. 1.

Patterns of transcript abundance of genes found to differ significantly in a two-way ANOVA. The intensity of the red color represents the relative mRNA levels; only for the illustrations the values were standardized to range between 0 and 100, within each gene. The data represents isofemale lines from the tropical (cr) and temperate (w) populations, reared at 17.5°C and 27.5°C. The dendograms on the left group genes with similar expression between natural populations and rearing temperatures. (a) Forty-five genes with different expressions between the natural populations. (b) Forty-one genes with different expression between the rearing temperatures. (c) Thirty-one genes with expression showing population by temperature interaction

Fig. 2.

Patterns of transcript abundance of the 19 genes with expression that shows a significant correlation with adult body size. The intensity of the red color represents the relative mRNA levels; only for the illustrations the values were standardized to range between 0 and 100, within each gene. The data represents isofemale lines from the tropical (cr) and temperate (w) populations, reared at 17.5°C and 27.5°C. Arrays are order for ascending mean isofemale line weight. The dendogram on the left groups genes with similar expression across isofemale lines of increasing mean body weight. Expression of three genes shows a negative correlation with body size; the expression of 16 other genes shows a positive correlation

Fig. 3.

Scatter plot of the first and second principal component from gene expression, based upon genes that show a significant difference between populations in a two-way ANOVA. The data represents the tropical population raised at 17.5°C (▪) and 27.5°C (□) and the temperate population raised at 17.5°C (⧫) and 27.5°C (⋄). PC1 (46.10% of variance in gene expression) separates the geographical populations at their native developmental temperatures; that is ⧫ versus □. PC2 (18.85% of variance in gene expression) separates the geographical populations under nonnative conditions; that is ▪ versus ⋄. Note that the 45° and 315° diagonals separate the geographical populations and developmental temperatures respectively

Fig. 4.

Scatter plot of the first and second principal component from gene expression, based upon genes that show a significant PxT interaction in a two-way ANOVA. The data represents the tropical population raised at 17.5°C (▪) and 27.5°C (□) and the temperate population raised at 17.5°C (⧫) and 27.5°C (⋄). PC1 (46.36% of variance in gene expression) separates the developmental temperatures within the tropical population. PC2 (26.46% of variance in gene expression) separates the developmental temperatures within the temperate population. Note that the 45° and 315° diagonals separate the geographical populations and developmental temperatures crosswise, opposed to the pattern found in the principal components from gene expression of “population effect” genes

Fig. 5.

Scatter plot of the first principal component from gene expression, based upon genes that show a significant correlation with adult weight and mean adult weight of isofemale lines. The data represents the tropical population raised at 17.5°C (▪) and 27.5°C (□) and the temperate population raised at 17.5°C (⧫) and 27.5°C (⋄). The plot depicts the correlation (rs = 0.902) between the composite measure of expression of 19 genes (74.11% of variance in gene expression) and adult weight. The correlation is present within the populations (rectangle, rs = 0.857 versus rhombus, rs = 0.867) and within the developmental temperatures (closed symbols, rs = 0.767 versus open symbols, rs = 0.81)

Fig. 6.

Scatter plot of the first principal component from gene expression, based upon genes that show a significant difference between populations in a two-way ANOVA and mean adult weight of isofemale lines. The data represents the tropical population raised at 17.5°C (▪) and 27.5°C (□) and the temperate population raised at 17.5°C (♦) and 27.5°C (⋄). The plot depicts the correlation (rs = 0.907) between the composite measure of expression of 45 genes and adult weight. The correlation is present within the populations (rectangle, rs = 0.734 versus rhombus, rs = 0.931) and within the developmental temperatures (closed symbols, rs = 0.853 versus open symbols, rs = 0.867)

We thank J. M. Boer and E. M. Mank at the Leiden Genome Technology Center at the Center for Human and Clinical Genetics, Leiden University Medical Center for performing the hybridization and scanning of the microarrays and facilitating the preliminary data analysis. We also thank H. J. Th. Goos for providing the San José isofemale lines and R. B. Huey for providing the Wenatchee isofemale lines. We thank Cees Loffeld for technical assistance and two anonymous reviewers for valuable comments on the manuscript.

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