Figure 3. Fed rovers and sitters store energy differently. The proportion of total calories due to lipids (horizontal axis) and carbohydrates are shown in whole-body measurements of fed 5-day- old males and females for the two sitter (red) and one rover (blue) strains. Data are standardized for fly dry weight (Methods). Rovers store significantly more energy as lipids and significantly less as carbohy- drates. For males and for females, mutant and natural sitters didn’t differ (Welch’s t-test, p..17 all tests) and hence were pooled for a rovers versus sitters comparison. Lipids: males, t = 3.26, df = 3.41, p = 0.039; females t = 5.08, df = 10.2, p = 0.0004. Carbohydrates: males, t = 23.98, df = 9.20, p = 0.003; females t = 25.64, df = 7.84, p = 0.0005. Data for n = 5 except n = 4 for male lipids. Error bars represent 61 s.e.m. doi:10.1371/journal.pgen.1000609.g003 an allocation shift between storage of energy as lipids and as carbohydrates. GEI in gene expression depends on metabolic role If for affects both behavioural and metabolic GEI and plasticity in a food-dependent manner, how is this reflected at the level of gene expression? To examine for’s effect on transcript levels we performed whole-genome microarray analysis on heads of rovers and sitters and sitter mutants under Fed and FD conditions (Methods). Array results were verified using qRTPCR on two genes with strong rover-sitter differences and involved in carbohydrate metabolism [Treh, trehalase, and CG10924, human homolog is PCK1 phosphoenolpyruvate carboxykinase 1 (soluble)] (Figure S3). Overall, the expression of genes involved in the breakdown of food to provide energy (catabolism) was significantly altered (had strong GEI), with rovers decreasing and sitters increasing their expression when food is present (I,0). For instance, glycogen phosphorylase (GlyP), which regulates glycogen breakdown, is highly significant (Figure 4A, q(for6food) = 0.000057). The differ- ing genetic background between fors and fors2 has a main effect on GlyP (q(BG) = 1.64?1027), but the response to food of each sitter genotype is similar (q(BG6food) = 0.33), so it is meaningful to speak of a GEI common to both types of sitters when compared to rovers. Conversely, expression of many genes in pathways for synthesis of proteins (anabolism) significantly increased in rovers in the Fed condition, exemplified by eukaryotic translation initiation foraging and Plasticity factor 4A (eIF-4A; Figure 4B, q(for6food) = 0.0011). I is positive for eIF-4A and negative for GlyP. We investigated whether functionally related genes show common modes of regulation as defined by the sign of I (see Text S1, Supplementary Methods, Group Level ANOVA), and found effects consistent with the catabolism-anabolism pattern described above. Catabolic groups such as carbohydrate (Figure 4C), glycogen, chitin, and amino acid breakdown genes, and mitochondrial oxidative phosphorylation complexes I–V had significant negative I values (Table S3). Anabolic groups associated with protein biosynthesis (Figure 4D), such as gene splicing, translation initiation, ribosomal proteins, and post-translational protein modification complex had significant positive I values (Figure 4D, Table S3). In Table S3, we list Gene Ontology groups with significant GEI, ordered from most positive to most negative I [groups with greater GO term specificity as per DAVID [16] GO levels 3–5 were used]. The most inclusive GO groups relating to catabolism/anabolism are group 9056 ‘‘catabolic process’’ which has significant negative for6food interaction I (p = 0.009, n = 140 genes) while the ‘‘biosynthetic process’’ group 9058 has positive I (p = 0.008, n = 300 genes; as these two groups were specifically singled out for testing we report p rather than FDR-adjusted q). Some gene groups with significant for GEI go beyond a simple anabolism - catabolism dichotomy. In particular, functional groups involved in neural and muscle function – neurotransmitter secretion groups, synaptic transmission, postsynaptic membrane, ion channels, GABA and calcium-binding EGF domains – all have negative I values (Table S3). Importance of genetic background in gene expression How does the genetic background (BG) difference between fors and the other two strains (forR and fors2) affect GEI of functional groups? Most groups in Table S3 have highly significant FDR- corrected q values for the main effect of background. However, only a few groups have significant BG6food interactions, and these are all groups with positive for6food I values, associated with transcription, splicing, translation, or post-translational modifica- tion of proteins. Thus, at the functional group level, gene groups with significant positive for GEI I often have significant interactions with the genetic background but those with negative I values do not. We used two methods to quantify the contributions of for, food, and BG to gene expression over all genes above cut off, not just those with interactions. First, we performed a principal components analysis (PCA) on log2 gene expression levels. Five PCA components were identified. The first component (explaining the largest amount of variance, see below) correlates strongly with main effect of BG, followed by components correlating with main effects of food and then for, with components 4 and 5 (explaining similar amounts of variance) correlating with the interactions of BG6food and for6food. Thus, genetic effects rank in the order BG.for.BG6 food.for6food when measured by variance explained. Our second method uses the Storey-Tibshirani false discovery rate (FDR) analysis to estimate the proportion of genes with significant effects [17]. This method uses a mixture-model approach to estimate the proportion p0 of genes matching the hypothesis of no effect. Then palt = 12p0 estimates the proportion of genes matching the alternative hypothesis of an effect. One interpretation of palt is that is the proportion of genes that would show significant effects, after FDR, if we had large numbers of replicates. p0 and hence palt depend not only on the true rate of differential expression of genes (DEG) but also on the signal to noise ratio of the array technology. Thus, for low expression genes with poorer signal to noise ratio p0 will be higher even if the true proportion of DEG is unchanged. We calculated PLoS Genetics | www.plosgenetics.org 3 August 2009 | Volume 5 | Issue 8 | e1000609