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Journal of Insect Physiology 55 (2009) 1050–1057 Contents lists available at ScienceDirect Journal of Insect Physiology journal homepage: www.elsevier.com/locate/jinsphys Combined expression patterns of QTL-linked candidate genes best predict thermotolerance in Drosophila melanogaster Fabian M. Norry d,1,*, Peter F. Larsen c,1, Yongjie Liu b,1, Volker Loeschcke a a Biological Sciences, Ecology and Genetics, University of Aarhus, Ny Munkegade, Bldg. 1540, DK-8000 Aarhus C, Denmark College of Plant Protection, Shandong Agricultural University, Daizong Street 61, Tai’an, Shandong 271018, China c Danish Agricultural Advisory Service, National Centre for Fur Animals, Udkaersvej 15, DK-8200 Aarhus N, Denmark d Departamento de Ecologı´a, Genética y Evolución, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, (C-1428-EHA) Buenos Aires, Argentina b A R T I C L E I N F O A B S T R A C T Article history: Received 8 June 2009 Received in revised form 22 July 2009 Accepted 23 July 2009 Knockdown resistance to high temperature (KRHT) is a thermal adaptation trait in Drosophila melanogaster. Here we used quantitative real-time PCR (qRT-PCR) to test for possible associations between KRHT and the expression of candidate genes within quantitative trait loci (QTL) in eight recombinant inbred lines (RIL). hsp60 and hsc70-3 map within an X-linked QTL, while CG10383, catsup, ddc, trap1, and cyp6a13 are linked in a KRHT-QTL on chromosome 2. hsc70-3 expression increased by heat-hardening. Principal Components analysis revealed that catsup, ddc and trap1 were either coexpressed or combined in their expression levels. This composite expression variable (e-PC1) was positively associated to KRHT in non-hardened RIL. In heat-hardened flies, hsp60 was negatively related to hsc70-3 on e-PC2, with effects on KRHT. These results are consistent with the notion that QTL can be shaped by expression variation in combined candidate loci. We found composite variables of gene expression (e-PCs) that best correlated to KRHT. Network effects with other untested linked loci are apparent because, in spite of their associations with KRHT phenotypes, e-PCs were sometimes uncorrelated with their QTL genotype. ß 2009 Elsevier Ltd. All rights reserved. Keywords: Gene expression Principal Components Heat knockdown resistance Quantitative polymerase chain reaction Quantitative trait loci Recombinant inbred lines 1. Introduction Phenotypes relevant for thermal adaptation in insects, such as knockdown resistance to high temperature (KRHT; Huey et al., 1992), may be influenced by variation in gene expression. Gene expression levels are expected to correlate with thermal-resistance phenotypes, particularly for candidate loci within genome regions that are well-known to affect thermotolerance. Quantitative trait loci (QTL) are functionally variable regions of the genome, detected by their substantial contribution to the phenotypic variation in the trait of interest. However, QTL typically represent large chromosomal segments carrying one or multiple unidentified genes that influence the genetic variation in the trait. The total number of functional polymorphisms within QTL regions, including any possible quantitative trait nucleotides or QTNs, is potentially enormous and difficult to fully scan in physiologically complex traits (Nuzhdin et al., 2007). In Drosophila melanogaster, global microarray scans in stress-selected populations were used for identifying genes affecting the stress-selected traits (e.g., * Corresponding author. Tel.: +54 11 45763300; fax: +54 11 45763354. E-mail address: [email protected] (F.M. Norry). 1 These authors contributed equally. 0022-1910/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.jinsphys.2009.07.009 Harbison et al., 2005; Nuzhdin et al., 2007; Sørensen et al., 2007; Telonis-Scott et al., 2009). Another complementary aim in thermal-stress studies is to dissect candidate genes (Rako et al., 2007; Folk et al., 2006; discussed in Hoffmann and Willi, 2008). When candidate genes are known within QTL regions, suites of such putative loci can be dissected for possible links between transcript abundance, QTL-effects and phenotypic variation in complex traits such as KRHT. If the goal is rather limited to dissect some candidate genes within QTL regions, we can otherwise use more routine and sensitive expression technologies, such as quantitative real-time polymerase chain reaction or ‘‘qRT-PCR’’ (Brown et al., 2005). For instance, screening candidate genes by qRT-PCR will determine whether or not they exhibit differential expression in heat-resistant versus heat-susceptible individuals (Bettencourt et al., 2008). Such differentially expressed genes can then be examined for further associations between transcript abundance and thermotolerance phenotypes. Recent applications of QTL-mapping for themotolerance traits have demonstrated that not only chromosome 3, where many heat-shock genes are concentrated, but also chromosomes X and 2 contain major QTL for thermal adaptation in D. melanogaster. In this model insect, an additive QTL for thermal resistance was consistently found in the middle of chromosome 2 in three different mapping populations (Norry et al., 2004, 2007a, 2008; F.M. Norry et al. / Journal of Insect Physiology 55 (2009) 1050–1057 Morgan and Mackay 2006). In addition, another major QTL for KRHT was X-linked, but it was significant only in populations selected for KRHT (Norry et al., 2007b). Here we use qRT-PCR to test for seven candidate genes in eight recombinant inbred lines (RIL) in D. melanogaster. Two tested genes (hsp60 and hsc70-3) map within an X-linked QTL of KRHT, as previously found (Norry et al., 2007b). Five autosomal genes (CG10383, catsup, ddc, trap1, and cyp6a13) were also tested as previously identified, putative candidate genes that are located within a KRHT-QTL in the middle of chromosome 2 (Norry et al., 2004, 2007a; Morgan and Mackay, 2006). Three of these loci (hsp60, hsc70-3, trap1) are heat-shock protein-related genes that were recently implicated as candidate QTL-genes for thermotolerance (Morgan and Mackay, 2006; Norry et al., 2007b). Other above mentioned genes (catsup and ddc) are involved in the catecholamine pathway and the response to extreme temperatures (Wright, 1987; Baden et al., 1996; Sabban and Kvetnansky, 2001). For instance, catecholamines up (catsup) is associated with naturally occurring variation in multiple traits such as locomotor behavior and longevity (Carbone et al., 2006), Additionally, we also tested two genes that are either up-regulated (CG10383) and down-regulated (cyp6a13) early in the heat-shock response in D. melanogaster (Sørensen et al., 2005; see Curtis et al., 2007, for pleiotropic effects of cyp6a13). RIL in this study are isogenic for all remaining major KRHT-QTL that are not included in the present analysis. Further, RIL were chosen not only on the basis of QTL genotypes but also on the basis of KRHT phenotypes. We show that gene expression levels of both hsc70 and trap1 were partially associated to KRHT across RIL. Expressed levels of both hsc70-3 and CG10383 increased by heat-hardening. Principal component (PC) analysis of expression (Coffman et al., 2005) revealed that catsup, ddc and trap1 were combined on expression-PC1 and this factor was positively associated to KRHT in non-hardened RIL. In heathardened RIL, hsp60 was negatively related to hsc70-3 on e-PC2, and this composite expression variable significantly correlated to KRHT. 2. Materials and methods 2.1. Recombinant inbred lines Eight RIL with different segments of each QTL allele were chosen for this study. These lines are isogenic for all remaining major KRHT-QTL that are not included in this study. However, RIL were chosen not only on the basis of QTL genotypes but also on the basis of KRHT variation. RIL were both analyzed for gene expression (see below) and phenotyped for knockdown resistance to high temperature (KRHT) in both non-hardened and heathardened females. All eight lines are a subset of intercontinental RIL described elsewhere (Norry et al., 2008). Briefly, two nearly homozygous and highly divergent lines were used as parental stocks to construct RIL. These parental lines, denoted SH2 and D48 (Norry et al., 2008), were derived lines originating from Australia and Denmark, respectively, selected for KRHT and subsequently inbred. SH2 and D48 are dramatically divergent for KRHT (Norry et al., 2004). F1-females (progeny of D48 SH2) were backcrossed to D48 males, and the backcross progeny were randomly mated for other two generations. After the last generation of random mating, individual pairs were set up, and their progeny were inbred by fullsib mating for 15 generations to form our ‘‘RIL-D48’’ stocks used in this study. Microsatellite loci were used as markers (Norry et al., 2008). Tested RIL are D48-4, D48-31, D48-35, D48-39, D48-57, D4872, D48-78 and D48-110 (all of them, excepting a new D48-72 line, were analyzed in Norry et al., 2008). Twenty newly emerged larvae were transferred into a vial with 30 ml of culture medium, 1051 with 10 vials per RIL at 25 8C. Emerged adults were collected every 12 h, sexed and maintained for 4–5 days at 25 8C, with 12 females per vial. Half of the whole flies of each line were snap frozen on liquid nitrogen and stored at 80 8C. Another half of the flies were kept at 35 8C for 45 min (heat stress in water bath), transferred to 25 8C for 1 h (recovery), frozen on liquid nitrogen and stored at 80 8C. 2.2. Thermotolerance phenotype Knockdown resistance to high temperature was measured by using a knockdown tube as described in Norry et al. (2008). Briefly, experimental individuals were reared at 25 8C, with 80 1–2 h-old larvae per culture bottle (40 ml Carolina culture medium per bottle; Biological Supply, Burlington, NC, USA), under a 12 h light/ 12 h dark cycle. KRHT was measured as knockdown time by using a knockdown tube connected to a water jacket attached to a single circulating bath. No anesthesia treatment was used to manipulate experimental flies. In addition to pilot studies by us, comparisons of independent measurements from previous studies (Norry et al., 2004, 2008), indicated that KRHT at 36–37 8C does not differ between 3 and 5 days of age both in D48 and SH flies. About 70 flies of 3 days of age for each RIL were released within the knockdown tube at 37 0.5 8C. KRHT was scored by using a collecting vial which was replaced every 30 s until the last fly in the column was knockeddown. Measurements for KRHT were replicated two times at different days, and the mean value of each replicated measurement was averaged to obtain the final estimate of KRHT for each RIL. In addition, KRHT was measured in flies that received no heat-hardening treatment as well as in flies that were heat-exposed for 35 min at 36 8C, 22 h before releasing the experimental flies in the knockdown tube to measure KRHT. These replicated measurements of KRHT were not significantly different from those reported in Norry et al. (2008). The reason why KRHT was measured 22 h after the heat-shock treatment in heat-hardened flies (as in Norry et al., 2008), was based on the fact that heat-hardening effects such as induced thermotolerance are maximal between 20 and 24 h after heat shock (when heat-shock proteins returned to basal levels), in contrast to minimal or even non-detectable inducible thermotolerance phenotypes within the first few hours after heat shock (reviewed in Hoffmann et al., 2003). The eight RIL lines in this study were chosen from Norry et al. (2008) not only on the basis of QTL genotypes but also on the basis of KRHT phenotypes. Three lines for low, 2 lines for intermediate plus 3 lines for high KRHT in non-hardened flies were used (Fig. 2). These lines were also chosen on the basis of KRHT in heathardened flies, as heat-hardening generally improves KRHT (Norry et al., 2008). Some lines that showed no KRHT change by heathardening, other lines that increased KRHT by heat-hardening and other lines that decreased KRHT after heat-hardening were chosen from Norry et al. (2008). 2.3. RNA extraction Total RNA was extracted using the RNeasy minikit (Qiagen, Hilden, Germany) and 12 adult females were immersed in 600 ml of RLT buffer containing b-mercapthoethanol. Flies were immediately processed using a Tissue-Tearor rotor homogenizer (BioSpec Products, Inc., OK, USA) for 20–30 s at max speed (35,000 rpm) until the whole body of each fly was completely homogenized. RNA purification was completed using the manufacturer’s instructions including an additional DNAse treatment step for 15 min at 25 8C in order to remove any remaining genomic DNA. Total RNA was eluted from the Qiagen spin columns using 30 ml of RNAse-free water and stored at 20 8C. Concentration of extracted RNA was determined at 260 nm in a standard Hellma F.M. Norry et al. / Journal of Insect Physiology 55 (2009) 1050–1057 1052 cuvette (path length 10 mm) using the GeneQuant II (RNA/DNA Calculator, Pharmacia Biotech). Table 2 qRT-PCR amplification efficiency for ten primer pairs. Genes Amplification efficiencies (E) Correlation relations (R2) Slopes trap1 ddc CG10383 cyp6a13 catsup actin-79B hsp60 hsc70-3 irk3 (1) irk3 (2) 0.99955 1.07615 1.07977 1.04582 1.05802 1.04391 1.09558 1.08021 1.34431 0.84169 R2 = 0.9909 R2 = 0.9967 R2 = 0.9957 R2 = 0.9907 R2 = 0.9950 R2 = 0.9958 R2 = 0.9951 R2 = 0.9975 R2 = 0.9713 R2 = 0.9932 3.3230 3.1520 3.1445 3.2168 3.1903 3.2210 3.1123 3.1436 2.7026 3.7705 2.4. cDNA synthesis (reverse transcription) Two mg of total RNA was used in the reverse transcription (RT) of RNA into cDNA employing the SuperScriptII RNase H-Reverse Transcriptase kit (Invitrogen). RT was performed in a reaction volume of 20 ml containing 1 reaction buffer, 5 mM MgCl2, 1 mM dNTP mixture, 1 ml of anchored oligo-(dT)20 primer (2.5 mg/ml) and 1 ml of SuperScriptII reverse transcriptase. Following RT, samples were diluted ten fold and stored at 20 8C until analysis. 2.5. Candidate genes and primers Seven candidate genes were chosen to test for any possible variation in expression level among the above mentioned 8 RIL. CG10383 (37A1), catsup (37B), ddc (37C), trap1 (42C) and cyp6a13 (44D3) (slightly outside the QTL region), are linked in a central region of chromosome 2. hsp60 (10A) and hsc70-3 (10E3) are linked to band 10 of chromosome X. hsp60, hsp70-3, catsup, ddc and trap1 are not regulated by heat stress (Sørensen et al., 2005). CG10383 and cyp6a13 appear to be up-regulated and down-regulated, respectively, by heat stress. Primer pairs (Table 1) were designed from transcript gene sequences from Flybase (www.flybase.org) using Primer-3 software (Rozen and Skaletsky, 2000) synthesized by DNA Technology (Aarhus, Denmark). 2.6. Quantitative real-time PCR qRT-PCR was performed on the Lightcycler 3.5 (Roche) using SYBR Green chemistry (LightCycler FastStart DNA Master SYBR Green I kit, Roche). Using the Lightcycler relative quantification software 3.5 (Roche) we employed the fully automated method for CT-determination called the ‘‘2nd Derivative Maximum’’ calculation. All qRT-PCR reactions were performed as follows: 10 min at 95 8C, followed by 40 cycles of 95 8C for 15 s, 60 8C for 10 s and 72 8C for 10 s. Melting curve analysis was performed following each reaction to confirm the presence of only a single product in the reaction. qRT-PCR of a serial dilution of cDNA was conducted for all primer pairs to confirm high and identical (close to 100%) Table 1 Primer pairs for seven candidate genes, 1 housekeeping gene and irk3. All oligonucleotides were designed by primer 3 based on transcript sequences. Primers Sequences actin-79B (left) actin-79B (right) ddc (left) ddc (right) catsup (left) catsup (right) trap1 (left) trap1 (right) hsp60 (left) hsp60 (right) hsc70-3 (left) hsc70-3 (right) cyp6a13 (left) cyp6a13 (right) CG10383 (left) CG10383 (right) irk3 (left) (1) irk3 (right) (1) irk3 (left) (2) irk3 (right) (2) 50 -ATCCGCAAGGATCTGTATGC-30 50 -AGTGCGGTGATTTCCTTTTG-30 50 -ACACAAATGGATGCTGGTGA-30 50 -AAGAGGGTCCACATTGAACG-30 50 -TCAAAGTGCTTCTGGCCTTT-30 50 -CTCCATGACTGTGTGGATGC-30 50 -AGAGGTGCCTGACGTTGAGT-30 50 -TGATTACCGCCTTGATCCTC-30 50 -GCGTTATCACCGTCAAGGAT-30 50 -TGATGAAGTACGGCGAGATG-30 50 -CGCATCGAAATTGAATCCTT-30 50 -TTCAGGGTGGAACGGAATAG-30 50 -CTCCACCACCATGTCCTTCT-30 50 -TTTTGGTTGTTGCGTTTCAA-30 50 -CAAGCCCATTTACAGGCAAT-30 50 -ACTGGCCATCCTGATCTTTG-30 50 -AACTGAAGCTGGAGGGGAAT-30 50 -TTTCGCTGTGGTGAACTGAG-30 50 -AACTGAAGCTGGAGGGGAAT-30 50 -TCGCTGTGGTGAACTGAGAC-30 amplification efficiencies. The amplification efficiencies of 7 candidate genes and 1 housekeeping gene are shown in Table 2. Relative gene expression values of candidate genes were normalized to actin-79B expression using the comparative method (Livak and Schmittgen, 2001). Four different samples from each line or treatment were analyzed with qRT-PCR and results are presented as relative mean expression levels SE. Variation in gene expression levels was tested with two-way ANOVAs for each gene, using RIL and heat-hardening treatment as fixed factors. Expression levels were further analyzed by computing the non-parametric, across-RIL Kendall’s tau correlations between phenotype (KRHT) and gene expression levels. Kendall’s tau is informative to test the association hypothesis in the present study, as this statistics represents the probability that two variables (e.g., KRHT and gene expression) are associated rather than a product-moment correlation (Sokal and Rohlf, 1995). The putative association between gene expression and KRHT phenotypes was further tested in a factor analysis by computing composite variables of gene expression. Factor analysis can be used to describe the structure of covariation among suites of expressed genes (Peterson, 2002; Coffman et al., 2005). In factor analysis, gene networks may be identified by the factor loadings (Peterson, 2002; Coffman et al., 2005). Factor loadings (1 to 1, range) provide a good index of the influence the transcript level of each gene upon the factor. Such expression factors can then be examined for associations with quantitative traits (Coffman et al., 2005). Normalized VARIMAX rotation was used to obtain the first two Principal Components (hereafter referred to as expression PCs: e-PCs) from (i) all gene expression data (N = 32 per gene, with 7 genes 4 measurements per gene per RIL) and (ii) expression data averaged per RIL (N = 8 per gene, with 7 genes per RIL), separately for non-hardened and heat-hardened RIL. Given that Principal Components are orthogonal variables, e-PC scores were used to test for true association between each e-PC and KRHT. This association was tested by computing Kendall’s correlations as well as by regressing KRHT on each e-PC. In addition, we computed Kendall’s correlations between QTL genotype and e-PCs. QTL genotype is the number of alleles (0 or 2) from the high-resistance (SH2) line for either maker within each QTL region (Fig. 1). Reported correlations of QTL genotype correspond to markers that had higher associations to each e-PC. The above indicated association-tests yielded identical conclusions whether e-PCs were computed from (i) all expression data (N = 32) or (ii) averaged data per RIL (N = 8 per gene). Therefore, e-PCs results were robust. All analyses were performed using the STATISTICA package (StatSoft, 1999). 3. Results KRHT was highly variable among RIL in this study, with significant interactions between line and heat-hardening treatment (Fig. 2; two-way ANOVA based on mean values of two F.M. Norry et al. / Journal of Insect Physiology 55 (2009) 1050–1057 Fig. 1. Chromosomes X and 2 (A and B, respectively) showing candidate genes on the physical map, QTL regions (dashed lines) and genotypes (white and black segments) for each RIL. White and black chromosomal segments correspond to D48 and SH genotypes, respectively, as inferred from microsatellite loci. D48 is the parental line of low KRHT; SH is the parental line of high KRHT. Tested genes and markers are shown for each QTL region. 1053 (Table 5). Expression levels of hsc70-3 and hsp60 were found to be negatively associated across RIL (Table 4). KRHT phenotypes were negatively associated to hsc70-3 in heat-hardened flies (Table 5), where this gene was up-regulated by heat-hardening treatment (Fig. 3b; Table 3). Factor analysis provided gene expression composite variables (e-PCs) that robustly explained most of the variation in expression traits (Table 6). In non-hardened flies, trap1–catsup–ddc had their highest loadings on e-PC1, explaining most of the total variation both in an overall analysis (based on all 7 genes with 4 replicated measurements per RIL) and in a subset analysis based on the subset of 5 autosomal loci (Table 6; Fig. 4). This composite variable (ePC1) was highly significantly associated to KRHT phenotype in both analyses (Table 6; Fig. 4a and c, regression statistics are: b = 0.57***, F1,30 = 14.87***, R2 = 0.33 for the whole analysis; b = 0.57***, F1,30 = 14.57***, R2 = 0.33 for the subset analysis; ***P < 0.001). Both CG10383 and cyp6a13 positively contributed to e-PC2, and this composite variable was associated with QTL genotype but did not significantly correlate with KRHT (Table 6; Fig. 4b and d). Similar results were obtained when expression values were averaged across RIL for e-PC analysis (N = 8 instead of N = 32 per gene; Table 6; Fig. 4). In heat-hardened flies, both X-linked genes (hsp60 and hsc70-3) had their highest factor loadings on e-PC2, and this composite variable significantly correlated with KRHT (Table 6; Fig 4d, regression statistics are: b = 0.86***, F1,30 = 85.18***, R2 = 0.74; ***P < 0.001)). This correlation between e-PC2 and KRHT disappeared when both X-linked genes were removed from analysis (see panel B, Table 6; Fig. 4h), indicating that the association between e-PC2 and KRHT (whole analysis) was due to X-linked genes only. Interestingly, hsp60 was negatively related to hsc70-3 in e-PC2 for heat-hardened flies, a result that was also significant in non-hardened flies (Table 3). The remaining genes differentially contributed to either e-PC1 or e-PC2 in heat-hardened flies but had no association to KRHT phenotypes, indicating that our set of autosomal genes had no major impact on KRHT in heat-hardened RIL. 4. Discussion Fig. 2. Knockdown resistance to high temperature (KRHT, in sec) in eight RIL lines is shown both for heat-hardened and non-hardened females. Numbers on the X-axis correspond to arbitrary labels used for each RIL. replicated measurements of KRHT, with (1) RIL and (2) heathardening treatment as fixed factors: F7,16 = 94.40 for (1), F1,16 = 0.18 for (2), F7,16 = 20.29 for (1) (2); ***P < 0.001). Further, associations were significant between KRHT and QTL genotypes (across-RIL Kendall’s correlations between KRHT and relevant markers in Fig. 1: 0.73* for AC004759 and 0.76** for DROSEV in non-hardened flies; 0.44 for AC004759 and 0.28 for DROSEV in heat-hardened flies; *P < 0.05; **P < 0.005). In addition, expression levels of tested genes differed significantly across RIL (Fig. 3; Table 3). For our set of candidate genes, expression levels were not consistently higher in heat-hardened than in non-hardened RIL, except for hsc70-3 and CG10803 (Fig. 3; Table 3). Specifically, hsc703 and CG10803 were up-regulated by heat-hardening treatment (Fig. 3; Table 3). Paired comparisons using Tukey’s tests confirmed increased expression of these two genes in heat-treated flies (*P < 0.05). Across-RIL correlations between gene expression data were high and positive among trap1, ddc and catsup in both nonhardened and heat-hardened individuals as well as between CG10803 and cyp6a13 after heat-hardening treatment (Table 4). trap1 was associated to KRHT phenotypes across all eight RIL The results show significant associations between thermotolerance phenotypes (KRHT) and gene expression of both autosomal and X-linked genes that map within QTL regions for KRHT in D. melanogaster. Further, expression levels of candidate genes were correlated within each QTL region. Two candidate genes within the X-linked QTL were negatively inter-correlated whereas high and positive correlations were found among candidate genes within the major autosomal QTL for thermotolerance (Tables 4 and 6). Previously identified QTL encompassed a number of candidate loci that also exhibited co-localization, especially in the 34C–43A region (chromosome 2), for three independently generated mapping populations from different continents (Norry et al., 2004, 2007a,b, 2008; Morgan and Mackay, 2006). Therefore, this QTL region is likely to contain some of the most relevant loci for adaptation to thermal stress in adult insects on diverse continents. In this study we did examine relative gene expression of some candidate genes in the KRHT-QTL. Candidate loci within this major QTL were tested for among RIL variation in gene expression both in non-hardened and heat-hardened flies. The results are rather supporting the hypothesis that our scanned QTL of chromosome 2 contains genes that influence variation in thermotolerance. Recent assays using global microarray gene expression scans in temperature-selected populations have not revealed whether or not dramatic responses to thermal selection result from changes in gene expression of genes within the 34C–43A region (Sørensen F.M. Norry et al. / Journal of Insect Physiology 55 (2009) 1050–1057 1054 Fig. 3. Relative gene expression levels among RIL lines are shown for expression data of seven candidate genes from two QTL affecting knockdown resistance to high temperature. Expression data are shown for both non-hardened and heat-hardened RIL (a–g). Numbers on the X-axis correspond to arbitrary labels used for each RIL from Norry et al. (2008). Table 3 ANOVA results for normalized mean values of gene expression are shown for each gene tested, with (1) RIL and (2) heat-hardening treatment as fixed factors. Source of variation d.f. (1) RIL (2) Heat-hardening (1) (2) interaction 7 1 7 ** *** F ratio hsp60 hsc70-3 CG10383 catsup ddc trap1 cyp6a13 25.10*** 0.71 3.80** 10.63*** 34.02*** 3.95** 22.48*** 89.41*** 3.788** 13.66*** 0.01 2.50* 36.75*** 8.29** 1.31 22.05*** 3.13 1.84 41.39*** 3.14 0.96 P < 0.01. P < 0.001. et al., 2007). The present study suggests that qRT-PCR for candidate loci within our QTL region represents a useful complementary approach to dissect candidate genes for differential expression affecting thermotolerance in RIL lines. Principal component analysis can identify orthogonal variables of gene expression that can contribute to search specific QTL, as e-PCs best predicted KRHT phenotypes in contrast to single gene analyses (Fig. 4, Table 6). In factor analysis of gene expression, factors can be extracted to find F.M. Norry et al. / Journal of Insect Physiology 55 (2009) 1050–1057 Table 4 Kendall’s non-parametric correlations are shown for significant associations between gene expression levels of candidate genes from two QTL regions in either non-hardened or heat-hardened RIL. Genes Non-hardened Heat-hardened hsp60–hsc70-3 catsup–ddc catsup–trap1 ddc–trap1 CG10383–cyp6a13 0.64* 0.86** 0.71* 0.71* 0.71* ns ns 0.57* 0.64* 0.79** * ** P < 0.05. P < 0.01. networks or groups of co-regulated genes (Coffman et al., 2005). Network effects among genes within QTL regions can be predicted if gene expression profiles are either positively or negatively correlated among genes within each QTL. Closely linked candidate Table 5 Kendall’s non-parametric associations are shown between expression levels of candidate genes and KRHT phenotypes both in non-hardened and heat-hardened RIL. Expression data are based on averaged q-PCR measurements in 8 RIL (Fig. 3). Gene Non-hardened Heat-hardened hsp60 hsc70-3 catsup ddc trap1 CG10383 cyp6a13 0.14 0.21 0.43 0.43 0.57* 0.28 0.14 0.36 0.57* 0.07 0.42 0.36 0.21 0.14 * P < 0.05. Table 6 Factor loadings are shown for the first two Principal Components of gene expression variation (expression PCs: e-PC1; e-PC2) in both non-hardened and heat-hardened RIL. Underlined values indicate the highest contributions of each gene to each e-PC (factor loadings higher than 0.7 identified genes that contributed differentially to either e-PC1 or e-PC2). Values in parentheses indicate % of the total variation in gene expression explained for the studied genes. Bold face values indicate significant associations (Kendall tau coefficients) between each e-PC and (i) KRHT phenotype, (ii) X-linked QTL genotype, and (iii) autosomal QTL genotype. Results are shown for: (A) whole set of data (N = 32 for each case)) and (B) averaged data of expression (N = 8) for the subset of autosomal genes (identical results were obtained for N = 32 in this subset analysis). P-values were corrected for multiple comparisons by using the sequential Bonferroni test separately for each A and B analysis. Candidate gene Non-hardened flies Heat-hardened flies e-PC1 e-PC1 e-PC2 (A) Whole analysis hsp60 hsc70-3 catsup ddc trap1 CG10383 cyp6a13 (% Var) 0.6614 0.2359 0.9088 0.9107 0.8524 0.4282 0.0642 (44%) 0.5432 0.6207 0.1038 0.1278 0.1369 0.8040 0.8795 (30%) (i) KRHT phenotype (ii) X-linked QTL (iii) Autosomal QTL 0.37* 0.02 0.07 (B) Subset analysis catsup ddc trap1 CG10383 cyp6a13 (% Var) 0.9838 0.9730 0.9338 0.4837 0.1015 (61%) (i) KRHT phenotype (iii) Autosomal QTL 0.57* 0.24 * P < 0.05. 0.28 0.29 0.43* 0.1656 0.1311 0.7888 0.6319 0.5335 0.9254 0.6528 (38%) 0.11 0.12 0.33* e-PC2 0.7771 0.7842 0.3056 0.6718 0.4516 0.0159 0.3550 (30%) 0.66* 0.30* 0.34* 0.0150 0.1531 0.1739 0.8439 0.9795 (34%) 0.8144 0.8787 0.9577 0.7311 0.0215 (58%) 0.3969 0.1534 0.1842 0.6088 0.9936 (31%) 0.28 0.55* 0.28 0.05 0.29 0.54* 1055 genes may be co-regulated, including position effects. Consistent with factor analysis (Table 6), we found three closely linked genes (trap1, ddc, catsup) to be positively correlated within the above discussed major autosomal QTL whereas two other genes (hsp60 and hsc70-3) were negatively associated in their expression levels within X-linked QTL in our lines. Future work will have to test for any possible network effects among these and other genes within these thermotolerance QTL. Single marker analysis has some difficulty in finding network effects or multiple QTL within QTL regions for a quantitative trait because of genotypic correlations (Zeng, 2005; Chen and Kendziorski, 2007; Sillanpaa and Noykova, 2008). In this study, we dissected a subset of previously identified candidate genes within QTL regions. We found composite variables of expression (ePCs) that best predicted KRHT phenotypes (Table 6). However, these e-PCs were sometimes uncorrelated with the corresponding QTL genotype even though they were correlated with phenotype (Table 6). These results strongly suggest that network effects rather than single genes are responsible for the QTL studied. Quantitative traits are shaped by networks of pleiotropic genes (Mackay, 2001). Genes that showed associations with either KRHT phenotypes or expression profiles between genes within the QTL regions in this study are known to have multiple effects on quantitative variation. catsup encodes a negative regulator of tyrosine hydroxylase, the rate-limiting step in the synthesis of the neurotransmitter dopamine, and it is a pleiotropic quantitative trait gene in Drosophila (Carbone et al., 2006). catsup is associated with naturally occurring variation in multiple quantitative traits but individual QTNs do not have pleiotropic effects (Carbone et al., 2006), and it was associated with expression levels of other genes but not with KRHT phenotypes in this study (Table 4). catsup, ddc and trap1 expression levels are combined within the autosomal thermotolerance QTL of chromosome 2 (Table 6). catsup and ddc are involved in the catecholamine pathway (Wright, 1987), which has been implicated in the response to temperature extremes and other environmental stressors (Baden et al., 1996; Sabban and Kvetnansky, 2001; Morgan and Mackay, 2006). catsup and trap1 showed related patterns of gene expression in this study (Fig. 4). trap1 was significantly associated with KRHT phenotypes (Table 4). This is a heat-shock protein-related gene that was recently implicated as candidate QTL-gene for thermotolerance (Morgan and Mackay, 2006). Our present results further support such previously inferred roles of trap1. Further, the significant correlation among trap1, e-PC1 and KRHT in non-hardened flies disappeared after heat-hardening (Table 4), which is consistent with the previous finding that the major thermotolerance QTL on chromosome 2 shows very reduced effects on KRHT after heathardening (Norry et al., 2008). Two closely linked candidate genes on the X chromosome (hsp60 and hsc70-3) were also associated in their expression levels within the X-linked QTL (Table 4). Interestingly, these two genes were negatively correlated in their gene expression levels across RIL, and hsc70-3 was negatively associated with KRHT phenotypes in heat-hardened flies (Table 4). Previous work showed that hsp60 and hsc70-3 were included within a large-effect QTL for KRHT (Norry et al., 2007b, 2008). Both the heat-shock protein cognate 3 (hsc70-3) and hsp60 are not induced by heat shock in Drosophila (Birch-Machin et al., 2005; Sørensen et al., 2005), but hsc70-3 was at least up-regulated by heat stress (Bettencourt et al., 2008; present study). Recently, Bettencourt et al. (2008) had further tested expression patterns of these genes in other lines of D. melanogaster, showing significant changes in gene expression with temperature. hsc70-3 is a DNAk-type of molecular chaperone but its precise functions are still poorly understood in Drosophila. The present results support some role of this gene on thermotolerance, as hsc70-3 increased its expression level after heat-hardening and its expression level was negatively associated to KRHT in heat- 1056 F.M. Norry et al. / Journal of Insect Physiology 55 (2009) 1050–1057 Fig. 4. Knockdown resistance to high temperature (KRHT) is plotted against the first two Principal Components of gene expression variation in seven candidate genes (expression PCs: e-PC1; e-PC2) in both non-hardened and heat-hardened RIL. Expression PCs were extracted from both the whole set of seven scored genes (a–d) and the subset of five autosomal genes (e–h). Results are shown for e-PCs obtained from both (i) all expression data (N = 32 per gene, plus signs) and (ii) averaged data per RIL (N = 8 per gene, circles). Solid and dashed lines correspond to significant regressions of KRHT on each e-PC for the above mentioned (i) and (ii) analyses, respectively (a single line is shown in [a] because both slopes overlapped). Linear regression statistics are given in the text. Factor loadings of each gene on each e-PC are given in Table 6. hardened RIL only (Table 4). Moreover, hsc70-3 expression level was negatively correlated with hsp60 expression (Table 5), and both genes are combined within a single QTL region. hsp60 is constitutively expressed in muscle (FlyBase Consortium, http:// FlyBase.org). The cellular distribution of hsp60 can change under stress conditions, with hsp60 leaving the cytosol and translocating to the plasma membrane, and this chaperonin appears to have a key regulatory role in apoptosis (reviewed in Gupta and Knowlton, 2005). In insects including Drosophila, genetic variation in KRHT is partially influenced by temperature-induced changes in gene expression (Sørensen et al., 2007). However, genetic variation in heat-induced thermotolerance is lower than genetic variation in basal (non-induced) thermotolerance (Norry et al., 2008). Moreover, not all of the phenotypic variation in thermotolerance may be the result of changes in gene expression level. Our qRT-PCR results are consistent with results from microarray assays in that most studied genes in this study showed no substantial heat-induced expression within our QTL regions (Fig. 3; Sørensen et al., 2005, 2007). However, two loci that were not identified as up-regulated by heat-shock in microarray assays were clearly up-regulated in heat-hardened flies in this study. Acknowledgments We thank Ary Hoffmann for letting us use his SH lines when starting our baselines, Jesper Dahlgaard for earlier collaboration on the set up of QTL lines, Pablo Sambucetii and Alejandra Scannapieco for collaboration on the set up of RIL, Jane Frydenberg for advice with the application of the qRT-PCR procedures, and two anonymous reviewers for helpful comments on the manuscript. This research was supported by frame and center grants from the Danish Natural Sciences Research Council to VL. 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