Download Combined expression patterns of QTL

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.
Additional support from ANPCyT, UBA and CONICET-Argentina
to FMN and from the Chinese–Danish Government Scholarship to
YJL is also acknowledged.
F.M. Norry et al. / Journal of Insect Physiology 55 (2009) 1050–1057
References
Baden, H.P., Kollias, N., Anderson, R.R., Hopkins, T., Raftery, L., 1996. Drosophila
melanogaster larvae detect low doses of UVC radiation as manifested by a
writhing response. Archives of Insect Biochemistry and Physiology 32, 187–
196.
Bettencourt, B.R., Hogan, C.C., Nimali, M., Drohan, B.W., 2008. Inducible and constitutive heat shock gene expression responds to modification of Hsp70 copy
number in Drosophila melanogaster but does not compensate for loss of thermotolerance in Hsp70 null flies. BMC Biology 6, 5.
Birch-Machin, I., Gao, S., Huen, D., McGirr, R., White, R.A.H., Russell, S., 2005.
Genomic analysis of heat-shock factor targets in Drosophila. Genome Biology
6, R63.
Brown, A.C., Olver, W.I., Donnelly, C.J., May, M.E., Naggert, J.K., Shaffer, D.J., Roopenian, D.C., 2005. Searching QTL by gene expression: analysis of diabesity. BMC
Genetics 6, 12.
Carbone, M.A., Jordan, K.W., Lyman, R.F., Harbison, S.T., Leips, J., Morgan, T.J., DeLuca,
M., Awadalla, P., Mackay, T.F.C., 2006. Phenotypic variation and natural selection at catsup, a pleiotropic quantitative trait gene in Drosophila. Current Biology
16, 912–919.
Chen, M., Kendziorski, C., 2007. A statistical framework for expression quantitative
trait loci mapping. Genetics 177, 761–771.
Coffman, C.J., Wayne, M.L., Nuzhdin, S.V., Higgins, L.A., McIntyre, L.M., 2005.
Identification of co-regulated transcripts affecting male body size in Drosophila.
Genome Biology 6, R53.
Curtis, C., Landis, G.N., Folk, D., Wehr, N.B., Hoe, N., Waskar, M., Abdueva, D.,
Skvortsov, D., Ford, D., Luu, A., Badrinath, A., Levine, B.L., Bradley, T.J., Tavaré,
S., Tower, J., 2007. Transcriptional profiling of MnSOD-mediated lifespan extension in Drosophila reveals a species-general network of aging and metabolic
genes. Genome Biology 8, R262.
Folk, D.G., Zwollo, P., Rand, D.M., Gilchrist, G.W., 2006. Selection on knockdown
performance in Drosophila melanogaster impacts thermotolerance and heatshock response differently in females and males. Journal of Experimental
Biology 209, 3964–3973.
Harbison, S.T., Chang, S., Kamdar, K.P., Mackay, T.F.C., 2005. Quantitative genomics
of starvation stress resistance in Drosophila. Genome Biology 6, R36.
Hoffmann, A.A., Sørensen, J.G., Loeschcke, V., 2003. Adaptation of Drosophila to
temperature extremes: bringing together quantitative and molecular
approaches. Journal of Thermal Biology 28, 175–216.
Hoffmann, A.A., Willi, Y., 2008. Detecting genetic responses to environmental
change. Nature Reviews Genetics 9, 421–431.
Huey, R.B., Crill, W.D., Kingsolver, J.G., Weber, K.E., 1992. A method for rapid
measurement of heat or cold resistance of small insects. Functional Ecology
6, 489–494.
Livak, K.J., Schmittgen, T.D., 2001. Analysis of relative gene expression data using
real-time quantitative PCR and the 22DDT method. Methods 25, 402–408.
Gupta, S., Knowlton, A.A., 2005. HSP60, bax, apoptosis and the heart. Journal of
Cellular and Molecular Medicine 9, 51–58.
Mackay, T.F.C., 2001. The genetic architecture of quantitative traits. Annual Review
of Genetics 35, 303–339.
1057
Morgan, T.J., Mackay, T.F.C., 2006. Quantitative trait loci for thermotolerance
phenotypes in Drosophila melanogaster. Heredity 96, 232–242.
Norry, F.M., Dahlgaard, J., Loeschcke, V., 2004. Quantitative trait loci affecting
knockdown resistance to high temperature in Drosophila melanogaster. Molecular Ecology 13, 3585–3594.
Norry, F.M., Gomez, F.H., Loeschcke, V., 2007a. Knockdown resistance to heat stress
and slow recovery from chill coma are genetically associated in a central region
of chromosome 2 in Drosophila melanogaster. Molecular Ecology 16, 3274–3284.
Norry, F.M., Sambucetti, P., Scannapieco, A.C., Gomez, F.H., Loeschcke, V., 2007b. Xlinked QTL for knockdown resistance to high temperature in Drosophila melanogaster. Insect Molecular Biology 16, 509–513.
Norry, F.M., Scannapieco, A.C., Sambucetti, P., Bertoli, C.I., Loeschcke, V., 2008. QTL
for the thermotolerance effect of heat hardening, knockdown resistance to heat
and chill-coma recovery in an intercontinental set of recombinant inbred lines
of Drosophila melanogaster. Molecular Ecology 17, 4570–4581.
Nuzhdin, S.V., Harshman, L.G., Zhou, M., Harmon, K., 2007. Genome-enabled
hitchhiking mapping identifies QTLs for stress resistance in natural Drosophila.
Heredity 99, 313–321.
Peterson, L.E., 2002. Factor analysis of cluster-specific gene expression levels from
cDNA microarrays. Computer Methods and Programs in Biomedicine 69, 179–
188.
Rako, L., Blacket, M.J., McKechnie, S.W., Hoffmann, A.A., 2007. Candidate genes and
thermal phenotypes: identifying ecologically important genetic variation for
thermotolerance in the Australian Drosophila melanogaster cline. Molecular
Ecology 16, 2948–2957.
Rozen, S., Skaletsky, H.J., 2000. Primer3 on the WWW for general users and for
biologist programmers. In: Krawetz, S., Misener, S. (Eds.), Bioinformatics Methods and Protocols: Methods in Biology. Humana Press, Totowa, NJ, pp. 365–386.
Sabban, E.L., Kvetnansky, R., 2001. Stress-triggered activation of gene expression in
catecholaminergic systems: dynamics of transcriptional events. Trends in
Neurosciences 24, 91–98.
Sillanpaa, M.J., Noykova, N., 2008. Hierarchical modeling of clinical and expression
quantitative trait loci. Heredity 101, 271–284.
Sørensen, J.G., Nielsen, M.M., Kruhøffer, M., Justesen, J., Loeschcke, V., 2005. Full
genome gene expression analysis of the heat stress response in Drosophila
melanogaster. Cell Stress and Chaperones 10, 312–328.
Sørensen, J.G., Nielsen, M.M., Loeschcke, V., 2007. Gene expression profile analysis
of Drosophila melanogaster selected for resistance to environmental stressors.
Journal of Evolutionary Biology 20, 1624–1636.
Sokal, R.R., Rohlf, F.J., 1995. Biometry the Principles and Practice of Statistics in
Biological Research. WH Freeman and Company, New York.
StatSoft, 1999. STATISTICA for Windows (Computer Program Manual). StatSoft Inc.,
Tulsa.
Telonis-Scott, M., Hallas, R., McKechnie, S.W., Wee, C.W., Hoffmann, A.A., 2009.
Selection for cold resistance alters gene transcript levels in Drosophila melanogaster. Journal of Insect Physiology 55, 549–555.
Wright, T.R.F., 1987. The genetics of biogenic amine metabolism, sclerotization, and
melanization in Drosophila melanogaster. Advances in Genetics 24, 127–222.
Zeng, Z.B., 2005. QTL mapping and the genetic basis of adaptation: recent developments. Genetica 123, 25–37.