Molecular Ecology (2011) 20, 1813–1828
doi: 10.1111/j.1365-294X.2011.05062.x
FROM THE COVER
Functional genomics of life history variation in a
butterfly metapopulation
C H R I S T O P H E R W . W H E A T , * †‡ H O W A R D W . F E S C E M Y E R , * J . K V I S T , § E V A T A S , † J . C R I S T O B A L
V E R A , * M I K K O J . F R I L A N D E R , § I L K K A H A N S K I † and J A M E S H . M A R D E N *
*Department of Biology, 208 Mueller Lab, Pennsylvania State University, University Park, PA 16802, USA, †Department of
Biosciences, University of Helsinki, PL 65, Viikinkaari 1, 00014 Helsinki, Finland, ‡Center for Ecology and Conservation, School
of Biosciences, University of Exeter, Cornwall TR10 9EZ, UK, §Institute of Biotechnology, University of Helsinki, PL 56,
Viikinkaari 9, 00014 Helsinki, Finland
Abstract
In fragmented landscapes, small populations frequently go extinct and new ones are
established with poorly understood consequences for genetic diversity and evolution of
life history traits. Here, we apply functional genomic tools to an ecological model system,
the well-studied metapopulation of the Glanville fritillary butterfly. We investigate how
dispersal and colonization select upon existing genetic variation affecting life history
traits by comparing common-garden reared 2-day adult females from new populations
with those from established older populations. New-population females had higher
expression of abdomen genes involved in egg provisioning and thorax genes involved in
the maintenance of flight muscle proteins. Physiological studies confirmed that newpopulation butterflies have accelerated egg maturation, apparently regulated by higher
juvenile hormone titer and angiotensin converting enzyme mRNA, as well as enhanced
flight metabolism. Gene expression varied between allelic forms of two metabolic genes
(Pgi and Sdhd), which themselves were associated with differences in flight metabolic
rate, population age and population growth rate. These results identify likely molecular
mechanisms underpinning life history variation that is maintained by extinction–
colonization dynamics in metapopulations.
Keywords: ecological genomics, gene expression, Glanville fritillary, microarray, mixed model
analysis, physiological ecology, polymorphism, reproduction
Received 13 November 2010; revision received 28 January 2011; accepted 8 February 2011
Introduction
Genomic studies of wild populations have the potential
to reveal the genetic basis of traits affecting fitness and
may ultimately lead to a synthesis of population biology and genomics (Ellegren & Sheldon 2008). Historically, genetic variation affecting fitness in populations
was expected to be uncommon due to fixation by selection (Fisher 1958), though opposing views were also
expressed (Dobzhansky 1955; Lewontin 1974). Over
time, research focus has shifted to understanding the
dynamics that can maintain such variation (Wade &
Goodnight 1998). Theoretical (Frank & Slatkin 2007) and
Correspondence: Christopher W. Wheat, Fax: +358 9 191 57694;
E-mail: [email protected]
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empirical studies (Cain et al. 1990; Gibbs & Grant 2002)
have shown that temporally varying selection due to
changing environmental conditions may maintain
genetic variation with large fitness effects (Bell 2010).
Similarly, it is well known that selection may vary from
one habitat type to another in a heterogeneous environment, thereby maintaining genetic variation at the landscape level (Levene 1953; Schaeffer 2008). Less well
understood is what happens in fragmented landscapes
in the absence of habitat differences but with a high
rate of population turnover. Can extinction–colonization
dynamics lead to diversifying selection and maintain
variation with large fitness effects?
An increasing number of studies have successfully
related phenotypes with known fitness effects in natural
populations to specific genetic variants. Technological
1814 C . W . W H E A T E T A L .
advances further increase the pace at which such genotype-to-fitness connections can be established across a
range of species. However, the majority of such studies
have focused upon conspicuous morphological phenotypes [e.g. coat colour in desert mice (Nachman et al.
2003) and beak size in Darwin’s finches (Abzhanov
et al. 2006)]. Few studies have used a functional
genomic approach to investigate genetic variation
affecting morphologically cryptic phenotypes that interact with ecological dynamics. To study such phenotypes
requires ecological knowledge to identify the phenotypes of interest and to sample them in an appropriate
manner.
Here we examine the consequences of repeated local
extinctions and re-establishment of new populations for
the pattern of genetic variation with large fitness effects
across a heterogeneous landscape. Specifically, we test
the hypothesis that gene expression phenotypes and
alleles associated with large phenotypic effects on
fecundity and dispersal become assorted according to
population age by the metapopulation dynamics (Hanski et al. 2004). We use data and material from the
long-term ecological study of the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands in Finland
(Hanski 1999). This large metapopulation persists in a
balance between stochastic local extinctions and establishment of new populations in a network of several
thousand small meadows (Hanski 1999). The extinction
and colonization rates and thereby the viability of this
(Hanski & Ovaskainen 2000) and other metapopulations
(Ronce & Olivieri 2004) are influenced by many life history traits. Recently, a non-synonymous SNP in the glycolytic enzyme gene phosphoglucose isomerase (Pgi)
(Orsini et al. 2009) was found to be strongly associated
with a range of life history traits (Niitepõld et al. 2009;
Saastamoinen et al. 2009) and even population growth
rate in the Glanville fritillary (Hanski & Saccheri 2006),
but beyond Pgi there is no knowledge of genetic mechanisms affecting life history variation in this species.
The strong Pgi effect on individual performance and
fitness components in the Glanville fritillary, which is
perhaps the best documented genetic polymorphism
affecting coupled ecological and evolutionary dynamics
(Hanski & Saccheri 2006; Zheng et al. 2009), has stimulated us to apply functional genomic tools to further
investigate how genetic variation interacts with metapopulation processes. Here, we compare gene expression in butterflies from new populations (habitat
patches recently colonized by females that dispersed
from existing populations) with butterflies from old
populations. With these data, we also systematically
scan for additional polymorphic loci associated with
population history. We validate findings of differentially expressed genes with physiological studies of the
relevant traits using independent samples, and we
examine how gene expression and flight metabolic phenotypes vary between allelic forms of Pgi and another
polymorphic metabolic gene revealed by our transcriptome scan. Finally, we determine how allele frequencies
at these two loci are related to metapopulation dynamics in a large independent sample.
Materials and methods
Materials
The butterflies used in the experiments were commongarden reared offspring (below) of butterflies originally
sampled from the Glanville fritillary metapopulation in
the Åland Islands in Finland (Hanski 1999). Sample
sizes and a summary of material used in the experiments are shown in Table S1 (Supporting information).
Throughout the text we refer to new and old populations. The age of local populations is known based on
long-term annual surveys since 1993 (Hanski 1999;
Nieminen et al. 2004). Here we compare butterflies
originating from new local populations, established by
dispersing butterflies, with butterflies originating from
old local populations, which had persisted for at least
5 years. Populations of both age categories were scattered across the Åland Islands (50 km · 70 km).
Sample for gene expression and metabolic rate measurements. To minimize any maternal effects, we used
second-generation butterflies that had been reared under
common garden conditions on the natural host plant
Plantago lanceolata grown in a greenhouse. The fieldcollected material consisted of !400 final instar larvae
sampled from 60 different local populations in 2005.
These larvae were reared to adults under constant environmental conditions (12:12 L ⁄ D, 25 and 20 "C), and
released into a large (30 m · 26 m) outdoor population
cage with natural conditions (Saastamoinen 2007b).
Mating and egg laying were observed and recorded
(Saastamoinen 2007b). During spring 2006, !200 of the
offspring were reared to adults under the abovedescribed conditions. Of them, 65 unmated females were
fed ad libitum honey water on day 1 post-eclosion. On
day 2, the butterflies were flown in a respirometer for
10 min (see below), and then quickly (<2 min) dissected
to isolate the head, thorax, and abdomen, which were
immediately flash frozen in liquid nitrogen. From this
set, we randomly selected an equal number of new-population and old-population butterflies to use in the gene
expression study, the population origin being determined by the known matriline. Material used in the gene
expression study comprised 34 thoraces (21 matrilines
from 9 new and 12 old populations) and, from a subset
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of the same individuals, 18 abdomens (12 matrilines from
6 new and 6 old populations). We conducted multilocus
single nucleotide polymorphism (SNP) genotyping of
butterflies used in the microarray experiment, which
showed that new and old populations do not comprise
genetically distinct subgroups (P = 0.30; Fig. S1, see Supporting information for methods). This was expected
based on the extensively documented biology of the
metapopulation, wherein butterflies have limited dispersal distances (up to 2–3 km) and new and old populations occur throughout the large habitat network
[50 km · 70 km (Hanski 1999)]. These and an additional
29 females from the same spring 2006 material described
above were genotyped for a polymorphism, succinate
dehydrogenase d (Sdhd), that we discovered in the gene
expression study (total N = 94 butterflies from 33 matrilines).
Sample for oogenesis physiology. To verify the results of
gene expression in the abdomen, we examined reproductive physiology of females reared from larvae collected in April 2005 from 11 different local populations
(6 new, 5 old). Larvae completed development on natural host plants (P. lanceolata) under common-garden
conditions. Starting on the day of adult eclosion (day 0)
and up to day 2, virgin females were sampled between
2 and 8 h after lights on, with no sampling time bias
for the two population ages, in order to minimize
potential effects of circadian rhythmicity in juvenile hormone (JH) titre that occurs in at least one insect (Zhao
& Zera 2004).
Sample for additional measurements of metabolic rate. To
examine the association between metabolic rate and
genotypes of interest in an independent sample, we
genotyped butterflies from a previous study that examined metabolism phenotypes (Niitepõld 2010). In this
case, 71 virgin females from 20 matrilines were measured for flight metabolic rate at adult day 2. The butterflies had been reared under the above-described
laboratory conditions from larvae collected in the field
in 2004.
Methods and analyses
In every analysis presented in this paper, family (matriline) was included as a random factor to account for
relatedness.
Metabolic rate measurements. Peak metabolic rate (PMR)
and total CO2 emitted during 10 min of flight were
measured using methods previously described (Haag
et al. 2005). Briefly, individual butterflies were placed in
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a plastic jar modified for flow-through respirometry
and the production of CO2 was recorded during gentle
shaking of the jar as needed to stimulate continuous
flight. A z-transformation (Bartholomew et al. 1981) was
used to remove autocorrelation caused by diffusion in
the gas stream and obtain instantaneous metabolic
rates.
Microarray design and annotation. Details of the microarray design have been reported (Vera et al. 2008).
Briefly, 454 GS20 sequencing was used with mRNA
from a diverse tissue collection. Assembled contigs
[Assembly v1.0 (Vera et al. 2008)] and singletons were
annotated using Blast homology searches against predicted gene sets of silkmoth (Bombyx mori, Lepidoptera:
Bombycidae) and fruit fly (Drosophila melanogaster, Diptera: Drosophilidae) genomes, and the UniProt database
(Vera et al. 2008). Contigs and singletons that had blast
hits (bitscore >45) were used for designing a tiled series
of hybridization probes (60 mers) using the publicly
available Agilent eArray tool. The best-performing
probe (highest hybridization intensity) per sequence
was identified from a preliminary array hybridization.
45K-feature Agilent microarrays were printed with
14 251 probes, each at least in triplicate, which corresponded to approximately 9000 unigenes. We subsequently obtained an additional !600 000 EST reads
using 454 FLX sequencing that we assembled and annotated together with the original sequences, which
formed assembly v2.0. We used the latter assembly to
better annotate the microarray probes and inform our
analyses. These assemblies, annotations (Melitaea cinxia
transcriptome assemblies v1.0 and v2.0), and raw
sequences are available at http://cinxiabase.vmhost.
psu.edu (for Assembly v1.0 data see also the NCBI
Sequence Read Archive, http://www.ncbi.nlm.nih.gov/
sites/entrez?db=genomeprj&cmd=Link&LinkName=
genomeprj_sra&from_uid=20761).
RNA labeling and hybridization to microarrays. Total RNA
was extracted using Trizol reagent (Life Technologies)
from individual flash frozen thoraces and abdomens
followed by RNA column purification (RNAeasy, Qiagen) and cDNA synthesis, with T7-(dT)24 primers.
Using this template, amino-allyl-UTP was incorporated
during in vitro aRNA amplification (Ampliscribe T7 kit;
Epicenter Biotechnologies). Subsequently, Alexa Fluor
555 or 647 dyes were incorporated using succinimidyl
ester chemistry, unincorporated dye was removed by
gel filtration, and the resulting labelled RNAs were
quantified before hybridization to Agilent arrays following manufacturer’s protocol. Quantification and quality
control assays at various steps used both Nanodrop
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and Bioanalyzer to assess aRNA quality and dye
incorporation. Using slides containing four arrays, we
hybridized labelled RNA from one old- and one newpopulation butterfly onto each array. Dye was randomized and balanced across population type for a balanced incomplete block design (Kerr & Churchill 2001)
that maximized the number of biological replicates with
no pooling, thereby maximizing estimates of both error
and population variance (Kerr 2003). Slides were
scanned at 5 lm resolution, averaged over two passes,
at 100% laser power, with PMT set to optimize dye
channel balance. Scanned images were checked in each
dye for the presence of regional effects or artefacts, with
rejected arrays being repeated.
Filtering and trimming of hybridization intensities. Log2
transformed hybridization intensities of spot median
pixel intensity were filtered to flag probes having mean
intensities within two standard deviations of either the
mean negative or positive controls (i.e. background or
saturated signal). Probes having >50% of individuals
flagged within a population age, dye, and tissue category were excluded from the analysis. Coefficients of
variation (CV) among replicated probes within arrays
that passed quality filtering were low (95% of probes
had CV <5% of their mean), indicating excellent technical performance (Table S2, Supporting information).
Expression data analysis. To identify expression differences in individual genes, we used the normalized
expression data in linear mixed model analyses (Wolfinger et al. 2001). Log2 transformed fluorescence intensities were quantile normalized by tissue and used in
mixed model analyses as implemented in JMPGenomics
3.2 (SAS Inc.), which uses the SAS PROC MIXED procedure with an adjusted (i.e. Type III) sum of squares that
allow for both fixed and random factors, as well as
gene-specific variance components (Wolfinger et al.
2001). Random factors account for the hierarchical structure of the experimental design (Spot + Slide +
Array + Butterfly + Family + Array · Spot), including
the spatial (Spot), grouped (Slide), and paired
(Array + Array · Spot) effects (Gibson & Wolfinger
2004). Inclusion of butterfly in the model accounts for
the technical effects captured in a dye · array interaction as well as the correlation among replicate spots
within a probe (Rosa et al. 2005). Inclusion of family as
a random factor accounted for correlated patterns of
gene expression among sibs.
We used a two-step approach to the mixed model
analysis of gene expression in each body section. Our
first objective was to determine expression differences
associated with population age, and so the first analy-
sis, referred to hereafter as MM-1 (mixed model 1),
included only population age (Popage) and a technical
variable (Dye) as fixed effects. Our second objective
was to examine the effects of factors independently
from their association with population age. Hence, the
second model for abdomen samples, referred to hereafter as MM-2, contained the following fixed effects:
Popage, Dye, Pgi genotype (Pgi_111_AC vs. Pgi_111_AA)
and Sdhd genotype (presence vs. absence of the Sdhd D
allele). MM-2 analyses of thorax data included those
factors along with peak metabolic rate and total CO2
production. Microarray data is available at NCBI’s Gene
Expression Omnibus (GEOArchive).
Enrichment analysis to detect co-varying expression of
functionally related groups of genes. Mean expression differences between population ages and other comparisons were generally small (<2-fold change), as expected
for a study of individuals from a single population and
an experiment that did not involve any environmental
or physiological manipulation (Oleksiak et al. 2002;
Crawford & Oleksiak 2007). These small differences
posed a challenge for inferring significant differences
while controlling type 1 error across thousands of tests,
an issue that is common in studies of standing variation
in gene expression (Mootha et al. 2003). Therefore, in
addition to a mixed model analysis of the normalized
hybridization intensities of individual genes, we used
gene set enrichment tests to identify co-varying sets of
functionally related genes. These tests used gene ontology (GO) annotations to detect over-representation of
GO categories in the ranked list of mean expression differences (Mootha et al. 2003; Al-Shahrour et al. 2007).
Gene set enrichment analysis is also an excellent tool
for identifying trans-regulated genes, as enrichment
analyses detect sets of co-regulated genes sharing a
common biological function that are commonly transregulated.
Prior to performing enrichment analyses, we minimized redundancy of the microarray probes (i.e. technical replication at the level of unique mRNA transcripts)
and used homology searches to assign Flybase gene IDs
to probes (see Supporting information for detailed
methods and Fig. S5 for a flow diagram). Flybase IDs
and gene expression data were used as inputs in an
enrichment analysis using Fatiscan (http://babelomics.bioinfo.cipf.es/). We used the D. melanogaster reference species option, which uses KEGG to assign
Flybase IDs to gene ontology (GO) terms. The results
show, at different levels of the gene ontology hierarchy,
groups of functionally related genes that have a systematic bias for higher expression in either of the two population ages. Reported P values take into account the
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F U N C T I O N A L G E N O M I C S O F M E T A P O P U L A T I O N D Y N A M I C S 1817
multiple testing inherent in the enrichment analysis;
they are adjusted P values based on False Discovery
Rate (FDR) method (Benjamini & Hochberg 1995; Benjamini & Yekutieli 2001).
Female reproductive physiology. Sampling involved collection of hemolymph for biochemical analyses of protein
and JH followed by counting chorionated eggs in dissected ovarian follicles as described in Webb et al.
(1999). Total soluble protein in hemolymph was first
quantified and then separated using SDS-PAGE. Levels
of both vitellogenins and a !78 kDa serum protein were
estimated using peak intensity of their bands in digital
gel images. Lipid hormones in hemolymph extracted
with 80% acetonitrile were analysed as in Westerlund &
Hoffmann (2004) with LC–MS ⁄ MS (Waters Micromass
Quattro micro# triple quadrupole mass spectrometer
operated in positive mode). Hormone detection and
identification was carried out by matching of retention
times with those of authentic standards and monitoring
the following diagnostic transitions: m ⁄ z 267 > 235 (JH
III), 281 > 249 (JH II), 295 > 263 (JH I), 447 > 303 (ecdysone), and 463 > 301 (20-hydroxyecdysone) with a dwell
time of 100 ms per channel. Only JH III was detected. Its
level was quantified using the external standard method
with signal-to-noise ratios of 3 and 10 to define limits of
detection and quantification, respectively. Detection
limit was 0.05 pmol.
Quantitative PCR. The same target cDNA we hybridized
to microarrays was used as template in qPCR assays
measuring the level of mRNA for angiotensin-converting enzyme, vitellogenin, and actin (cytoplasmic), with
the latter serving as the endogenous control. The Glanville fritillary transcriptome assembly v2.0 was used to
design primers with which we ran sample, standard,
and NTC reactions in triplicate on an ABI 7500 Fast
Real-Time PCR System (Applied Biosystems). Levels of
mRNA were calculated using the standard curve
method with curves for each plate and amplicon deriving from Ct values for qPCRs containing template from
six different amounts of a cDNA standard. See Supporting information for methods details.
Systematic scan for population-age differences in nucleotide
variation. Each of the 14 251 unique probes on the
microarray is potentially sensitive to genetic variation
in our sample, and our data for SNP probes (methods
described in Supporting information) showed that transcripts having a single nucleotide variant from the
microarray probe could cause a 2-fold or greater difference in hybridization efficiency, with potentially greater
changes resulting from multi-nucleotide or indel poly-
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morphism (Hughes et al. 2001). In contrast, transcript
abundance variation is likely to be small (i.e. <2 folds)
among healthy, common-garden reared individuals
from the same population. To search for ecologically
important genetic polymorphism that could produce
high hybridization variance (e.g. single or multiple
DNA differences, insertions, deletions, splice variants),
we took the following approach. First, we ranked the
probes based upon their absolute expression difference
between population ages (new vs. old) from the mixed
model analysis. Second, we ranked the probes based on
their among-butterfly variance in hybridization intensity. Third, we took the sum of these two ranks, and
ranked that value. This approach was performed on
both the abdomen and thorax expression data.
We then took the sum of these tissue ranks, and
ranked this sum. Finally, we examined the top 5 ranked
probes, i.e. those with the most extreme differences
between population types and variance among individuals across two tissue types. Each of these five probes
was examined for potential divergent allelic variation in
our EST assembly by aligning the microarray probe
with the respective contig.
Genotyping Pgi. A portion of the dscDNA generated
prior to dye incorporation was used to PCR amplify the
first 1209 bp of the Pgi gene from all individuals used
on the microarray [forward primer = first 21 bp of Pgi
gene, reverse primer was the degenerate primer previously reported (Orsini et al. 2009)], which was subsequently sequenced using internal primers [mPGI-598R
and mPGI-1192R (Orsini et al. 2009)]. All primers were
located in regions free of SNP variation, or in sole case
of known low-frequency SNP variation, the primer was
degenerate in this location.
Genotyping Sdhd. The indel polymorphism in Sdhd was
discovered initially by blasting the Sdhd microarray
probe against our transcriptome assembly and discovering that there was an additional contig containing ESTs
of the deletion allele that aligned partially to the probe
sequence. To properly characterize this polymorphism,
a portion of the 3¢-UTR containing the indel was PCR
amplified from genomic DNA or cDNA using fluorescently labelled forward (5¢- ⁄ 56-FAM ⁄ –ACTTAATGAAAAGYGTGATTG-3¢) and pig-tailed reverse (5¢-GTTTC
TTTGTTAAAAGGTCTTGAGTTCG-3¢) primers. Fragment analysis with capillary electrophoresis and
GeneMapper$ (Applied Biosystems Inc., Foster City,
CA, USA) software was used to associate size of
labelled amplicons with the indel allele. This analysis
revealed three alleles: deletion (D), mini deletion (M)
and insertion (I). Alleles were confirmed by cloning,
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sequencing and alignment (MegAlign, DNASTAR Inc.)
of amplicons from both cDNA and genomic DNA.
Results
Abdomen gene expression and fecundity phenotypes
Our primary aim was to detect possible systematic differences in the expression of functionally related groups
of genes between new and old local populations. In
abdomens, we found higher expression of larval serum
protein genes in new-population butterflies (LSPs;
GO:0005616; P = 4.5 · 10)9; Fig. 1a; Tables S3 and S4,
Supporting information). LSPs are amino acid transport
and storage molecules (Burmester 2001) regulated at the
transcription level by JH (Gkouvitsas & Kourti 2009)
and serve as protein sources during the provisioning of
developing eggs [vitellogenesis (Pan & Telfer 2001)].
Also more highly expressed in new-population butterflies were lipid transporters (lipophorins and perilipin;
GO:0005319; P = 8.6 · 10)4, Table S3, Supporting information) that are similarly involved in the mobilization
and transfer of nutrients to eggs (Teixeira et al. 2003).
Higher expression of genes putatively involved in the
mobilization of reserves for egg development should be
(a)
Larval serum proteins
Angiotensin coverting enzyme
7
(b)
6
0.4
1.2
3
2
1
0
–2
–1
Higher in
old
0
1
2
Higher in
new
Expression difference (Log2 scale)
Ace mRNA
normalized expression
4
Vitellogenin mRNA
normalized expression
- Log10 P
5
0
0
accompanied by higher expression of vitellogenin (Vg),
the major insect egg provisioning protein. We could not
assess this using the microarray because Vg transcripts
were too abundant (i.e. saturated probe signals for
nearly all individuals). Using quantitative PCR (qPCR)
on a subset of the same material, we found that Vg
transcription was !2-fold higher in new-population
butterflies (P = 0.02; Fig. 1b; Table S5, Supporting
information).
Among the individual genes that showed a significant population age difference in the abdomen microarray experiment (Table S6, Supporting information)
was angiotensin converting enzyme (Ace; Fig. 1a; P =
0.0000005). ACE regulates oviposition in Lepidoptera,
apparently through effects on hormone synthesis and
trypsin enzymes that release proteins from the fat body
(Vercruysse et al. 2004). We confirmed the microarray
result for Ace expression using qPCR and found that it
was higher in new populations (P = 0.001) and precisely mirrored the differences in Vg expression
(Fig. 1b; Table S5, Supporting information).
These results for gene expression in the abdomen
suggested that new-population females mobilize protein
from fat body reserves to provision developing eggs
more rapidly than old-population females. To test this
hypothesis, we examined a number of oogenesis physiology phenotypes in an independent sample of 0- to 2day-old virgin females (statistical analyses in Table S7,
Supporting information). New-population females had
a higher hemolymph concentration of JH III (5.3 pmol ⁄ uL vs. 3.7 pmol ⁄ uL, respectively, at the mean age;
P = 0.01; Fig. 2c), which in nymphalid butterflies stimulates oogenesis (Ramaswamy et al. 1997), along with
higher levels of total hemolymph protein (P < 0.0001;
Fig. 2d), including LSP and two vitellogenin proteins
(Apo-Vg1, Apo-Vg2; P < 0.003; Fig. 2b). New-population
females also had more mature (chorionated) eggs (P =
0.02; Fig. 2a, e), in a manner positively related to total
hemolymph protein (P = 0.0002; Fig. 2f).
New Old
Fig. 1 Differential gene expression in the abdomens of butterflies representing the new vs. old population matrilines and
related reproductive physiology. (a) Volcano plot of microarray
data highlighting probes for egg-provisioning genes (larval
serum proteins) and angiotensin converting enzyme. (b) Transcript abundance of vitellogenin (Vg; black bars) and angiotensin
converting enzyme (Ace; open bars) mRNA in the abdomen measured by quantitative PCR (N = 14, a balanced subsample of
the microarray butterflies). Vg expression was !2-fold higher
in new-population butterflies (P = 0.02). Variation in Vg
expression was mirrored by differences in Ace (P = 0.001). See
Table S5 in Supporting information for statistical analysis and
Methods for qPCR details. Note that expression differences in
Vg are confirmed at the protein level in an independent sample
shown in Fig. 2.
Thorax gene expression and flight metabolic
phenotypes
New-population females had higher peak metabolic rate
during flight than old-population females (12% mean
difference; P = 0.008, Fig. 3a, Table S8, Supporting
information; N = 65, the sample from which butterflies
were drawn randomly for the microarray experiment).
This population age difference in mean PMR confirms a
previous result on the Glanville fritillary (Haag et al.
2005). Having these data allowed us to directly examine
how gene expression varied with peak metabolic performance in addition to our primary focus on population age.
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F U N C T I O N A L G E N O M I C S O F M E T A P O P U L A T I O N D Y N A M I C S 1819
(a)
(b)
4
2
0
1
2
Age (days)
4
0
3
0
2
0
0
1
100
2
Age (days)
(e)
Number of chorionated eggs
6
Number of chorionated eggs
(d)
Hemolymph total protein (µg/µL)
JH titer (pmole/µL)
(c)
80
60
40
20
0
1
Age (days)
2
(f)
100
80
60
40
20
20
25
30
35
40
45
Hemolymph total protein (µg/µL)
Fig. 2 Comparison of female butterflies representing the new vs. old population matrilines in reproductive physiology. (a) Examples
of ovarioles at 0 and 2 days of age. (b) Coomassie blue-stained SDS-PAGE of proteins in hemolymph (0.1 lL volumes) and partially
purified egg vitellin (Vt) from individual butterflies, and for comparison from the moth Helicoverpa zea, whose vitellogenins have
been previously identified (Satyanarayana et al. 1992). From left to right, lanes 1–3 contain hemolymph collected from day 0 (D0)
through 2-day-old female butterflies from a new population, lanes 4–6 contain hemolymph collected from 0 to 2 day old female butterflies from an old population, lane 7 contains Vt from butterfly eggs, lanes 8–9 contain hemolymph collected from day 1 through
day 2 old male butterflies, lane 10 contains Vt from H. zea eggs, lane 11 contains hemolymph from day 2 old female H. zea, and lane
12 contains protein size standards (mk). Hemolymph from female butterflies and the moth contain two apoprotein bands identified
as vitellogenins (Vg) present in egg but absent in male hemolymph. The apo-Vg-1 and apo-Vg-2 bands are about 160 and 45 kDa,
respectively. Also shown is a band at about 75 kDa that is probably larval serum proteins based on size and absence from egg Vt.
(c–f) Physiological measurements (mean, SE) from an independent sample of 0–2 day old virgin females from new (open circles) and
old (closed circles) population matrilines: (c) juvenile hormone III titer; (d) hemolymph protein concentration; (e) chorionated eggs;
(f) relationship between total hemolymph protein and number of chorionated eggs. See Table S7 in Supporting information.
In our analysis of groups of functionally related genes
in the thorax, we found that new-population butterflies
had a significant tendency for higher expression of proteasome (core and regulatory particle) and unfolded
protein response genes (chaperones; Fig. 3b; Tables S9
and S10, Supporting information). Among the individual genes most strongly associated with population age
in the thorax (Table S6, Supporting information) were
! 2011 Blackwell Publishing Ltd
two protease inhibitors (serpins; Fig 3b); these had
reduced expression in new-population butterflies, consistent with elevated proteasome expression. The strongest association (P = 0.00001) between population age
and expression of an individual gene in the thorax was
NADH-ubiquinone reductase (syn. NADH dehydrogenase), a nuclear gene for the 24 kDa subunit of mitochondrial complex I, more highly expressed in old
Residual peak metabolic rate
(mL CO2/h)
1820 C . W . W H E A T E T A L .
(a)
N = 65 butterflies
1.0
0.5
0.0
–0.5
–1.0
New populations
(b) 5
Population age
(c)
PMR
6
Unfolded protein response
Proteasome
Serpin (protease inhibitor)
4
- Log10 P
Old populations
3
4
2
2
1
0
0
–1.0
–0.5
Higher in
old
0.0
0.5
1.0
Higher in
new
–1.0
–0.5
Higher in
low PMR
0.0
0.5
1.0
Higher in
high PMR
Expression difference (Log2 scale)
Fig. 3 Peak metabolic rate during flight (PMR) and thoracic gene expression for virgin female butterflies. (a) PMR (residual adjusted
for thorax mass) in different families classified according to population age, with population means indicated by dashed lines. (b–c)
Volcano plots highlighting expression differences for unfolded protein response and proteasome genes in relation to population age
and PMR. Population age was the only fixed effect in the mixed model in (b). Population age, PMR, total CO2 emitted during 10 min
flight, and Pgi and Sdhd genotypes were included as fixed effects in (c).
populations. NADH dehydrogenase affects superoxide
radical formation in the presence of elevated levels of
succinate (Muller et al. 2008), which may be meaningful
given the population age and metabolic rate association
(see below) we found for alleles of a succinate dehydrogenase gene.
Variation in gene expression in relation to flight
metabolic rate and Pgi polymorphism
Here we switch to using our second mixed model
approach (MM-2, see Methods) in order to examine
associations between gene expression and physiological
variables, independently of population age differences.
In the thorax, higher expression of proteasome (core
and regulatory particle) and unfolded protein response
genes was positively associated with mass-adjusted
peak metabolic rate during flight (PMR; Fig. 3c;
Table S9, Supporting information). These same genes
were associated with population age (MM-1 analyses
above). This suggests that the genetic variation affecting
physiological measures of flight performance is the
same variation being sorted by the metapopulation
dynamics. Some of this physiological variation may
arise from polymorphism at Pgi, since both the abdomen and thorax showed gene expression phenotypes
that varied with Pgi genotype. The Pgi_111_AC genotype associated in Glanville fritillaries with higher
fecundity (Saastamoinen 2007a) and PMR (Niitepõld
2010) had higher expression of genes involved in the
final step of oogenesis (chorion protein genes in
the abdomen, P = 1.9 · 10)22; Fig. S2, Supporting
! 2011 Blackwell Publishing Ltd
F U N C T I O N A L G E N O M I C S O F M E T A P O P U L A T I O N D Y N A M I C S 1821
information; Table S3, Supporting information) and of
oxidoreductase and ribosomal complex genes (in the
thorax; Table S9, Supporting information).
A transcriptome scan for additional population-age
associated allelic variation
In our transcriptome scan for ecologically important
alleles or splicing variation, Succinate dehydrogenase d
(Sdhd) showed the greatest population age-associated
variability in probe intensity in both the abdomen and
thorax (Fig. 4a). Hybridization intensity for Sdhd ranged
among individuals in a discontinuous fashion from near
saturation to indistinguishable from background
(Fig. 4b). This extreme range seemed highly unlikely to
reflect transcript abundance given that Sdhd encodes an
essential enzyme. Hence, we examined our 454 transcriptome assembly for evidence of polymorphism and
found multiple contigs (gene assemblies) containing different sequences suggestive of an indel in the gene
region corresponding to the microarray probe. None of
the other top five ranked genes in this analysis had as
broad a range of hybridization intensities or evidence in
the transcriptome assembly of polymorphism or splice
variation in the probe regions. To verify the polymorphism in Sdhd, we cloned and sequenced this gene from
both cDNA and genomic DNA and confirmed that the
microarray probe overlapped with an insertion ⁄ deletion
polymorphism in the 3¢ UTR, with three distinct alleles
(I, M and D; Fig. 4c). Within the indel is a predicted
target site for a micro-RNA (miR-71; Fig. 4c). Individuals homozygous for the Sdhd M allele had high microarray probe intensity signals, befitting the perfect match
between their genotype and the probe, whereas individuals lacking the M allele had greatly reduced probe
intensity. Using qPCR probes targeted to invariant
regions of the gene, we found that Sdhd expression level
in the abdomen varied significantly with the indel
genotype (Fig. S3, Supporting information).
Butterflies carrying the Sdhd D (deletion) allele had
higher expression of chorion genes in the abdomen
(P = 1.2 · 10)11; Fig. S2, Supporting information;
Table S3, Supporting information) and carbohydrate
metabolism genes in the thorax (glycolysis, pyruvate
metabolism, and TCA cycle; P = 2.3 · 10)4; Fig. 5b;
Table S9, Supporting information). Individuals carrying
the Sdhd D allele were better able to maintain a high
flight metabolic rate over 10 min of flying, hereafter
referred to as endurance (P = 0.01; Fig. 5a; N = 65;
Table S11A, Supporting information).
To further test the association between both Sdhd, Pgi
and flight phenotypes, we genotyped both loci in an
independent sample of 2-day old virgin females that
had been previously measured for metabolic rate
! 2011 Blackwell Publishing Ltd
(Table S1, Supporting information; Niitepõld 2010).
Again we found superior endurance associated with the
Sdhd D allele (P = 0.003; mean difference = 32%;
N = 69; Table S11B). Peak metabolic rate was not
related to Sdhd genotype but was significantly related to
Pgi genotype (P = 0.01; mean difference = 23%, with
Pgi_111_AC heterozygotes the highest; Table S11B).
Alleles at these two loci may interact epistatically,
as butterflies possessing both Pgi_111_AC and Sdhd D
had the greatest flight endurance (P < 0.05 for an
interaction effect; Fig. 5c; Table S11B). Analysis of
Pgi and Sdhd genotype associations in our samples indicates that they do not exhibit linkage disequilibrium
(P = 0.3 for association; R2 = 0.008). Additionally, there
is high synteny among Lepidopteran chromosomes
(d’Alencon et al. 2010), and in the model Lepidoptera,
Bombyx mori, the genes Pgi and Sdhd are located on separate chromosomes [chromosomes 20 (nscaf 2780) and
26 (nscaf 1071); based on blast against the BGI assembly
http://www.silkdb.org/silkdb/genome/index_png.html].
Hence, these two loci appear to be independently associated with different aspects of flight metabolism (peak
vs. endurance; Fig. 5a).
Sdhd alleles and metapopulation dynamics
Previous research on the Glanville fritillary metapopulation has shown that allelic variation at Pgi is associated
with large ecological effects (Hanski & Saccheri 2006).
To test the hypothesis that the newly discovered Sdhd
polymorphism has similar relationships with ecological
dynamics, we examined associations between Sdhd
indel allele frequencies and metapopulation processes.
The Sdhd D allele was more frequent in new populations (P = 0.04; N = 94 butterflies, 33 populations;
Table S12, Supporting information), in agreement with
the expectation that butterflies with greater flight
endurance are more likely to disperse (Niitepõld et al.
2009) and colonize new habitat patches. In a separate
sample used previously to demonstrate an association
between Pgi alleles and the growth rate of isolated local
populations (Hanski & Saccheri 2006), both Pgi and
Sdhd allele frequencies had highly significant effects on
growth rates of local populations (R2 = 0.64; P < 0.0001;
Table 1).
Discussion
Previous studies have shown that female Glanville fritillary butterflies inhabiting newly-established vs. old
populations are a non-random draw of the genotypes
and phenotypes present in the metapopulation (Hanski
et al. 2004). Female offspring of the founders of new
populations exhibit differences in life-history traits
1822 C . W . W H E A T E T A L .
12 000
difference and variance
Thorax, sum ranks for population age
(a)
8000
4000
5000
10 000
15 000
20 000
Abdomen, sum ranks for population age difference and variance
Log2 probe intensity
(b)
16
Sdhd
14
12
10
Actin
8
0
5
10
15
20
25
30
Microarray
(c)
Probe A_2463-t_3 TATCAAATCTTTTCGAAATCAAATTACTGCCTACTACA----CCATATACTAAAATTTGTAAAA
Deletion
MiniDeletion
Insertion
TATCAAATCTTTTCGAAATCAAATAACTGCCTAAATGA------------------------------------------------------AAAAACCTTTTTATAATG
TATCAAATCTTTTCGAAATCAAATTACTGCCTACTACA----CCATATACTAAAATTTGTAAAAAAAACTAATGTCTTTCTCTTAAATGTTTAAAAACCTTTTTATAATA
TATCAAATCTTTTCGAAATCAAATTACTGCCTAAATGAGTTACCATATACTAAAATTTGTAAAAAAA-CTCATGTCTTTCTCTTAAATGTTAAAAAACCTTTTTATAATA
5′ _AAAAAACTCATGTCTTTCT_
3′ M. cinxia Sdhd 3′ UTR region
.
3′ _AGUGAUGGGUACAGAAAGU_ 5′ bmo-miR-71
miR seed (2–8 match, 5′–3′)
Fig. 4 Systematic scan for population-age differences in alleles affecting variation in microarray probe hybridization. (a) Probes were
ranked for absolute value of population-age difference in each body region and these ranks were summed with ranks for among-butterfly variance in hybridization intensity. The plot shows that Sdhd (arrow at upper right) was the top-ranked probe in both tissues.
(b) Log2 hybridization intensity for each individual in the thorax microarray (N = 34) for Sdhd and Actin. (c) Portion of the 3¢ UTR in
Sdhd transcripts showing the microarray probe alignment over Sdhd allelic variants (M, D, and I). The insertion (I) contains a site
with a perfect match to the seed site (5¢–3¢ positions 2–8) and the next six bases of miR-71, a micro-RNA that is highly conserved in
invertebrates, including the moth Bombyx mori (bmo-miR-71).
related to dispersal (Haag et al. 2005; Hanski et al.
2006) and reproduction (Saastamoinen 2007b), including
higher flight metabolic rate, earlier mating, and more
frequent egg laying during the first days of adult life.
Here we have identified a number of differentially
expressed genes that are likely to cause these life history differences. Two-day old virgin females from
new-population matrilines have higher transcription of
! 2011 Blackwell Publishing Ltd
F U N C T I O N A L G E N O M I C S O F M E T A P O P U L A T I O N D Y N A M I C S 1823
ab ab
6
0
0
400
800
1200
–2
Time (s)
–1
Higher in
non-D
0
1
0.0
2
Higher in
D
Expression difference (Log2 scale)
/D
1
0.2
D
2
D
AA
/D
1
3
a
0.4
AA
/n
o
2
4
b
/n
o
5
3
P = 0.005
AC
Endurance
4
(c)
Central metabolism
7
AC
(b)
LS mean,total CO2 (mL)
Peak performance
- Log10 P
Flight metabolic rate (mL CO2/h)
(a)
Pgi and Sdhd genotype
Fig. 5 Flight performance of virgin female Glanville fritillary butterflies. (a) Flight metabolic rate in two siblings, exemplifying variation in endurance despite similar peak performance. (b) Thoracic gene expression differences among butterflies with and without the
Sdhd D allele, highlighting the significant enrichment of higher expression of central metabolism genes in flight muscles of Sdhd D
butterflies. (c) Flight endurance [i.e. area under the curves in panel (a)] according to Pgi_111 (AA or AC) and Sdhd (D vs. no D) genotypes, adjusted for family, mass and ambient temperature. Letters above bars indicate significant differences in a posteriori comparisons.
Table 1 Genotype frequencies and population growth
Source
Patch area
Frequency
Frequency
Frequency
Frequency
Pgi F
Pgi F · Patch area
Sdhd M allele
Sdhd M allele · Patch area
d.f.
F ratio
P
1
1
1
1
1
0.0001
10.9
22.5
19.1
21.1
0.99
0.002
<0.0001
0.0001
<0.0001
Population growth is defined as the regionally adjusted year-to-year change in the number of larval groups in 43 isolated
populations in the Åland Island metapopulation of the Glanville fritillary (see Table 1 in Hanski & Saccheri 2006). Here we examine
the effects of Pgi allozyme genotype (which corresponds closely to the Pgi_111_AC SNP genotype; Orsini et al. 2009) and Sdhd M
allele frequencies on population growth. Log transformed area of the habitat patch is included as an additional environmental
variable (see Hanski & Saccheri 2006). Frequencies were arcsine transformed to achieve normality. The regression is weighted with
the number of alleles sampled per population, and the unweighted model yielded a nearly identical result. The R2 value for the full
model is 0.64.
angiotensin-converting enzyme (Ace) and a higher JH titer
(Figs 1 and 2). These two factors are likely to upregulate (Ramaswamy et al. 1997; Vercruysse et al. 2004)
expression of LSP and Vg genes involved in the release
of stored proteins for reproduction (Figs 1 and 2).
These gene expression differences were consistent with
physiological phenotypes, as the concentration of protein in the hemolymph (blood) and the number of
mature eggs in new-population butterflies were approximately one day ahead of old-population butterflies
(Fig. 2c–e). These differences are likely to cause the earlier mating (Hanski et al. 2006) and 1-day earlier reproductive maturity (Saastamoinen 2007a) of newpopulation females, a substantial difference considering
that daily mortality among the adult butterflies is
approximately 10% (Ovaskainen et al. 2008a).
! 2011 Blackwell Publishing Ltd
Our analysis of gene expression in the thorax, which
contains the flight musculature primarily responsible
for flight metabolic rate, showed that both new-population butterflies and butterflies with higher peak metabolic rate (independently of population age) had higher
expression of proteasome and unfolded protein
response genes. Proteasome function affects the rate of
protein turnover in muscle, which has positive effects
on locomotion in insects (Haas et al. 2007), along with
pleiotropic, trans-acting effects on the regulation of gene
expression in general (Collins & Tansey 2006). Protein
turnover within muscle cells increases in response to
higher levels of circulating amino acids (Franch 2009),
and hence the higher thorax proteasome gene expression and superior flight performance of new-population
butterflies (Fig. 3; Haag et al. 2005; Niitepõld et al.
1824 C . W . W H E A T E T A L .
2009) may be causally related to their higher LSP and
Vg gene expression and higher overall protein content
in the hemolymph (Figs 1a and 2d). Previous research
in both flies (Levenbook & Baur 1984) and moths (Huebers et al. 1988; Miller & Silhacek 1992; Wu & Tischler
1995) has shown that amino acids, iron, and riboflavin
from LSPs are used to synthesize adult tissues, with
nearly 50% of radiolabelled LSP amino acids incorporated in flight muscle proteins (Levenbook & Baur
1984). Positive effects of protein mobilization on both
oogenesis and flight performance provides a mechanistic hypothesis for the absence of a trade-off between
dispersal and fecundity in this species (Hanski et al.
2006) and other lepidopterans (Zhao et al. 2009; Jiang
et al. 2010), contrary to other species in which ovaries
and flight muscles compete for protein (Roff 1977).
In addition to characterizing gene expression variation
that underlies ecologically important life history traits,
we have identified two polymorphic loci with apparent
large effects on life history phenotypes (Pgi and Sdhd).
Previous studies in the Glanville fritillary, stimulated by
the strong fitness effects of Pgi polymorphism in other
distantly related butterflies (Watt 2003) and other types
of insects (Wheat 2009), found highly significant life
history associations with Pgi alleles (Haag et al. 2005;
Hanski & Saccheri 2006; Niitepõld et al. 2009; Saastamoinen et al. 2009), and evidence for long-term balancing selection acting on the coding variation at Pgi
(Wheat et al. 2010). The present study extends that work
by examining gene expression phenotypes related to Pgi
alleles, along with the interaction between Pgi genotype
and a newly discovered polymorphism in Sdhd. We
found that butterflies with the Pgi_111_AC genotype,
associated with earlier female fecundity and higher
flight metabolic rate (Saastamoinen 2007a; Niitepõld
2010), had higher expression of chorion genes in the
abdomen (Fig. S2, Supporting information), suggesting
more rapid progression to the final stage of egg maturation (deposition of an egg shell rich in chorion protein).
Our scan of the microarray for additional population
age-associated allelic variation revealed an indel polymorphism in the 3¢ UTR of Sdhd. The deletion allele
(Sdhd D) was associated with higher expression of
energy metabolism genes in the thorax, chorion genes
in the abdomen, and higher flight endurance. This scan
was unbiased, yet identified another metabolic gene.
Glanville fritillary butterflies fuel flight exclusively by
consuming carbohydrates (respiratory exchange ratio =
1.0; Fig. S4, Supporting information), and thus Pgi and
Sdhd are in the same aerobic pathway functioning to
support active flight muscles. Sdhd alleles assort independently of Pgi alleles but these two loci appear to
interact epistatically, as the highest flight performance
occurred in butterflies possessing the Pgi and Sdhd
alleles that are disproportionately abundant in new
populations (Fig. 5c).
The indel polymorphism in Sdhd is located in the 3¢
UTR, that is, outside of the amino acid coding region
beyond the stop codon. This polymorphism may be in
linkage with other variation within the coding region,
or other parts of the gene, or potentially even the flanking chromosomal region. However, there is some evidence pointing to the 3¢ UTR polymorphism itself being
a target of selection, as 3¢ UTRs commonly affect transcript processing and ⁄ or stability. The polymorphism in
Sdhd is a potential candidate for differential effects on
translational control because it contains a putative target site for a microRNA, miR-71 (Fig. 4c). Polymorphism for the miR-71 target is particularly interesting
because this microRNA has 100% sequence conservation across invertebrates and is known to affect life history. In the C. elegans nematode, miR-71 was the top
gene revealed by a scan for microRNAs affecting life
history, with target sites in the 3¢ UTR of a number of
genes that affect insulin signalling and life span. Manipulation of miR-71 expression in that study caused large
effects on lifespan and responses to thermal and oxidative stress (de Lencastre et al. 2010).
Functional genomics studies in butterflies are beginning to identify alleles of large effect with the ultimate
goal of integrating that knowledge with evolutionary
ecology. In Heliconius butterflies, wing coloration
involved in geographically variable and evolutionarily
dynamic mimicry complexes have been mapped to specific genomic regions and are now intensively studied
(e.g. Baxter et al. 2010). In the Glanville fritillary, individual-based modelling strongly suggests that the ecological metapopulation dynamics and the dynamics of
Pgi allele frequency are coupled (Zheng et al. 2009). It
now appears that allelic variation in both Pgi and Sdhd
are associated with variation in life history and metabolism phenotypes, vary in frequency with population
age, and may even affect changes in population size. In
addition, there is likely to be allelic variation in other
genes having large trans-acting effects on expression
variation, of which the Ace gene is one good candidate.
How is genetic variation with fitness consequences
maintained in the metapopulation? Spatially and ⁄ or
temporally varying selection (Levene 1953; Gillespie
1991) as well as heterozygote advantage may all play a
role, perhaps in concert with pleiotropic, epistatic and
sex-dependent effects. While theoretical studies have
examined these effects, along with G · E interactions in
the maintenance of genetic variation (e.g. Barton &
Whitlock 1997; Turelli & Barton 2004), we know little of
their combined effects in the metapopulation context
(Whitlock 2004). Empirical data on Pgi in the Glanville
fritillary points to strong heterozygote advantage (Haag
! 2011 Blackwell Publishing Ltd
F U N C T I O N A L G E N O M I C S O F M E T A P O P U L A T I O N D Y N A M I C S 1825
et al. 2005; Orsini et al. 2009), which appears to interact
with temperature and habitat type (Hanski & Saccheri
2006; Ovaskainen et al. 2008b; Saastamoinen & Hanski
2008; Niitepõld 2010) and have sex specific effects on
dispersal (Hanski et al. 2004; Niitepõld et al. in press).
The Sdhd polymorphism reported here may also involve
heterozygote advantage with environmental (Table 1)
and genomic (Fig. 5) interactions. In addition to these
different forms of balancing selection, genetic variation
may be maintained by the coupling between organismal-level demographic and microevolutionary dynamics, of which Pgi in the Glanville fritillary provides a
prime example (Zheng et al. 2009). Thus, our findings
suggest that integrating these various mechanisms into
a unified analysis of the maintenance of variation is
warranted.
To summarize, we have combined functional genomics with a long-term ecological study to gain a more
mechanistic understanding of life history variation
affecting ecological and evolutionary dynamics. First,
the long-term ecological study has allowed the identification of groups of populations that differ in their
demographic history. Second, population age, which
does not correlate with any morphological traits of individuals, was used as a ‘treatment’ in the functional
genomics experiment. Third, gene expression differences and allelic polymorphisms were associated, across
independent samples, with life history traits and population dynamics. These results demonstrate that integrating functional genomics with population ecology is
a powerful way to obtain mechanistic insights into life
history ecology and evolution (Ronce & Olivieri 2004)
and to identify new candidate genes affecting ecoevolutionary dynamics (Saccheri & Hanski 2006). Our
findings have significance for conservation biology,
because the life history traits we have studied affect
metapopulation persistence in fragmented landscapes
(Hanski & Ovaskainen 2000).
Acknowledgements
We thank P. Auvinen at the DNA sequencing and Genomics
laboratory, Institute of Biotechnology, University of Helsinki
for his advice and help with array scanning, D. Crawford for
his advice on microarray experimental design, M. Saastamoinen for her help with the butterfly rearing, numerous people
for comments on earlier versions of this manuscript (A. Meyer,
K. Elmer, K. Bargum, R. Schilder, A. Read, C. Grozinger, S.
Schaeffer, A. Zera), and C. Brenner, D. Matasic, and S. Wherry
for their assistance with DNA isolation and cloning, with special appreciation to A.D. Jones for analytical measurement of
lipid hormones. Funding for this work was provided by the
US National Science Foundation (grants EF-0412651 and IOS0950416), the Academy of Finland [grant numbers 131155,
38604 and 44887 (Finnish Centre of Excellence Programmes
! 2011 Blackwell Publishing Ltd
2000–2005, 2006–2011)], and AdG number 232826 from the
European Research Council.
Conflicts of interest
The authors have no conflict of interest to declare and note that
the funders of this research had no role in the study design,
data collection and analysis, decision to publish or preparation
of the manuscript.
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Supporting information
Additional supporting information may be found in the online
version of this article.
Methods S1 Analysis of polymorphism and population subdivision.
Methods S2 Enrichment analysis.
Methods S3 Quantitative PCR.
Table S1 Samples sizes and details table.
Table S2 Distribution of coefficients of variation among datasets.
Table S3 Abdomen enrichment analysis results.
Table S4 Abdomen enrichment analysis data.
Table S5 Quantitative PCR analyses for individual genes in
abdomens.
Table S6 Top genes by tissue.
Table S7 Analyses of oogenesis phenotypes.
Table S8 Peak flight metabolic rate in microarray sample.
Table S9 Thorax enrichment analysis results.
Table S10 Thorax enrichment analysis data.
1828 C . W . W H E A T E T A L .
Table S11 Peak and total flight metabolic rate.
Fig. S4 Respiratory exchange ratio during flight.
Table S12 Sdhd allele frequencies in new and old populations.
Fig. S5 Annotation of Assembly 2.0.
Fig. S1 SNP analysis of genetic relatedness.
Please note: Wiley-Blackwell are not responsible for the content
or functionality of any supporting information supplied by the
authors. Any queries (other than missing material) should be
directed to the corresponding author for the article.
Fig. S2 Abdomen gene expression by three fixed factors.
Fig. S3 Sdhd expression level by allele.
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