![Comparison between lepidopteran cadherin-like proteins and the Aedes aegypti cadherin studied here. Cadherin-like proteins are constituted of different domains: SD, subdomain; MPED, membrane-proximal extracellular domain; TM, transmembrane domain; CYTO, cytoplasmic domain. Only features present in the mature form of the protein are outlined here. Known and putative Cry binding sites characterized in lepidopterans are indicated by parentheses. B. mori, Bombyx mori; H. armigera, Helicoverpa armigera; H. virescens, Heliothis virescens; M. sexta, Manduca sexta; Ae. aegypti, Aedes aegypti. Adapted from Figure 10 in [27].](https://www.researchgate.net/publication/40024023/figure/fig1/AS:214084789248003@1428053199871/Comparison-between-lepidopteran-cadherin-like-proteins-and-the-Aedes-aegypti-cadherin_Q320.jpg)
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BMC Genomics
Open Access
Research article
Candidate genes revealed by a genome scan for mosquito
resistance to a bacterial insecticide: sequence and gene expression
variations
Aurélie Bonin*†, Margot Paris†, Guillaume Tetreau†, Jean-Philippe David and
Laurence Després
Address: Laboratoire d'Ecologie Alpine, CNRS-UMR 5553, Université Joseph Fourier, BP 53, 38041 Grenoble cedex 09, France
Email: Aurélie Bonin* - abonin@indiana.edu; Margot Paris - margot.paris@e.ujf-grenoble.fr; Guillaume Tetreau - guillaume.tetreau@e.ujf-
grenoble.fr; Jean-Philippe David - jean-philippe.david@ujf-grenoble.fr; Laurence Després - laurence.despres@ujf-grenoble.fr
* Corresponding author †Equal contributors
Abstract
Background: Genome scans are becoming an increasingly popular approach to study the genetic
basis of adaptation and speciation, but on their own, they are often helpless at identifying the
specific gene(s) or mutation(s) targeted by selection. This shortcoming is hopefully bound to
disappear in the near future, thanks to the wealth of new genomic resources that are currently
being developed for many species. In this article, we provide a foretaste of this exciting new era by
conducting a genome scan in the mosquito Aedes aegypti with the aim to look for candidate genes
involved in resistance to Bacillus thuringiensis subsp. israelensis (Bti) insecticidal toxins.
Results: The genome of a Bti-resistant and a Bti-susceptible strains was surveyed using about 500
MITE-based molecular markers, and the loci showing the highest inter-strain genetic differentiation
were sequenced and mapped on the Aedes aegypti genome sequence. Several good candidate genes
for Bti-resistance were identified in the vicinity of these highly differentiated markers. Two of them,
coding for a cadherin and a leucine aminopeptidase, were further examined at the sequence and
gene expression levels. In the resistant strain, the cadherin gene displayed patterns of nucleotide
polymorphisms consistent with the action of positive selection (e.g. an excess of high compared to
intermediate frequency mutations), as well as a significant under-expression compared to the
susceptible strain.
Conclusion: Both sequence and gene expression analyses agree to suggest a role for positive
selection in the evolution of this cadherin gene in the resistant strain. However, it is unlikely that
resistance to Bti is conferred by this gene alone, and further investigation will be needed to
characterize other genes significantly associated with Bti resistance in Ae. aegypti. Beyond these
results, this article illustrates how genome scans can build on the body of new genomic information
(here, full genome sequence and MITE characterization) to finally hold their promises and help
pinpoint candidate genes for adaptation and speciation.
Published: 21 November 2009
BMC Genomics 2009, 10:551 doi:10.1186/1471-2164-10-551
Received: 25 July 2009
Accepted: 21 November 2009
This article is available from: http://www.biomedcentral.com/1471-2164/10/551
© 2009 Bonin et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
BMC Genomics 2009, 10:551 http://www.biomedcentral.com/1471-2164/10/551
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Background
In the past few years, evolutionary biologists have increas-
ingly bet on population genomics approaches to study the
genetic basis of adaptation and speciation. Genome scans
have flourished in the literature, providing valuable
insight into the genetics of local adaptation [1,2], sympat-
ric speciation [3], host race or ecotype differentiation [4-
7], and response to climate change or exotic invasions
[8,9], among others. In most cases, however, population
genomics alone fell short of pinpointing the specific
gene(s) or mutation(s) targeted by selection during the
adaptation or speciation process [10,11].
One of the main reasons for this recurring setback is the
present lack of genomic resources for most examined spe-
cies. For example, due to the absence of more powerful
alternatives, many population genomics studies rely on
genetic markers such as AFLPs (Amplified Fragments
Length Polymorphisms) [11,12], which can generally be
obtained easily for any organism. Unfortunately, such
markers present the double disadvantage of being anony-
mous and of falling predominantly in non-coding regions
of the genome, i.e. far from potential candidate regions
for adaptation and speciation [10]. Moreover, when
genomic sequences are scarce or poorly annotated, identi-
fying candidate genes in the vicinity of markers showing a
selection signature represents a daunting task that few
researchers have undertaken so far (but see [13]). Yet, we
predict that these limitations are bound to disappear in
the near future, especially with the increasing use of next-
generation sequencing technologies. In this article, we
aim at providing a foretaste of this exciting new era by
illustrating how new genomic tools can help unravel the
genetic basis of mosquito resistance to Bacillus thuringien-
sis subsp. israelensis (Bti) insecticidal toxins.
Considered as a safe alternative to chemical insecticides,
the bio-insecticide Bti is widely used worldwide for mos-
quito control [14]. Bti toxicity is mainly conferred by three
Cry toxins (Cry4A, Cry4B and Cry11A) and one Cyt toxin
(Cyt1A), which aggregate in a proteic crystal produced
during sporulation of the bacteria [15]. Bti is usually
sprayed in mosquito breeding sites as a mixture of spores
and toxins, which is ingested together with organic detri-
tus by developing larvae. In the larval midgut, Bti toxins
are first activated by protease/trypsin-like enzymes or ami-
nopeptidases [16]. Then, they bind to specific receptors of
the midgut cells (Cry toxins), or directly interact with the
cell membrane (Cyt1A toxin), ultimately causing pore for-
mation and cell lysis [16]. Cy1A is also known to act syn-
ergistically with Cry toxins, increasing the overall toxicity
of the Bti mixture [17,18]. Due to the complexity of Bti
toxicity mechanisms, some have argued that resistance to
Bti would likely require adaptive mutations in several
genes [19,20]. This argument is reinforced by the fact that
only a handful of studies have observed evidence of labo-
ratory or natural resistance to Bti in mosquito [19,21-23].
Because of the probable multilocus nature of Bti resist-
ance, population genomics appears to be an approach of
choice to identify genes involved in this process.
Here, we describe the application of population genomics
to the search for candidate genes for resistance to a toxic
leaf litter containing Bti spores. With the mosquito Aedes
aegypti as a model, we first conducted a genome scan rely-
ing on about 500 MITE (Miniature Inverted-repeat Trans-
posable Element)-derived markers expected to occur
frequently in gene-rich regions [24,25]. By combining the
results of this genome scan with data from the publicly
available genome sequence of Ae. aegypti, we were then
able to localize two good candidate genes for Bti resist-
ance. Finally, these two genes were further analyzed at the
sequence and gene expression levels in order to determine
if selection was indeed a driving force in their evolution.
Results
Genome scan and identification of outlier loci presumably
influenced by selection
Our search for candidate genes linked to Bti resistance was
conducted in two Aedes aegypti strains differing drastically
in their susceptibility to Bti as well as to individual Bti tox-
ins (see the Methods part for resistance ratios). The
genome of Ae. aegypti was screened using a variant of the
DArT (Diversity Arrays Technology) procedure, where
motifs of a particular MITE (Miniature Inverted-repeat
Transposable Element) family called Pony served as
primer anchors for PCR amplification. In total, 476 bial-
lelic dominant markers were surveyed for 29 individuals
in each mosquito strain, revealing a particularly high
genetic differentiation between strains (mean Fst =
0.556). This strong genetic structure was not surprising
given the history of the two strains, and in particular the
recent bottleneck experienced by the resistant strain
(Additional file 1). However, high neutral Fst values are
expected to reduce the power of methods revealing outlier
loci potentially under selection on the basis of an atypi-
cally high genetic differentiation. For example, the appli-
cation of the program Dfdist [26] to our data detected
only one locus departing from neutral expectations for α
= 1%, because the neutral envelope included almost the
entire range of possible differentiation values (Additional
file 2). As a result, we adopted a different strategy and
retained as outliers those loci for which alternative pheno-
types (fragment presence/absence) were fixed or nearly
fixed in each strain. A total of 70 such markers were
sequenced for further analyses.
Outlier sequencing and localization in the genome
Among the outlier sequences obtained, one pair differed
only by a gap and another one by only a mutation, result-
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ing in a redundancy rate of 2.86%. After trimming the
primer sequence and the Pony motif, the 68 unique
marker sequences (GenBank accession no. FJ231034-
FJ231090; sequences shorter than 50 bp could not be
deposited) had an average size of 185.2 bp (range 16-868
bp). Of these unique sequences, 41 could be assigned to a
unique position in the Aedes aegypti genome, and all but
two of these positions were found on different supercon-
tigs (Additional file 3). Six sequences were situated on the
same supercontig as a candidate gene for Bti resistance
(DArT_102, DArT_318, DArT_400, DArT_415, DArT_432
and DArT_467), and two of them (DArT_432 and
DArT_467) co-localized with the same gene (a cadherin).
It had to be noted that physical distances between candi-
date genes and outlier markers situated on the same
supercontig were considerable, ranging from 97907 bp
(cadherin and DArT_467) to more than 300 Mbp (glyco-
syltransferase and DArT_415). According to these results,
the cadherin gene (CAD, VectorBase Gene ID
AAEL001196) turned out to be a serious candidate for Bti
resistance because two outlier markers pointed towards it,
one of them at the shortest distance recorded in this study.
This gene, which codes for a possible toxin-binding recep-
tor [27], was thus selected for further investigation at the
sequence and expression levels. We also focused on the
leucine aminopeptidase gene (LAP, VectorBase Gene ID
AAEL001649) because of its potential implication in Bti
toxin activation [28].
Candidate gene sequence analysis
A total of 1,467 bp and 1,657 bp were sequenced for the
CAD and LAP genes, respectively. All sequences were
deposited into GenBank, with accession numbers
GU066340 to GU066385. As shown in Table 1, the two
candidate genes shared similarities: both had a higher
genetic diversity in the susceptible than in the resistant
strain even if more individuals were sequenced in this lat-
ter. This observation was consistent with the fact that both
strains are separated by 18 generations of selection, and
that the resistant strain experienced a strong bottleneck at
generation 10 (Additional file 1). Both genes also showed
similar numbers of haplotypes in each strain (13 and 12
haplotypes in the susceptible strain for CAD and LAP,
respectively; and 5 haplotypes in the resistant strain for
both genes). On the other hand, the two candidate genes
differed by their overall level of genetic diversity and inter-
strain differentiation. For example, the Fst value was 0.186
only for CAD vs. 0.321 for LAP. Likewise, the nucleotide
diversity estimated for LAP in the susceptible strain was
about four times lower than that estimated for CAD, this
ratio reaching 17 when considering the resistant strain.
However, it has to be noted that polymorphisms were not
uniformly distributed along the CAD gene sequence,
some regions showing globally more variation (e.g. sub-
domain SD4 and interdomain SD4-SD5 of the protein;
Figure 1).
These patterns of genetic diversity translated differently at
the codon level. We found three non-synonymous sites in
the CAD sequence, of which none was diagnostic of one
strain in particular, whereas for the LAP sequence, only
two of nine non-synonymous mutations were present in
the resistant strain. Six non-synonymous sites, among
which five could only be found in the susceptible strain,
were noticeably situated in 5' and 3' untranslated regions
and could thus affect transcript stability and/or transla-
tion.
Finally, the examination of haplotype repartition between
strains allowed this picture to be completed. For the CAD
gene, one haplotype present in all 12 sampled resistant
individuals (18 haplotypes out of 24) existed only in 2
susceptible individuals (2 haplotypes out of 20). For the
LAP gene, the most frequent haplotype in the resistant
strain (11 haplotypes out of 24) was absent from the sus-
ceptible strain.
Neutrality tests
For both candidate genes, we tested for deviation from
neutral evolution in the resistant strain by applying differ-
ent neutrality tests to the observed polymorphism data
(Table 2). Tajima's D and Fay and Wu's H highlight a skew
in the frequency distribution of variants, H giving more
weight to high-frequency polymorphisms, whereas Fu
and Li's D* and F* detect a discrepancy between either the
total number of mutations (D*) or the average number of
differences between two sequences (F*) and the number
of singletons. As a result, each one of these tests is based
on different diversity parameters and gives different infor-
mation on the type of selection presumably in action. In
this study, every test except Fay and Wu's H gave positive
values for both genes (Table 2), which is consistent with
the recent bottleneck experienced by the resistant strain.
None of the tests was significant for LAP, whereas D*, F*
and H remained significant for CAD even when account-
ing for the particular demographic history of the strain
(Table 2). The positive values of D* and F* suggested a
deficiency of recent (i.e. rare) mutations, whereas the neg-
ative H indicated an excess of high compared to interme-
diate frequency mutations.
Gene expression analyses
Real-time reverse-transcription PCR (RT-PCR) analyses
revealed that levels of gene expression were reduced in the
resistant strain compared to the susceptible strain for both
candidate genes (2.17-fold and 1.68-fold under-expres-
sion for the CAD and the LAP genes, respectively; Figure
2). Non-parametric Mann-Whitney tests indicated that
reduction in expression levels was significant for both
genes (p = 0.017 and p = 0.002 for the CAD and LAP genes,
respectively). However, only the expression fold change
observed for the CAD gene (2.17) was higher than the
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two-fold change conservatively used as a significant
threshold in expression studies [29].
Discussion
A MITE-based genome scan to search for candidate genes
The goal of this study was to identify genes conferring
resistance to the bio-insecticide Bacillus thuringiensis
subsp. israelensis (Bti) in an Aedes aegypti mosquito strain
selected for several generations with a toxic leaf litter con-
taining Bti spores. The genetic basis of Bti resistance is
likely to be multigenic [19,20], and in Ae. aegypti, we listed
as many as 160 serious candidates by virtue of their
known function and/or proven association with Bti resist-
ance in other species (e.g. [20,30,31]). As a result, we
chose to tackle this study by adopting a population
genomics approach. The underlying idea was to examine
many MITE-derived DArT markers scattered in the
genome to get an accurate estimate of the overall back-
ground (i.e. neutral) genetic differentiation between the
selected (Bti-resistant) and control (Bti-susceptible)
strains. This in turn allowed detecting markers with an
atypically high inter-strain genetic differentiation, and
thus possibly linked to a gene under positive selection due
to Bti resistance. Our genome scan revealed an overall
high level of neutral genetic differentiation between the
two strains (mean Fst = 0.556), which is not unexpected
in the light of the history of the resistant strain, and espe-
cially of the bottleneck it experienced at generation 10
Table 1: Measures of genetic diversity and divergence obtained for the two candidate genes
Gene/Domain Strain Fragment size (bp) NHap S SiNS Hd WkKFst
Cadherin
(CAD)
Whole
sequence*
Susceptible
Resistant
1467 20
24
13
5
67
65
0
1
3
3
0.932
0.435
0.023
0.017
0.013
0.012
32.363
25.033
35.254 0.186
SD2 Susceptible
Resistant
75 22
24
3
2
2
2
0
0
0
0
0.567
0.391
0.013
0.010
0.007
0.007
0.961
0.783
1.136 0.233
SD2-SD3 Susceptible
Resistant
69 22
24
3
2
2
1
1
0
0
0
0.558
0.391
0.009
0.006
0.008
0.004
0.610
0.391
0.568 0.119
SD3 Susceptible
Resistant
268 22
24
3
2
12
8
4
0
1
0
0.541
0.391
0.016
0.012
0.012
0.008
4.264
3.130
4.705 0.214
SD3-SD4 Susceptible
Resistant
77 22
24
2
2
3
3
0
0
2
1
0.485
0.391
0.019
0.015
0.011
0.010
1.455
1.174
1.705 0.229
SD4 Susceptible
Resistant
279 22
24
4
2
18
14
4
0
0
0
0.571
0.391
0.026
0.020
0.018
0.013
7.273
5.478
8.080 0.211
SD4-SD5 Susceptible
Resistant
69 22
24
3
2
6
5
1
0
0
0
0.541
0.391
0.038
0.028
0.024
0.019
2.602
1.957
2.795 0.185
SD5 Susceptible
Resistant
246 10
22
4
4
17
14
1
0
1
1
0.733
0.398
0.034
0.020
0.024
0.016
8.422
5.039
8.164 0.176
SD5-SD6 Susceptible
Resistant
63 10
22
2
2
1
1
0
0
0
0
0.533
0.368
0.008
0.006
0.006
0.004
0.533
0.368
0.555 0.187
SD6 Susceptible
Resistant
279 10
22
2
2
15
15
0
0
1
1
0.533
0.368
0.029
0.020
0.019
0.015
8.000
5.519
8.318 0.187
SUP6 Susceptible
Resistant
42 10
22
2
2
1
1
0
0
0
0
0.533
0.368
0.013
0.009
0.008
0.007
0.533
0.368
0.555 0.187
Leucine aminopeptidase (LAP) Susceptible
Resistant
1657 22
24
12
5
29
6
0
0
9
2
0.900
0.688
0.006
0.001
0.005
0.001
10.069
2.025
8.909 0.321
N, number of alleles sampled; Hap, number of different haplotypes; S, number of segregating sites; Si, number of singletons; NS, number of non-
synonymous mutations; Hd, haplotype diversity; , nucleotide diversity (per site); W, Watterson's mutation parameter θ estimated from S (per site);
k; average number of nucleotide differences within strain; K, average number of nucleotide differences between strains; Fst, genetic differentiation
between strains.
* Some measures (e.g. S and NS) are different between the whole sequence and the total of all sequences because of a different number of sampled
haplotypes.
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(see Additional File 1). Yet, the Fst value obtained
between the same two mosquito strains using AFLP mark-
ers is substantially lower (mean Fst = 0.114; Paris, pers.
comm.). AFLP and DArT markers are biallelic, dominant
and, to a certain extent, randomly distributed in the
genome so they should provide similar genetic differenti-
ation estimates. However, the DArT markers developed
for the purpose of this study are intimately associated with
a specific family of MITEs called Pony, whose characteris-
tics might explain the discrepancy between the two Fst
measures. Pony elements constitute about 1.1% of the
genome of Ae. aegypti [25], and although their transposi-
tion mechanism is still unclear, it could be triggered by
unfavorable environmental conditions as was shown for
other MITEs in plants for example [24,32,33]. Several
authors have even underlined the potential contribution
of MITEs to rapid adaptations and, ultimately, genome
evolution [24,34]. One can thus speculate that the envi-
ronmental stress imposed by toxic leaf litter selection
stimulated Pony transpositions, hence inflating estimates
of inter-strain genetic differentiation as measured by our
Pony-associated markers. Like many MITEs, Pony motifs
are also known to frequently occur in the non-coding
regions of genes [25]. Because of this last characteristic of
Pony elements and of their high mutational potential pos-
sibly enhanced by stress, Pony-based DArTs are ideal ran-
dom markers to explore the genome of Ae. aegypti and
search for genes conferring resistance to Bti.
DArT marker sequences and identification of candidate
genes
We decided to sequence the MITE-based DArT markers
showing the highest inter-strain differentiation (outliers),
as those are the most likely to be linked to genes confer-
ring resistance to Bti. Of the 70 sequences obtained, 41
(68.6%) matched to unique locations in the genome of
Comparison between lepidopteran cadherin-like proteins and the Aedes aegypti cadherin studied hereFigure 1
Comparison between lepidopteran cadherin-like pro-
teins and the Aedes aegypti cadherin studied here.
Cadherin-like proteins are constituted of different domains:
SD, subdomain; MPED, membrane-proximal extracellular
domain; TM, transmembrane domain; CYTO, cytoplasmic
domain. Only features present in the mature form of the
protein are outlined here. Known and putative Cry binding
sites characterized in lepidopterans are indicated by paren-
theses. B. mori, Bombyx mori; H. armigera, Helicoverpa armig-
era; H. virescens, Heliothis virescens; M. sexta, Manduca sexta;
Ae. aegypti, Aedes aegypti. Adapted from Figure 10 in [27].
Table 2: Results of the neutrality tests for the resistant strain
Neutrality test Value Significance according to coalescent sim-
ulations based on a large constant popu-
lation sizea
Significance according to coalescent simu-
lations based on the known demographic
history of the resistant strainb
Cadherin (CAD)
Tajima's D1.717 p < 0.01 n.s.
Fu and Li's D* 1.698 p < 0.001 p < 0.01
Fu and Li's F* 2.000 p < 0.001 p < 0.05
Fay and Wu's H-50.370 p < 0.001 p < 0.01
Leucine aminopeptidase (LAP)
Tajima's D0.788 n.s. n.s.
Fu and Li's D* 1.233 n.s. n.s.
Fu and Li's F* 1.280 n.s. n.s.
Fay and Wu's H1.181 n.s. n.s.
a, performed following the method of Hudson [70] implemented in DnaSP; b, performed using the program ms [68].
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Ae. aegypti, while the rest consisted of repeated sequences.
As less than 40% of the genome of this species is consti-
tuted of single or low copy sequences [35], this result con-
firms the fact that MITE-based DArTs tend to fall near low-
copy, possibly coding, sequences. The 41 genomic loca-
tions involved were all but two situated on different
supercontigs of the Ae. aegypti full genome sequence, sug-
gesting that there was no real bias in the distribution of
our markers in the genome, at least for highly differenti-
ated ones. Unfortunately, the Ae. aegypti genome has a
weak gene density [35], and most of our outlier sequences
(85.4%) fell in supercontigs with few or no putative
genes. Furthermore, we recently performed a differential
transcriptome analysis of Ae. aegypti larvae exposed to dif-
ferent xenobiotics. This study, based on the sequencing of
millions of cDNA tags using next-generation sequencing,
detected more than 2000 loci situated outside predicted
genes and showing a significant transcription signal
(David, pers. comm.). There is thus an obvious need for a
better gene annotation in the Ae. aegypti genome, and one
efficient strategy to tackle this task would be to concen-
trate the effort on regions of interest, like those showing
signatures of selection.
The selection with toxic leaf litter was a recent event in the
history of the resistant strain so linkage disequilibrium is
probably extensive in the vicinity of selected genes [36]. In
those conditions, detecting selection signatures in a
genome is somewhat easier even with a relatively small
marker density [37], but on the other hand, the existence
of large haplotype blocks makes it more difficult to pin-
point the exact gene(s) under selection. In this study, the
distance between outlier markers and candidate genes was
considerable (between 100 and 3,000 Kb) and might
exceed the window of linkage disequilibrium around
selected genes. In other words, we might have overlooked
closer genes genuinely responsible for the signatures of
selection, but not already annotated or absent from our
list of candidates.
The cadherin and leucine aminopeptidase as candidate
genes
Among the five candidate genes discovered in the proxim-
ity of outlier markers, we decided to examine further the
cadherin (CAD) and leucine aminopeptidase (LAP) at the
sequence and expression levels, to confirm or infirm the
selection footprint. This step was all the more crucial since
selection but also particular demographic histories can
generate an atypically high genetic differentiation [38,39].
Neutrality tests are often used to verify the influence of
selection in intraspecific sequence data [40,41]. However,
they have often been criticized for their lack of power and
their sensitivity to demographic events like bottlenecks or
population expansions which can mimic selective effects
[38,42]. In this study, this possible bias was overcome by
performing tailor-made coalescence simulations based on
the known demographic history of the resistant strain, in
order to assess the significance of the tests. No firm evi-
dence of selection was found at the nucleotide or expres-
sion level for the LAP gene, which rules out its implication
in Bti resistance, at least as a gene with major effects. For
the CAD gene, Fu and Li's D* and F* tests highlighted a
deficit in rare mutations, which is usually the trademark
of balancing selection [43]. Fay and Wu's H statistics indi-
cated an excess of high compared to intermediate fre-
quency mutations, which on the contrary suggests the
spread of an advantageous mutation at a linked site (i.e.
positive selection) [44]. Although those conflicting results
might be hard to interpret at first glance, one has to keep
in mind that the selection with the toxic leaf litter started
only 20 generations ago. In addition, the resistant strain
experienced a recent bottleneck which probably further
eliminated low frequency variants. New mutations cer-
tainly have not had time yet to appear in the population,
which would explain the positive D* and F* values. For
the same reason, Tajima's D can be transiently positive
right after a bottleneck [45]. As for the H statistics, it is not
as sensitive to the loss of rare variants, so it is presumably
more reliable here and we can reasonable think that the
CAD gene shows genuine signs of positive selection. This
evidence is further reinforced by the significant under-
expression of this gene in the resistant strain. Cadherins
are indeed known to bind to Cry toxins in other insect
species [27] and to be involved in many cases of Cry resist-
ance in insects (e.g. [46-48]). Here, under-expression of
the cadherin gene could reduce the number of a certain
type of Cry receptors and thus hinder pore forming, ulti-
Results of the expression analyses for the two candidate genesFigure 2
Results of the expression analyses for the two candi-
date genes. This figure illustrates, for each candidate gene,
the mean gene expression in the resistant strain relative to
that in the susceptible strain.
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mately limiting susceptibility to Bti. Similarly, resistance
to the toxin Cry1Ac has been shown to be linked to
reduced levels of membrane receptors in the cotton pest
Heliothis virescens [49], or in the cabbage moth Plutella
xylostella [50]. Nevertheless, cadherins are usually specific
to one particular Cry toxin [27], and it is unlikely that the
CAD gene alone is responsible for resistance to Bti which
is a mixture of several Cry toxins. One can thus hypothe-
size that this gene's effects supplement those of one or sev-
eral other genes that remain to be identified.
Conclusion
Beyond their implications for the understanding of the
genetic mechanisms of Bti mosquito resistance, these
results illustrate how genome scans can build on the body
of new genomic information (here, full genome sequence
and MITE characterization) to finally hold their promises
and help pinpoint candidate genes for adaptation and
speciation [10]. In the near future, a wealth of genomics
tools will be available for a much wider range of species,
mostly thanks to the rapid development of next-genera-
tion sequencing technologies [51]. We predict this new
knowledge will boost in many respects the use of popula-
tion genomics for the study of the genetic bases of adapta-
tion and speciation. Several limitations of current genome
scans will certainly be soon overcome, with for example
(1) the development of new genetic markers allowing
screening the genome more finely (e.g. [52]) or specifi-
cally targeting coding regions (e.g. [53]); (2) an easier
access to outlier sequences as well as full genomic
sequences serving as references to locate outlier loci and
identify nearby candidate genes; and (3) a better gene
annotation. In short, population genomics will at last
have the means to meet our expectations when it comes to
identify genes under natural or artificial selection.
Methods
Biological material
Two Aedes aegypti laboratory strains were compared for the
purpose of this study: the Bora Bora reference strain,
known to be susceptible to most insecticides, and a strain
artificially selected for resistance to a decomposed tree leaf
litter showing a high toxicity for mosquito larvae. This leaf
litter had been collected in a mosquito pond in Eastern
France three months after treatment for mosquito control,
and has been proved to contain Bacillus thuringiensis
subsp. israelensis (Bti) spores from commercial origin [54].
The use of this Bti-contaminated leaf litter in the selection
experiments allowed mimicking the evolution of resist-
ance to Bti in a situation close to field conditions. The sus-
ceptible and resistant strains were separated by 18
generations of selection (and by 20 generations in total),
with a strong bottleneck at generation 10 (see Additional
File 1 for more details on the demographic history of the
resistant strain). The selection experiment and the bio-
assays implemented to monitor the evolution of resist-
ance are described in [55]. At each generation, the lethal
dose for 50% of the individuals after a 24 h-exposure (24
h-LD50) was determined for each strain using the Probit
software [56]. After 18 generations of selection, the resist-
ance ratios RR of the resistant strain (i.e., the ratio
between the 24 h-LD50 values for the resistant and the
susceptible strains, respectively) were 3.4-fold, 30.2-fold,
13.7-fold, 6.3-fold and 3-fold for the toxic leaf litter,
Cry4A, Cry4B, Cry11A and Cyt1A toxins, respectively.
DNA extraction and genome scan
The genomic DNA used for all subsequent molecular
work was extracted from fresh fourth-instar mosquito lar-
vae using the Qiagen DNeasy Tissue Kit and protocol
(Qiagen). Prior to extraction, the larvae midgut was
removed carefully to avoid bacterial contamination.
The classical protocol of the Diversity Array Technology
(DArT) [57] was slightly modified so as to provide hun-
dreds of good-quality markers scattered in the genome of
Ae. aegypti and possibly associated to gene-rich regions
[55]. Briefly, in a first step, genomic DNA was digested
with restriction enzyme Bsp1286I and a specific adaptor
was ligated to compatible ends. Restriction fragments
including a particular Ae. aegypti MITE called Pony were
PCR-amplified using a primer annealing to the adaptor
sequence and a primer complementary to a conserved
motif of the Pony element. PCR products obtained for all
individuals of the two strains were pooled together and
cloned to construct a DArT library containing a total of
6144 MITE-based clones. In a second step, a labelled tar-
get produced for each Ae. aegypti individual as described
in the first step was hybridized to the library fragments
spotted on a glass slide in order to reveal the polymorphic
ones. Details of the protocol, in particular the adaptor and
primer sequences used and the reproducibility rates, can
be found in [55].
Identification and sequencing of outlier loci potentially
under selection
For each DArT marker obtained, allelic frequencies were
estimated with the Bayesian method with non-uniform
prior distribution [58] implemented in AFLP-SURV 1.0
[59]. Among those markers, we tracked those for which
alternative phenotypes (fragment presence/absence) were
fixed or nearly fixed in the two strains (for example, frag-
ment present/absent for all individuals or all individuals
except one). Our assumption was that such a pattern of
extreme inter-strain genetic differentiation could be
explained by the spread, in the resistant strain, of an
advantageous allele initially present at low or intermedi-
ate frequency in the susceptible strain (standing varia-
tion), from which it is eventually purged by genetic drift
and/or because it is slightly deleterious.
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For 70 such markers, bacterial cultures were sent to
Genome Express® http://www.genome-express.com for
insert amplification and sequencing with M13 forward
and M13 reverse primers. Raw sequence files were edited
with BioEdit 7.0.9 [60] and purged from Pony and primer
sequences. The obtained sequenced were blasted against
the full genomic sequence of Ae. aegypti (consisting of
4758 supercontigs and available at http://aaegypti.vector
base.org/GetData/Downloads?type=Genome).
Identification of candidate genes
Although mechanisms of resistance to Bti are still
unknown in dipterans, resistances to several Cry toxins
have been intensively studied, especially in lepidopteran
pests resistant to transgenic crops expressing Bacillus thur-
ingiensis Cry toxins genes [20,30]. Because these toxins
share similar three-dimensional structures, similar modes
of action and resistance mechanisms can be expected
between lepidopteran and dipteran insects [16,61]. We
therefore considered as candidate genes for Bti resistance
those belonging to families previously proved to be
involved in Cry resistance [20,30,31]. To this list, we
added genes potentially implicated in activation of Bti
toxins (aminopeptidases, e.g. [28]; and trypsins and chy-
motrypsins, e.g. [62]), in toxin binding (alkaline phos-
phatases, e.g. [49,63]; aminopeptidases, e.g. [64];
cadherins, e.g. [48]; galactosidases and glycosyltrans-
ferases, e.g. [20,65]); or immune defense (mitogen-acti-
vated protein kinases, e.g. [31]). A keyword search was
conducted in the VectorBase database http://aaegypti.vec
torbase.org/index.php and a total of 160 candidates
located on 98 different supercontigs were identified out of
the 15,419 putative genes (16789 transcripts in total) ref-
erenced in the Aedes aegypti genome.
Cadherin (CAD) and leucine aminopeptidase (LAP) gene
sequencing
The complete genomic sequences of the CAD and LAP
genes (VectorBase Gene IDs AAEL001196 and
AAEL001649, respectively) were downloaded from the
VectorBase website to help design sequencing primers
(Additional file 4) with the software package Lasergene
7.2 (DNASTAR Inc.). The sequencing strategy for the CAD
gene targeted exon 5 and more specifically the membrane-
proximal subdomains (subdomains 4 to 6) of the protein
which are the preferential binding sites of Bti Cry toxins in
lepidopterans (Figure 1). For the LAP gene, three different
primer pairs were selected to amplify the two main exons.
PCR amplifications were conducted for each gene in a 25-
μl total volume with 2 mM MgCl2, 0.1 mM of each dNTP
(Roche), 0.2 μM of each primer, 5 μg of BSA, 0.6 U of
AmpliTaq Gold DNA polymerase (Applied Biosystems)
and 10-30 ng of DNA. The PCR program included an ini-
tial 10-min denaturation step at 95°C; 40 cycles of dena-
turation at 95°C for 45s, annealing at the optimal
temperature indicated in Additional file 4 for 45s and
elongation at 72°C for 60s; followed by a final extension
step at 72°C for 5 min. PCR products were purified with
the QIAquick PCR purification kit (Qiagen) and sequenc-
ing reactions were performed in both directions using the
amplification primers and the BigDye Terminator Cycle
Sequencing Kit 3.1 (Applied Biosystems), following the
manufacturer's indications. Fluorescently labelled
sequencing products were run on an ABI PRISM 3100 cap-
illary DNA sequencer (Applied Biosystems) and
sequences were analyzed with SeqMan Pro 7.1.0 (DNAS-
TAR Inc.). Overall, we obtained sequences for 11 and 12
individuals of the susceptible and resistant strains, respec-
tively.
Sequence analysis and neutrality tests
The software DnaSP 5.0 [66] was used to infer haplotype
phase and to assess a variety of genetic diversity and dif-
ferentiation parameters (e.g., nucleotide diversity p, hap-
lotype diversity Hd, number of segregating sites S, Fst, etc.)
for each gene. Several statistics were also calculated based
on the observed polymorphism data to test for deviation
from neutral evolution in the resistant strain, including
Tajima's D [67], Fu and Li's D* and F* [43], and Fay and
Wu's H [44]. To assess whether these statistics signifi-
cantly departed from a neutral scenario of evolution given
the known demographic history of the resistant strain, we
performed coalescent simulations using the program ms
[68]. This program generates random independent sam-
ples according to a Wright-Fisher neutral model allowing
population size changes in the past. For each gene, the
mutation rate μ was estimated from the per-locus muta-
tion parameter ? observed for the susceptible strain (θ =
4Neμ and Ne = 6000) and used as the starting value for the
simulations (that is, as the value at present). Then 1000
neutral samples consisting of 24 haplotypes were simu-
lated based on the known demographic history of the
resistant strain (Additional file 1).
RNA extraction and gene expression analyses
For the two candidate genes, real-time reverse-transcrip-
tion PCR (RT-PCR) analyses were performed on three bio-
logical replicates for each strain, with each replicate
consisting of 30 larvae reared in standard insectary condi-
tions up to the fourth-instar stage (5 days). Total RNAs
were extracted using TRIzol (Invitrogen) following the
manufacturer's instructions and their quality was assessed
with a 2100 Bioanalyzer (Agilent) after DNase I (Invitro-
gen) treatment. Four micrograms of total RNA were
digested with DNase I (Invitrogen) and then used for first-
strand cDNA synthesis with SuperScript III (Invitrogen)
reverse transcriptase and oligo-dT20 primers for 60 min at
50°C, according to the manufacturer's instructions. Real-
time RT-PCR reactions were performed on an iQ5 system
BMC Genomics 2009, 10:551 http://www.biomedcentral.com/1471-2164/10/551
Page 9 of 11
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(Bio-Rad) in a 25-μL total reaction volume with 0.3 μM of
each primer, 12.5 μL of iQ SYBR Green supermix (Bio-
Rad) and 5 μl of cDNA diluted 25 times. The real-time RT-
PCR program included an initial 3-min denaturation step
at 95°C and 40 cycles of denaturation at 95°C for 15s and
annealing for 30s at the optimal temperature indicated in
Additional file 4. For each gene, real-time RT-PCR effi-
ciency was estimated from a serial dilution of cDNA (5 to
500 times) and taken into account in the data analysis
performed with the ΔΔCT method [69]. Two housekeep-
ing genes encoding ribosomal protein L8 (RPL8, GenBank
accession number: DQ440262) and S7 (RPS7, GenBank
accession number: AY380336) were used for normaliza-
tion. Results were represented as mean expression ratios
between Bti-resistant and susceptible larvae (± SE).
Abbreviations
Bti: Bacillus thuringiensis subsp. israelensis; CAD: cadherin;
DArT: Diversity Arrays Technology; LAP: leucine ami-
nopeptidase; MITE: miniature inverted-repeat transposa-
ble element; RT-PCR: reverse-transcription PCR.
Authors' contributions
AB carried out the genome scan, analyzed the candidate
gene sequence data and drafted the manuscript. MP
worked on the outlier sequences, identified the two candi-
date genes, helped with the analyses and wrote substantial
parts of the paper. GT obtained and analyzed the candi-
date gene sequence and expression data and was involved
in the writing. JPD supervised the gene expression study
and helped draft the manuscript. LD conceived the overall
study, performed the demographic simulations and took
part to the data analysis and to the writing. All authors
read and approved the final manuscript.
Additional material
Acknowledgements
The authors would like to thank Sébastien Boyer for help with the insecti-
cide selection and Andrzej Kilian for guidance on the DArT technique. AB
and LD were funded by the Région Rhône-Alpes (grants #0501553401 and
#0501545401, respectively) and MP, JPD and LD benefited from a collabo-
rative grant attributed by the Démoustication Rhône-Alpes. GT was sup-
ported by the French Ministry of Research.
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