Download Chikungunya virus impacts the diversity of symbiotic bacteria in

Molecular Ecology (2012) 21, 2297–2309
doi: 10.1111/j.1365-294X.2012.05526.x
Chikungunya virus impacts the diversity of symbiotic
bacteria in mosquito vector
K A R I M A Z O U A C H E , * †§ R O R Y J . M I C H E L L A N D , * † A N N A - B E L L A F A I L L O U X , ‡
G E N E V I E V E L . G R U N D M A N N * † and P A T R I C K M A V I N G U I * †
*Université de Lyon, F-69622 Lyon, France, †UMR CNRS 5557 Ecologie Microbienne, Universite Lyon 1, INRA USC 1193,
VetAgro Sup, 43 Boulevard du 11 Novembre 1918, 69622 Villeurbanne cedex, F-69622 Lyon, France, ‡Department of Virology,
Institut Pasteur, Arboviruses and Insect Vectors Laboratory, 25-28 rue du Dr Roux, F-75724 Paris Cedex 15, France
Abstract
Mosquitoes transmit numerous arboviruses including dengue and chikungunya virus
(CHIKV). In recent years, mosquito species Aedes albopictus has expanded in the Indian
Ocean region and was the principal vector of chikungunya outbreaks in La Reunion and
neighbouring islands in 2005 and 2006. Vector-associated bacteria have recently been
found to interact with transmitted pathogens. For instance, Wolbachia modulates the
replication of viruses or parasites. However, there has been no systematic evaluation of
the diversity of the entire bacterial populations within mosquito individuals particularly
in relation to virus invasion. Here, we investigated the effect of CHIKV infection on the
whole bacterial community of Ae. albopictus. Taxonomic microarrays and quantitative
PCR showed that members of Alpha- and Gammaproteobacteria phyla, as well as
Bacteroidetes, responded to CHIKV infection. The abundance of bacteria from the
Enterobacteriaceae family increased with CHIKV infection, whereas the abundance of
known insect endosymbionts like Wolbachia and Blattabacterium decreased. Our results
clearly link the pathogen propagation with changes in the dynamics of the bacterial
community, suggesting that cooperation or competition occurs within the host, which
may in turn affect the mosquito traits like vector competence.
Keywords: Aedes albopictus, bacterial community, chikungunya, microarray, quantitative PCR,
Wolbachia
Received 7 September 2011; revision received 29 November 2011; accepted 7 December 2011
Introduction
Mosquitoes are major vectors of arboviruses. In the last
few decades, the mosquito species Aedes albopictus, originating from Southeast Asia (Smith, 1956), has expanded
over the world and has been responsible for many epidemic outbreaks of chikungunya and dengue (reviewed
in Paupy et al. 2009). For example, Ae. albopictus has
been identified as the principal vector of chikungunya
virus (CHIKV) in regions of the Indian Ocean (Delatte
et al. 2008a,b; Bagny et al. 2009a,b), Asia (Manimunda
et al. 2010), Africa (Peyrefitte et al. 2007, Peyrefitte et al.
2008) and Europe (Rezza et al. 2007). Massive chikunguCorrespondence: Patrick Mavingui, Fax: +33 4 72 43 11 43;
E-mail: [email protected]
§
Present address: Institut Pasteur, Department of Virology,
Arboviruses and Insect Vectors, Paris, France.
2012 Blackwell Publishing Ltd
nya outbreaks have been correlated with an emerging
CHIKV variant that is efficiently replicated and transmitted by Ae. albopictus (Schuffenecker et al. 2006; Vazeille
et al. 2007; Dubrulle et al. 2009; Tsetsarkin et al. 2011).
Over the last few years, many studies have focused on
the impact microbial communities have on the fitness
and competence of the insect vectors of pathogens. It has
been established that microbial symbionts can contribute
to insect host adaptation, including the aspects of nutrition and survival (Moya et al. 2008; Oliver et al. 2010),
reproduction (reviewed in Werren et al. 2008), heat tolerance (Montllor et al. 2002), protection against pathogens
or natural enemies (Scarborough et al. 2005; Hedges
et al. 2008; Oliver et al. 2009) and vector competence
(Gottlieb et al. 2010). Particularly, the bacterium Wolbachia was shown to interfere with or modulate the replication and transmission of both arboviruses and parasites
2298 K . Z O U A C H E E T A L .
in mosquitoes (Kambris et al. 2009; Moreira et al. 2009;
Bian et al. 2010; Glaser & Meola 2010; Mousson et al.
2010; Walker et al. 2011). Microbiota endogenous to some
mosquito species induce the expression of basal genes in
the antimicrobial immune response, thus optimizing host
immune signalling when pathogens attack. For instance,
the microbiota of mosquito vector Aedes aegypti influence
the infection by the dengue virus by modulating the gene
expression of several antimicrobial peptides (Xi et al.
2008). In addition, modification or elimination of commensal microbiota increased the sensitivity of Anopheles
gambiae to Plasmodium falciparum (Dong et al. 2009). Altogether, these data indicate that there is interplay between
resident bacterial communities and invading pathogens
in the mosquito vector pathosystem.
To better understand the mechanisms involved in the
failure or success of pathogen propagation and maintenance in the insect host, the interaction between resident
microbes and invading pathogen needs to be taken into
account not only at the individual or the species level,
but also in terms of the ecological community unit.
Advances in this domain are impaired by a weak
knowledge of the microbial diversity occurring in insect
host. Some studies have begun describing the bacterial
community associated with mosquito vectors. For Aedes
mosquitoes, cultivable bacteria belonging to Enterobacteriaceae family as Enterobacter, Klebsiella and Serratia have
been reported in the midgut of Aedes triseriatus maintained in the laboratory (Demaio et al. 1996). Recently,
wild populations of A. albopictus and A. aegypti have
been shown to harbour principally Proteobacteria and Firmicutes, including genera Acinetobacter, Asaia, Delftia,
Pseudomonas, Wolbachia and Bacillus, as well as members
of the family Enterobacteriaceae (Zouache et al. 2011).
Although mosquito-associated bacteria have recently
been shown to affect the transmission of pathogens, few
studies have tried to link the pathogen infections with
alterations in the insect-vector microbiota composition.
Here, the effect of CHIKV infection on the bacterial community in the Ae. albopictus population from La Reunion
was investigated at different times after a bloodmeal.
The dynamics of change in bacterial community was
analysed by comparing virus-infected and uninfected
individuals using taxonomic microarrays and quantitative PCR. Results showed that CHIKV impacts the
diversity of symbiotic bacteria in the mosquito vector.
Material and methods
wAlbA and wAlbB (Tortosa et al. 2008; Zouache et al. 2009)
and other commensal bacteria (Zouache et al. 2009). The
Ae. albopictus ALPROV population was maintained in standard conditions as described (Mousson et al. 2010), and the
fourth generation was used for all experiments. Briefly,
eggs from the third generation were hatched in water, and
larvae were reared in pans containing one yeast tablet per
litre of dechlorinated tap water. Adult mosquitoes were fed
with 10% sucrose and maintained at 28 ± 1 C and 80%
relative humidity under 16-h light ⁄ 8-h dark cycles.
Virus production and oral infection
The strain CHIKV (E1-226V), which has an A->V amino
acid substitution at position 226 in the E1 glycoprotein,
was used (Schuffenecker et al. 2006). Virus stock and
mosquito infection were performed as described (Vazeille et al. 2007; Mousson et al. 2010). To infect mosquitoes, 1 mL of viral suspension was added to 2 mL of
washed rabbit erythrocytes supplemented with ATP
(5 · 10)3 M) as a phagostimulant. The resulting infectious blood at a titre of 107.5 PFU ⁄ mL was transferred
to a glass feeder at 37 C on top of the mesh of a plastic
box containing 60 1-week-old female mosquitoes that
had been starved for 24 h. After 15 min of feeding,
mosquitoes were sorted on ice. Engorged females were
transferred to cardboard containers, then fed with 10%
sucrose at 28 C and analysed daily for 14 days.
Nucleic acid extraction
Five female mosquitoes were killed each day until day
14 post-infection. RNA and DNA were co-extracted
from individual mosquitoes using the NucleoSpin
RNA ⁄ DNA buffer set and NucleoSpin RNA II, following the manufacturer’s instructions (Macherey-Nagel).
Each mosquito was crushed in 350 lL of lysis buffer
(RA1) with 1% b-mercaptoethanol. Samples were
homogenized through a NucleoSpin filter. After adding
350 lL of 70% ethanol, samples were transferred to
NucleoSpin columns. Nucleic acids were bound to the
column membrane by centrifugation at 11 000 g for 30 s.
After 2 washes, DNA was eluted with 60 lL of water.
Column membranes were treated with DNAse (according to the manufacturer’s instructions), and after successive washes, RNA was eluted with 40 lL of RNAse-free
water. DNA and RNA were quantified using an ND1000 spectrophotometer (NanoDrop Technologies) and
stored at )20 C (DNA) or )80 C (RNA) until use.
Mosquito rearing
Larvae of Aedes albopictus were collected in Providence, La
Reunion Island, and maintained in insectaries. This strain
ALPROV is naturally infected with Wolbachia strains
Quantifying chikungunya virus
RNA extracted from one mosquito was used to measure
the viral load by quantitative RT–PCR. One-step
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V I R U S A N D B A C T E R I A L D Y N A M I C S I N M O S Q U I T O 2299
RT–PCR was performed in the LightCycler apparatus
(Roche Applied Science) using EXPRESS One-Step
SYBRGreenER Kit (Invitrogen) in a volume of 20 lL
containing 10 ng of RNA template, 1· EXPRESS SYBR
GreenER SuperMix Universal, 200 nM of each primer
and 1· EXPRESS Superscript Mix. Primers used correspond to the E2 structural protein coding region:
Chik ⁄ E2 ⁄ 9018 ⁄ + (5¢-CACCGCCGCAACTACCG-3¢) and
Chik ⁄ E2 ⁄ 9235 ⁄ ) (5¢-GATTGGTGACCGCGGCA-3¢). The
amplification programme was 50 C for 15 min, 95 C
for 2 min, then 40 cycles of 95 C for 15 s and 63 C for
1 min. Viral RNA was quantified by comparison to a
standard curve generated using duplicates of a 10-fold
serial dilution of synthetic CHIKV RNA transcripts
(Mousson et al. 2010).
PCR detection and quantification of Wolbachia
and the total bacterial community
Relative levels of the wsp gene, indicative of Wolbachia,
the rrs gene, indicative of the total bacterial community,
and the mosquito host actin gene in the DNA extracted
from mosquitoes were measured by quantitative PCR.
DNA was amplified using the LightCycler LC480 apparatus (Roche Applied Science). The 20-lL reaction mixture contained 5 ng of template DNA, 1· LightCycler
DNA Master SYBR Green I (Roche Applied Science)
and primers for wsp and rrs at 300 nM or for actin at
200 nM. Primers used were as follows: QAdir1 (5¢-GGG
TTGATGTTGAAGGAG-3¢) and QArev2 (5¢-CACCAGC
TTTTACTTGACC-3¢) for the wAlbA gene; 183F (5¢-AA
GGAACCGAAGTTCATG-3¢) and QBrev2 (5¢-AGTTGT
GAGTAAAGTCCC-3¢) for the wAlbB gene; actAlb-dir
(5¢-GCAAACGTGGTATCCTGAC-3¢) and actAlb-rev (5¢GTCAGGAGAACTGGGTGCT-3¢) for the actin gene;
519F (5¢-CAGCMGCCGCGGTAANWC-3¢) and 907R (5¢CCGTCAATTCMTTTRAGTT-3¢) for the rrs gene (numbers correspond to equivalent positions in Escherichia
coli gene) (Lane 1991). The PCR programme was 10 min
at 95 C, 40 cycles of 15 s at 95 C, 1 min at 60 C (for
wsp and actin genes) or 63 C (for the rrs gene) and
72 C for 30 s. Standard curves were drawn of the
amplification from template pQuantAlb16S, the pQuantAlb plasmid (Tortosa et al. 2008) containing a 405-bp
rrs gene fragment amplified from total A. albopictus
DNA.
PCR amplification and transcription labelling of rrs
genes
PCR amplification of rrs genes was carried out with a
modified forward primer T7-pA (5¢-TAATACGACT
CACTATAGAGAGTTTGATCCTGGCTGAG-3¢) and reverse primer pH (5¢-AAGGAGGTGATCCAGCCGCA-3¢)
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(Bruce et al. 1992). The T7 promoter sequence was
included in T7-pA to mediate the in vitro transcription.
PCR was performed in a T gradient Thermocycler
(Biometra) using a 50-lL reaction mixture containing
1 lL of DNA template in 1· polymerase reaction buffer
(Roche Applied Science), 200 lM of each deoxynucleoside triphosphate, 500 nM of each primer, 0.025 mg ⁄ mL
of T4 gene protein 32 (Roche Applied Science) and
0.5 U of Expand polymerase (Roche Applied Science).
PCR products were purified using the MinElute PCR
purification kit (QIAGEN, Hilden, Germany). DNA concentrations were determined using a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies) and
adjusted to 50 ng ⁄ lL. PCR products containing the T7
promoter were used as templates for T7 RNA polymerase–mediated in vitro transcription (Stralis-Pavese et al.
2004) and labelled with Cy3. Labelling was carried out
as previously described (Kyselková et al. 2008). Briefly,
RNA (50 lL) was incubated at 60 C for 30 min with
5.71 lL of 100 mM ZnSO4 and 1.43 lL of 1 mM Tris-Cl
(pH 7.4). Fragmentation was stopped on ice by adding
1.43 lL of 500 mM EDTA (pH 8). RNAsin (40 U) was
added. Fragmented and labelled RNA was stored at
)20 C until use.
Probe and microarray manufacture
Probes targeting different taxonomic levels such as phylum, family, genus and species were designed using
ARB software and its rrs database (Ludwig et al. 2004;
SSURef_96_SILVA_04_10_08_opt.arb; http://www.arbhome.de/). Probes used were those designed by Sanguin et al. (2006a,b, 2008) and Kyselková et al. (2009).
Probes synthesized in vitro (Eurogentec, Seraing, Belgium) were spotted onto slides at 50–55% relative
humidity and 19 C with a MicroGrid II spotter (BioRobotics, Cambridge, UK). Each probe was spotted four
times per slide. Slides were incubated overnight at
room temperature in the spotter, placed for 15 min in a
humid Genetix hybridisation chamber (Proteigene,
St Marcel, France) at 37 C, dried at 120 C for 90 min,
then stored in the dark at room temperature.
Hybridization
Slides were washed twice for 2 min each in 0.2· sodium
dodecyl sulphate (SDS) and twice for 2 min each in filtered deionized water with vigorous shaking at room
temperature. Slides were incubated for 15 min in a
fresh blocking solution (0.25 g of NaBH4 in 75 mL of
PBS and 25 mL of absolute ethanol) to reduce all
remaining reactive aldehyde groups on the slides.
Hybridization was carried out as described previously
(Kyselková et al. 2008).
2300 K . Z O U A C H E E T A L .
Slides were scanned at 532 nm with a 10-lm resolution
using a GeneTac LS IV scanner (Genomic Solutions,
Huntingdon, UK). Images were analysed using GENEPIX
4.01 software (Axon, Union City, CA, USA). Data were
filtered using R 2.10.0 software (R Development Core
Team, 2009; http://www.r-project.org) with parameters
defined by Sanguin et al. (2006a,b).
Statistical analysis
All statistical analyses were carried out using R software version 2.10.0 (R Development Core Team, 2009;
http://www.r-project.org). Differences in CHIKV titre
and densities of Wolbachia and total bacteria in Ae. albopictus were tested using ANOVA and Tukey HSD (honestly significant difference) according to the infection
status, day and their interaction as fixed effects. The
proportion of variance in hybridization data because of
the infection status and time was tested in a 50–50 multivariate analysis of variance (FF-MANOVA) with 10 000
rotations with infection and time as fixed effects (Langsrud 2002, 2005).
To determine the correlation between a given probe
and infection status, conditional inference random forest (CI-RF) prediction was performed using the RANDFORESTGUI program version 1.1 developed for the R
environment (R. J. Michelland & G. L. Grundmann,
unpublished data). A CI-RF is an ensemble of individual conditional inference trees as base learners, considered to be unbiased in comparison with classification
and regression analysis trees (CART) (Strobl et al.
2007). For each sample, each individual tree votes for
one cluster and the forest predicts the cluster that has
the most votes. The rate of sample misclassification in
good clusters by the forest is termed the out-of-bag
error (OOB). The CI-RF was fitted using (i) the microarray data of the 3 dates post-infection (days 5, 9 and
14 post-infection) corresponding to the maximum virus
load; (ii) the trees were not pruned and were grown as
large as possible; (iii) the number of randomly selected
probes to be searched through to find the best split at
each node (mtry) was the square root of the number of
probes; and (iv) the importance of probes used was the
unbiased conditional mean decrease in accuracy proposed by Strobl et al. (2007). The dissimilarity between
samples was plotted using an isotonic non-metric multidimensional scaling (nMDS) with 10 000 random starts.
To identify the smallest set of non-redundant probes
involved in the discrimination of infection status, we
carried out a backward probe elimination algorithm
from supervised CI-RF by successively eliminating the
least important probes and by using OOB as the mini-
mization criterion. To comfort the robustness of this
probe set, two subsequent analyses were performed: (i)
the Pearson r2 value was calculated between the initial
CI-RF and the new CI-RF dissimilarities and (ii) an iterative Kruskal–Wallis test was performed for each probe.
The selected probes were plotted using a heatmap
based on Ward trees with the CI-RF dissimilarities to
cluster microarray samples and Euclidian distances to
cluster probes. To test the changes in microarray probe
intensity over time, the Kruskal–Wallis test was performed for infected and uninfected mosquito data.
Results
CHIKV replication
After an infectious bloodmeal, the CHIKV viral load in
the ALPROV line of Aedes albopictus increased significantly from 104.54 ± 100.06 at day 0 to a maximum of
108.36 ± 100.24 at day 5 post-infection (P < 0.001) (Fig. 1).
The viral load remained high until day 8 post-infection
(108.10 ± 100.07), decreasing slightly until day 14 postinfection (107.50 ± 100.18).
Total bacterial community
Bacterial density, as measured by the ratio of bacterial
rrs to host actin gene copies (Fig. 2), was not significantly different between CHIKV-infected and uninfected mosquitoes. The copy number of the bacterial rrs
gene per mosquito at day 0 was between 101.34 ± 100.07
(infected) and 101.36 ± 100.09 (uninfected) and at day 13
between 101.54 ± 100.14 (uninfected) and 101.58 ± 100.16
(infected) (P > 0.01). Although not significant, the
10
Log viral RNA copies
Scanning, image analysis and normalization
8
6
4
2
0
0
2
4
6
8
10
12
14
Days post-infection
Fig. 1 Variation in chikungunya virus load. Five Aedes albopictus ALPROV females were killed each day post-infection and
nucleic acids extracted. RNA was used to measure the mean
viral load by quantitative RT–PCR. Bars represent ± standard
deviation (SD).
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V I R U S A N D B A C T E R I A L D Y N A M I C S I N M O S Q U I T O 2301
D05_NI
3
D09_NID05_NI
D09_NI
D05_NI
D05_NI
2
D09_NI
D09_IN
D05_IN
D09_NI
D09_IN
0
1.5
D09_NI
1
Dimension 2
D09_NI
D14_NI
D05_IN
–1
1.0
0
1
3
4
6
7
8
10
11
12
13
Days post-infection
D14_IN
D14_IN
D14_IN
D09_IN
D05_IN D09_IN
D05_IN
–2
Fig. 2 Total relative density of bacteria in Aedes albopictus ALPROV. Relative total bacterial density is given as the ratio of
the number of copies of bacterial rrs genes to that of host actin
genes for CHIKV-infected (grey) or uninfected (white) mosquitoes. Bars inside box-plots represent the median.
bacterial density tended to increase slightly from day 11
to day 13 in both CHIKV-infected and uninfected
mosquitoes.
A taxonomic microarray was used to analyse the
effects of blood feeding and CHIKV infection on bacterial community in mosquitoes by comparing rrs gene
sequences present in mosquitoes 1 day before and at
days 0, 2, 5, 9 and 14 after an infected or uninfected
bloodmeal. In total, 131 of 1035 probes were positive
(12.8%). Of these, most probes (73.3%) corresponded to
sequences from bacteria belonging to Proteobacteria comprising Alpha (38.5%), Beta (21.9%), Gamma (31.3%),
Delta (6.2%) and Epsilon (2.1%) classes. Other positive
probes corresponded to sequences from phyla Firmicutes
(11.5%), Bacteroidetes (4.6%), Actinobacteria (2.25%), Acidobacteria (3.0%), Cyanobacteria (2.3%), Planctomycetes
(0.8%) and OP11 division (2.25%).
The average number of positive probes per mosquito
was 42.7 ± 7.1. FF-MANOVA showed that kinetics and
infection status explained 38% and 5% of the variance,
respectively (P < 0.001).
To compare the infected and uninfected modalities,
mosquitoes on days 5, 9 and 14 after the bloodmeal
were chosen as times corresponding to the maximum
virus load (Fig. 1). A supervised CI-RF of the microarray data set was performed with the 131 positive
probes using time, infection status and their interaction
as fixed effects. To ensure the best performance of the
supervised CI-RF, the best entry for the model was previously determined as 13 (see Material and methods).
Results showed a good discrimination between infected
and uninfected samples (Fig. 3). The OOB of 4% of the
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D14_NI
D09_IN
–2
Log rrs copy number/action
copy number
2.0
0
Dimension 1
2
4
Fig. 3 Isotonic non-metric multidimensional scaling (nMDS)
map of rrs microarray data. Supervised CI-RF dissimilarities
constrained by infection were used as an input for the nMDS
plot. Sample day and infection status are indicated next to each
point. NI, uninfected (continuous circle); IN, infected (dashed
circle). Of 1035 probes, 131 were found to be positive.
Stress = 8.86%.
supervised CI-RF was low because of one sample misclassified in the uninfected clusters (D09_IN) (Fig. 3).
To identify the set of non-redundant probes significantly involved in the discrimination of samples, a
backward probe elimination algorithm was applied.
This procedure allowed successive elimination of the
least important probes using OOB as minimization criterion (Fig. S1, Supporting Information), and identified
15 discriminating probes with the smallest OOB of 4%
(Table 1, Fig. S1A, Supporting Information). This OOB
value was explained by the same misclassification of
D09_IN as in the initial supervised CI-RF. The correlation between CI-RF dissimilarities using 131 probes or
the 15 discriminating probes was 0.96 (P < 0.001,
Fig. S1B, Supporting Information), which demonstrated
that these 15 probes were sufficient to classify the samples. The Kruskal–Wallis test performed on each of the
15 probes showed a significant difference between
infected and uninfected clusters (P < 0.05).
Variation in bacterial groups after chikungunya virus
infection
The contribution of each of the 15 probes in discriminating uninfected from CHIKV-infected mosquitoes
ALPROV was assessed (Fig. 4). The relative intensities
of Gammaproteobacteria probes were higher for CHIKVinfected individuals (P < 0.001). The signal intensity of
probes targeting sequences from bacteria affiliated to
2302 K . Z O U A C H E E T A L .
Table 1 Phylogenetic affiliation of the target of the 15 discriminating probes
Phylogenetic affiliation of the target
Probes
Class
Family
Genus
Species
Alpha685
Brady4
Wol3
Wol4
Rhodobacter1B
Blatta2
Ecoli1B
Alphaproteobacteria
Alphaproteobacteria
Alphaproteobacteria
Alphaproteobacteria
Alphaproteobacteria
Bacteroidetes
Gammaproteobacteria
⁄
Bradyrhizobiaceae
Anaplasmataceae
Anaplasmataceae
Rhodobacteriaceae
⁄
⁄
Wolbachia
Wolbachia
⁄
⁄
⁄
pipientis
pipientis
⁄
Enterobacteriaceae
⁄
enter3
enter4
enter5
enter6
Gammaproteobacteria
Gammaproteobacteria
Gammaproteobacteria
Gammaproteobacteria
Enterobacteriaceae
Enterobacteriaceae
Enterobacteriaceae
Enterobacteriaceae
Escherichia, Citrobacter,
Salmonella, Shigella
Escherichia
Escherichia
Pagg6
Plancto12
OP2-1 (OP2 phylum)
OP11-5 (OP11 division)
Gammaproteobacteria
Planctomycetes
Uncultured Eubacteria
Uncultured Eubacteria
Enterobacteriaceae
agglomerans
⁄
⁄
⁄
⁄
⁄
⁄
Fig. 4 Heatmap of the 15 positive bacterial rRNA probes discriminating
CHIKV-infected from uninfected ALPROV mosquitoes. Samples are clustered using the Ward tree method based
on supervised CI-RF dissimilarities,
while probes are clustered using the
Euclidian distances. Relative fluorescent
intensity of probes is indicated with a
heat colour palette. Row side colours
correspond to infection microbial clusters. Column side colours correspond to
the taxonomic unit importance. Sample
day and infection status are indicated
on the right of the heatmap. Numbers
correspond to day after the ingestion of
bloodmeal; NI, uninfected, IN, infected.
Samples with the same name correspond to mosquitoes replicates killed
for a given day (days 5, 9 and 14).
Brady4
Rhodobact1B
Wol4
Wol3
Alpha685
OP2-1
OP11-5
Blatta2
enter5
enter6
enter4
Pagg6
enter3
Ecoli1b
Plancto12
0.042
⁄
Escherichia, Citrobacter,
Salmonella, Shigella
Pantoea
D09_NI
D05_NI
D05_NI
D09_NI
D09_NI
D05_NI
D09_NI
D14_NI
D09_NI
D09_NI
D09_IN
D14_NI
D09_IN
D05_NI
D09_IN
D05_IN
D05_IN
D14_IN
D14_IN
D09_IN
D14_IN
D05_IN
D05_IN
D09_IN
0
coli, alberti
0.084
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Change in bacterial community following bloodmeal
As previously mentioned, microarray data analysed
with FF-MANOVA indicated that 38% of the observed variance was explained by the time elapsed after mosquito
feeding. The bacterial diversity at days 0, 2, 5, 9 and 14
after the bloodmeal was examined using the Kruskal–
Wallis test. Overall, a change in bacterial community
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1.2
(A)
1.0
0.8
0.6
0.4
0
1
3
4
6
7
8
10
11
12
13
Days post-infection
Log wsp wAIbB copy number/action copy number
the Enterobacteriaceae family (enter5) was higher for
infected than for uninfected mosquitoes (P < 0.001). At
the genus level, signals of probes targeting Enterobacteriaceae sequences were also higher for CHIKV-infected
than for uninfected mosquitoes, including (i) Ecoli1b
(P < 0.001) and enter6 (P < 0.001) targeting the
Escherichia, Citrobacter, Salmonella and Shigella sequence
cluster; (ii) enter3 (P < 0.001) and enter4 (P < 0.001) targeting sequences from other Escherichia species (E. coli
and E. alberti); and (iii) Pagg6 targeting the Pantoea agglomerans sequence (P < 0.001).
On the contrary, relative intensities of probes characteristic of Alphaproteobacteria, Bacteroidetes and Planctomycetes decreased with CHIKV infection. These probes
include Alpha685 for Alphaproteobacteria (P < 0.01), Blatta2 for Blattabacterium (P < 0.01), Plancto12 for Planctomycetes (P < 0.05), as well as OP11-5 for uncultured
Eubacteria WCHB1 (OP11 division) and OP2-1 for phylum OP2 (P < 0.05). Positive probes targeting Alphaproteobacteria sequences that had a weaker signal intensities
in CHIKV-infected mosquitoes included (i) Rhodobact1B for bacteria affiliated with the Rhodobacteraceae
family (P < 0.01), (ii) Brady4 for Bradyrhizobiaceae family
(P < 0.05) and (iii) Wol3 (P < 0.05) and Wol4 (P < 0. 01)
for the genus Wolbachia.
As Wolbachia is the most prevalent symbiont in Ae. albopictus (Kittayapong et al. 2002; Zouache et al. 2011), we
quantified the decrease in relative density of this bacterium by quantitative PCR. Wolbachia relative density was
measured as the ratio of wsp wAlbA or wAlbB copy number to host actin copy number (Fig. 5). In mosquitoes fed
on uninfected bloodmeal, Wolbachia relative density
increased, not significantly, between day 0 and day 13
from 100.98 ± 100.01 to 101.12 ± 100.05 for wAlbA (P < 0.67)
(Fig. 5A) and from 100.96 ± 100.02 to 101.18 ± 100.18 for
wAlbB (P < 0.08) (Fig. 5B). The infectious CHIKV bloodmeal led to a significant decrease (P < 0.001) in the relative density of each Wolbachia strain. At day 0 postinfection, the relative density of Wolbachia per mosquito
was 100.97 ± 100.02 for wAlbA and 100.95 ± 100.04 for
wAlbB. Wolbachia density decreased significantly by day
13 post-infection to 100.54 ± 100.01 wAlbA per mosquito
(P < 0.001) (Fig. 5A) and 100.41 ± 100.07 wAlbB per mosquito (P < 0.01) (Fig. 5B). These results are consistent
with those obtained from microarrays (Fig. 4).
Log wsp wAIbB copy number/action copy number
V I R U S A N D B A C T E R I A L D Y N A M I C S I N M O S Q U I T O 2303
1.4
(B)
1.2
1.0
0.8
0.6
0.4
0
1
3
4
6
7
8
10
11
12
13
Days post-infection
Fig. 5 Wolbachia relative density in Aedes albopictus. DNA
extracted as in Fig. 1 was used to quantify Wolbachia relative
density by qPCR as the ratio of wsp gene to host actin gene
copy numbers. Variation in relative density of Wolbachia strains
wAlbA (A) and wAlbB (B) in CHIKV-infected (grey) or uninfected (white) mosquitoes. Bars inside box-plots represent the
median.
structure was observed. Alpha- and Deltaproteobacteria,
Bacteroidetes and Firmicutes were positive for both
CHIKV-infected and uninfected mosquitoes, contributing significantly to time discrimination (Table 2). The
greatest variation in the relative intensity was for
probes corresponding to acetic acid bacteria of the
Alphaproteobacteria. Indeed, probes Aceto2, Aceto3A and
Aceto3B for the Acetobacteriaceae family and probes corresponding to genera Acidiphilum (Aci), Acidocella (Acidocella1), Acidomonas (Acidomonas2 and Acdp821),
Asaia (Asia2) and Gluconacetobacter (Inteur1) increased
significantly with time after bloodmeal (Table 2). A
similar increase was observed for probes targeting Rhiz-
2304 K . Z O U A C H E E T A L .
Table 2 Dynamics of bacterial groups in uninfected and infected mosquitoes after the bloodmeal
2012 Blackwell Publishing Ltd
V I R U S A N D B A C T E R I A L D Y N A M I C S I N M O S Q U I T O 2305
Table 2 Continued
obiales sequences. These included probes for the Bradyrhizobiaceae family (Bradhy4), for the Rhizobiaceae family
(Rgal157, Rhira7 and Rhizo157) and for the Methylobacterium genus (Methylo1). Azo1 and Azo5 probes for the
genus Azospirillum also increased significantly with
time. By contrast, a significant reduction in signal intensity was observed for probes targeting sequences from
other Alphaproteobacteria such as members of genera
Cowdria (cow1) and Ehrlichia (Ehrli), and Rhodobacteraceae family (Rhodobacter1). In the same way, signal
intensities for probes indicative of bacteria from the
phyla Deltaproteobacteria (probe un43Chon4 for uncultured Chondromycetes), Firmicutes (probes LGC354A and
LGC354B, Paeni7-1 and Pbo1 for Paenibacillus) and Bacteroidetes (probe Blatta2 for Blattabacterium) decreased
with time (Table 2).
A subcommunity of bacteria (92 positive probes) was
not significantly different for all modalities and times
(Table S1, Supporting Information). This mostly
included bacteria from phyla Proteobacteria (71.7%), that
is, Alpha- (21.2%), Beta- (31.8%), Gamma- (36.4%), Delta(7.6%) and Epsilon Proteobacteria (3%), as well as Firmicutes (12%), Bacteroidetes (5.4%), Actinobacteria (3.3%)
and Acidobacteria (4.3%) and Cyanobacteria (3.3%)
(Table S1, Supporting Information).
Discussion
There is increasing evidence that interactions occur
between resident or introduced microbial taxa in arthro 2012 Blackwell Publishing Ltd
pods and invading pathogens (Azambuja et al. 2005;
Brownlie & Johnson 2009; Cirimotich et al. 2011; Weiss
& Aksoy 2011). For instance, previous studies on Wolbachia indicated that it interacted with CHIKV in mosquitoes (Moreira et al. 2009; Mousson et al. 2010). Using
culturing and denaturing gel electrophoresis methods,
we previously found that Proteobacteria and Firmicutes
dominated the bacterial communities associated with
Ae. albopictus from the Indian Ocean, and the bacterial
diversity and composition were influenced by the environment inhabited by the mosquitoes (Zouache et al.
2009, 2011). Here, using a taxonomic microarray targeting more diverse bacterial taxa, we showed that the
bacterial community in Ae. albopictus ALPROV strain
originating from La Reunion island was more diverse
than previously described (Zouache et al. 2009) and that
various endosymbionts could interact with each other
and with CHIKV within the host.
The infection status of mosquitoes was established by
quantifying virus and bacterial abundance in individual
mosquitoes, making it possible to compare the diversity
and composition of the bacterial populations in mosquitoes fed with CHIKV-infected or uninfected blood. Overall, our results revealed that community composition
was influenced primarily by the time elapsed after the
bloodmeal and secondarily by CHIKV infection. Variations owing to virus infection were significant, although
restricted to fewer taxa. The bloodmeal induced a change
in the composition of the bacterial community, but not of
its structure, whereas the density of bacteria changed
2306 K . Z O U A C H E E T A L .
slightly with ageing of mosquitoes, probably due to the
modified nutritional conditions (Dillon et al. 2010).
A core bacterial community in Ae. albopictus, modified neither by infection nor by the bloodmeal, was
mostly represented by Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria and Acidobacteria. The presence of
core taxa has been noted previously in other environments, and it may function to stabilize the community.
When the bacterial community was modified, most
changes concerned abundance as infected and uninfected samples differed in terms of probe signal intensity rather than in number of positive probes.
Variability in the signal intensity of a given probe is
expected to reflect a modification of the number of target rRNA molecules, unless nonspecific binding
occurred. For instance, the intensity of specific probes
corresponding to Wolbachia was shown to decrease in
CHIKV-infected but not in uninfected individuals.
Quantitative PCR confirmed that, as opposed to uninfected mosquitoes, the relative density of Wolbachia in
CHIKV-infected mosquitoes decreased as the CHIKV
titre increased. Therefore, the differences in probe signal intensities between samples may be interpreted as
differences in abundance. The results showing that
Wolbachia relative density decreases concomitantly with
CHIKV replication were consistent with our previous
studies (Mousson et al. 2010). The signal intensity of
the probe for the Blattabacterium was significantly
higher in uninfected mosquitoes. This is the first evidence for Blattabacterium presence in mosquitoes. As it
is one of the bacteria strongly discriminating between
infected and uninfected individuals, it should be the
focus of further investigation to localize it and to
determine its role in insect physiology. Other probes,
the intensity of which decreased upon CHIKV infection, also corresponded to phyla Alphaproteobacteria,
such as members of families Bradyrhizobiaceae and Rhodobacteriaceae, Bacteroidetes and Plantomycetes.
Conversely, CHIKV invasion resulted in a higher
intensity of probes corresponding to Beta- and Gammaproteobacteria. Although no function can be assigned to
bacteria identified through rrs microarray technology,
these bacterial taxa inhabit diverse environments and
some are known to establish facultative or mutualistic
symbioses with insects (reviewed in Oliver et al.
2010). This metabolic feature may be important for
insects to cope with the cost of microbial infection.
Interestingly, the intensity of probes targeting
sequences from acetic bacteria increased significantly
over time for both infected and uninfected individuals.
This increase suggests that acidification may accompany blood degradation in mosquitoes. Among acetic
bacteria, the genus Asaia is known to be associated
with Anopheles stephensi and Aedes mosquitoes where it
is vertically transmitted from one generation to the
next (Favia et al. 2007; Crotti et al. 2009; Damiani
et al. 2010; Zouache et al. 2011).
It is not clear whether CHIKV invasion affects distinct bacterial taxa or whether it is a collateral effect
resulting from the Ae. albopictus innate immune
response to combat viral infection (Sanchez-Vargas
et al. 2004, 2009; Sanders et al. 2005; Xi et al. 2008;
Fragkoudis et al. 2009). Alternatively, virus infection
might compete with the mosquito-associated microbiota for limited resources or space. To corroborate this
hypothesis, it would be interesting to localize bacteria
after viral infection and bloodmeal and to focus on
key organs for viral dissemination ⁄ replication and
transmission, that is, midgut and salivary glands.
Although the over-replication of CHIKV coincides with
a decrease in Wolbachia relative density in CHIKVinfected individuals, the total bacterial community
does not respond in the same way.
Altogether, the results show that while the size of
the whole bacterial community remains the same upon
virus infection, sizes of subpopulations change. These
subpopulations include taxa with probable direct and
indirect roles in determining the outcome of infection
status. This is the first study to show that virus infection affects the composition and dynamics of total bacterial community in mosquitoes. To date, no treatment
or vaccine is available for most arboviruses including
CHIKV. The bacterium Wolbachia is a candidate to
limit the transmission and spread of arboviruses using
symbiosis-based control (reviewed in Aksoy 2008). But
our results suggest that other bacteria could also be
candidates. While there is increasing evidence for both
positive and negative effects of natural or introduced
bacteria on parasite development and transmission
(Azambuja et al. 2005; Dong et al. 2009; Cirimotich
et al. 2011; Weiss & Aksoy 2011), our results highlight
the importance of considering the whole microbial
community and their reciprocal interactions, to better
appreciate the phenomenon of interference in determining vector competence. However, as the study is
based exclusively on 16S rRNA gene microarray using
one Ae. albopictus line and one virus strain, the data
should be taken in the context that expression of functional bacterial and viral genes and influence of the
host genetic background are lacking. This limitation is
potentially significant because the type of the interaction may vary depending on all interacting genetic
determinants. This line of reasoning is that the
extended mosquito phenotype, at the individual level,
might result from interactions (cooperation or competition) between communities of genes belonging to different entities (nuclear and symbionts). Multipartite
interactions between a community of genetic compo 2012 Blackwell Publishing Ltd
V I R U S A N D B A C T E R I A L D Y N A M I C S I N M O S Q U I T O 2307
nents from hosts and symbionts might also have an
impact at population and community levels as they
could participate in, for example, local adaptation to
changing environment, colonization of new biotopes
(invasion), evolution and speciation. Future work on
mosquito vector systems should therefore seek to integrate the concept of the extended phenotype, from
competence through to adaptive capacity, resulting
from interacting genomes, in a community genetics
approach (Whitham et al. 2006). This will help in deciphering the mechanisms involved in viral transmission
and improve the present use of symbiotic control strategies (Christodoulou 2011).
Acknowledgements
KZ was supported by PhD fellowships from the French Ministère de l’Education Nationale, de la Recherche et des Nouvelles
Technologies. We thank Martina Kyselková and Juliana Almario for advices on microarray experiments. We thank Laurence
Mousson and Camilo Arias-Goeta for mosquito’s infection.
This work was funded by grants from Agence National de la
Recherche (ANR-06-SEST07 ChikVendoM) and Fondation pour
la Recherche sur la Biodiversité (FRB, formely IFB, CD-AOOI07-012) and was carried out within the frameworks of GDRI
‘Biodiversité et Développement Durable à Madagascar’ and
COST action F0701 ‘Arthropod Symbioses: from fundamental
to pest disease management’.
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Data accessibility
DRYAD entry:doi:10.5061/dryad.3486n266.
Supporting information
This study is part of K.Z.’s PhD research devoted in investigating the role of bacterial communities in transmission of arboviruses by mosquitoes. The work was accomplished under
supervision of P.M., the head of the team ‘‘Microbial Dynamics
and Viral Transmission’’ involved in a broad range of studies
on microbial ecology in eukarya, including arthropod vectors
such as mosquitoes and ticks, in the unit ‘‘Microbial Ecology’’
at the University of Lyon. R.J.M. was a postdoctoral statistician
under supervision of G.L.G., an environmental microbiologist.
A-B.F. is head of ‘‘Arboviruses and Insect Vectors Laboratory’’
in the department of Virology at the Institut Pasteur in Paris,
working on a wide range of topics regarding the transmission
of arboviruses by mosquitoes.
2012 Blackwell Publishing Ltd
Additional supporting information may be found in the online
version of this article.
Table S1 Core of bacterial community of Aedes albopictus.
Fig. S1 Determination of the optimal number of probes specific
to infection status using the out-of-bag as the minimization criterion in the backward probe selection algorithm based on conditional inference random forest (CI-RF).
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