Introduction

Macro-organisms can be viewed as distinct ecosystems, in which numerous microorganisms establish close mutualist, commensal and pathogenic associations with their hosts [1]. These microbial-host associations are known to influence host fitness as well as host adaptation and evolution when the microbial components are transmitted among generations—a common feature in many microbial-host systems [2]. In particular, insects are suitable model systems to disentangle mechanisms driving the interactions between the host and its associated microbiome [3].

The insect microbiome is known to influence host phenotype in a variety of ways: through diet supplementation [4], the transmission of pathogens in insect disease vectors [5], reproductive behaviour and isolation [6] and kin recognition [7]. More specifically, Drosophila melanogaster Meigen (Diptera: Drosophilidae) has been used as a model to study host-microbiome interactions since the beginning of the 20^th century and was the first gnotobiotic organism to be cultured (i.e. under aseptic conditions) [8]. Curing Drosophila from its microbiome revealed modifications in a number of host physiological responses, ranging from reproduction [9] to immunity and resistance to parasitoids and pathogens [10–15]. Some of these changes are attributed to the genus Wolbachia, a widespread endosymbiont of arthropods [9], but Wolbachia is not the only bacterium affecting hosts’ fitness. Also gut-associated bacteria [16], other endosymbionts [15], or microbes that reside on its exogenous body parts [17] can exert great influence on their host, by affecting lifespan [17], intestinal stem cell activity [18], kin recognition and mate choice [7,19]. Although much research has been performed on the range of processes that are affected by the microbiome [20], it is still not clear what the relative contributions are of different mechanisms shaping the Drosophila microbiome and host-symbiont interactions.

Despite its relative simplicity, consisting of 1–30 OTUs [21,22] and usually dominated by 1 or 2 taxa [22,23], the Drosophila microbiome is dynamic, changing throughout the developmental stages of the host [21]. The factors that are most likely to exert an influence on the establishment of Drosophila microbiome have been shortlisted: host diet [23,24], host taxonomy [23,25], geography, morphology, genetics, physiology [26], random events [24] and surrounding environmental conditions [27]. Different studies have been using different methods, so comparisons should be done with caution. However, the debate persists on which factors have a dominant role. Whereas some studies suggested the core Drosophila microbiome to be shaped by the diet, and not by geography or host taxonomy [21,23], others established that taxonomy [25] or stochastic processes, rather than diet, was the major driver of bacterial composition and disputed the existence of a core microbiome in Drosophila [21,24].

Population background is also a factor that can potentially exert an influence on the D. melanogaster microbiome. For instance, it was shown that the microbiome of freshly caught D. melanogaster flies from various fruits across distant geographical populations differed in bacterial composition [28]. Natural populations encounter local conditions that may vary considerably, in terms of abiotic conditions (e.g. temperature, humidity), the available food sources and the other organisms that inhabit these environments. Moreover, as D. melanogaster feed and breed on a variety of decomposing fruit [29], they are exposed to a broad range of microorganisms, which form a pool of mutualists, commensals and potential pathogens [27]. It has been demonstrated that natural populations of Drosophila adapt to their local conditions, including differentially resisting various bacterial pathogens [26] and parasites [30,31]. The underlying mechanism for resistance against bacterial pathogens is associated with genetic variation in immunity genes [26]. Since interactions between fruit fly hosts and microorganisms can vary greatly between different environments, this could imply that Drosophila populations of different genetic backgrounds are capable of acquiring and maintaining different bacterial types. One tantalizing question is whether the phenotypic and genetic variation among natural populations in parasite resistance is perhaps partially mediated by the complex interactions between the host and its microbiome.

This study aimed to determine whether population background affects the Drosophila microbiome in natural populations reared in the lab under identical conditions for over 4 years, and whether the established Drosophila microbiome is of significance for host immunity against parasites. Firstly, we determine whether six genetically differentiated populations of D. melanogaster differ in microbiome when controlling for diet effects for ca. 50 generations. If host population origin (and not only diet) plays a role in shaping the bacterial community, we would predict differences between microbiome compositions among different Drosophila lines that were reared on identical diets for years. Population background differences may reflect the population genetics of the lines, or alternatively, long-lasting associations with the original microbiome. Secondly, we evaluated whether antibiotic manipulation of the hosts’ microbiome had a phenotypic effect on host resistance to the parasitoid Asobara tabida Nees (Hymenoptera: Braconidae). Asobara tabida is a small wasp, that attacks 2-3^rd instar larvae of Drosophila and lays an egg, which will either develop into an adult wasp and kill the fly, or will be killed itself by the hosts immune response [32–36]. Natural populations of D. melanogaster vary in their resistance to A. tabida [31,32]. If the variation in the resistance is mediated by host-microbiome interactions, we would predict changes in parasitoid resistance after Drosophila microbiome was altered by antibiotics. Finally, if the microbiome differs among lines of different population backgrounds, we might expect dissimilar effects of antibiotic treatment on parasitoid resistance among those Drosophila lines. To test our hypotheses we assessed the bacterial community composition, diversity and abundance, as well as the specific abundance of Wolbachia in 3^rd instar larvae of six D. melanogaster lines, i.e. at the developmental stage that has to defend itself against the parasitoid. We subjected these lines to a broad-spectrum of antibiotic treatments to disturb the indigenous microbiome and tested whether this would affect their resistance to A. tabida.

Materials and methods

Microbiome composition

DNA extraction.

Total genomic DNA was extracted from pooled samples containing ca. 30–40 2-3^rd instar D. melanogaster larvae. We collected three biological replicates per line. Larvae were not surface-sterilized because it was previously demonstrated that Drosophila have an exogenous bacterial community, which is also important for the host’s physiology [17]. To collect larvae, adults were incubated overnight for egg laying on Petri-dishes with standard medium at 25°C. The adults were removed in the morning and the eggs transferred to 20°C. At 72 h after egg laying the larvae were carefully collected with a fine sterile spatula and snap-frozen in liquid nitrogen.

Insects were thoroughly homogenized with a sterile motorized pestle to make sure intracellular bacterial DNA (e.g. Wolbachia) was also extracted. DNA was isolated using the Power Soil^® DNA Isolation Kit, following the manufacturer`s protocol (Power Soil^®, MoBio Laboratories Inc., California, United States). DNA concentration was quantified using NanoDrop ND2000 (Thermo Scientific^TM) and standardized to concentrations of 25 to 50 ng μL^-1.

PCR condition for the partial amplification of the bacterial 16S rRNA gene.

Partial bacterial 16S rRNA gene was PCR-amplified using the primer set F968/R1401 (S1 Table)—expected fragment size of 433 bp—in the following 50 μL master mix: 0.4 μL of 25 mM dNTPs, 3.75 μL of 50 mM MgCl₂, 5 μL of 10x PCR Buffer, 0.5 μL of Formamide, 0.25 μL of 20 mg mL^-1 bovine serum albumin (BSA), 200 nM of forward and reverse primers, and 0.5 μL of 10 U μL^-1 Taq DNA polymerase (Roche Applied Science, Germany). To ensure the specificity of the reaction, touchdown PCR condition was set as follows: the initial denaturation step at 94°C for 5 min, followed by 10 cycles of 94°C for 1 min, 60°C (lowering the temperature by 0.5°C every cycle) for 1 min, 72°C for 2 min; and by 25 cycles of 94°C for 1 min, 55°C for 1 min, 72°C for 2 min; with a final step of 72°C for 30 min. The presence and specificity of the amplicons were verified in 1.5% agarose gel stained with ethidium bromide.

Denaturing Gradient Gel Electrophoresis (DGGE).

The DGGE analysis was performed to estimate differences in the structure of bacterial communities across populations of D. melanogaster and to determine the sampling effort needed to fully characterize their community composition. 16S rRNA bacterial genes were PCR-amplified using the primer set F968 with a GC-clamp attached to 5’ and R1401 (S1 Table), as described above. The obtained amplicons were further used for the DGGE analysis. The DGGE were visualized with Imagemaster VDS (Amersham Biosciences, Buckinghamshire, United Kingdom) and further analysed with GelCompar software (Applied Maths, Sint-Martens Latem, Belgium). The observed low bacterial diversity, as evidenced by the low number of bands on DGGE gels (S1 Fig), justifies our choice of sequencing method (Sanger sequencing) and was used to determine sampling size.

Cloning and sequencing of the bacterial 16S rRNA gene.

The previously amplified 16S rRNA gene region (without the GC-clamp) was used for the cloning library. The PCR product was diluted and 20 ng of the amplicon was ligated into the pGEM-T vector (Promega, manufacturer’s instructions). Competent Escherichia coli cells were used for the transformation step. For each of the 6 lines, 3 biological replicates were used.

For each replicate ~90 clones were picked from the agar plates for sequencing and analyses. Positive clones were amplified using pGEM-T forward and reverse primers (S1 Table). The amplicons were checked for size and concentration on 1.5% agarose gel. PCR products of the expected ~440 bp were purified following the manufacturer’s recommendations (ExoSap-IT^®, Affymetrix) and further used for sequencing on ABI3170 Prism sequencer by Applied Biosystems, following the manufacturer’s protocol (Applied Biosystems Big Dye^®).

Analyses of bacterial 16S rRNA gene clone libraries.

Obtained sequence chromatograms were initially trimmed using the Lucy algorithm [38] at a threshold of 0.002 (quality score of 27), available within the Ribosomal Database Project (RDP) pipeline (https://rdp.cme.msu.edu/pipeline/). Only sequences with trimmed lengths longer than 320 bp were retained for analysis (i.e. 1,044 sequences representing the six lines of D. melanogaster). In order to integrate the cleaned sequence data into the QIIME pipeline [39], we artificially added barcodes sequences (ca. 10 bp) at the 5’ of each sequence. Different barcodes were added for each sample. Operational taxonomic units (OTUs) were generated by binning the sequences at 97% of nucleotide identity using Uclust [40]. Selected representative sequences per OTU were aligned against the Greengenes coreset [41] using PyNAST [39], with sequences classified using the Greengenes taxonomy via RDP classifier [42]. The alignment was filtered to remove common gaps and a phylogenetic tree was constructed de novo using FastTree [43].

For all OTU-based analyses, the original OTU table was rarefied to a depth of 50 sequences per sample (i.e. the lowest in a single sample), to minimize effects of sampling effort on the analysis. This was carried out using default parameters in QIIME (script alpha_rarefaction.py) that conducts 10 iterations per sampling depth. Rarefaction curves and the estimated sample coverages for each sample are shown at S2 Fig and Table 1, respectively. One replicate (ARL_2) was excluded from the analysis due to the low number of sequences (ca. 30).

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Table 1. Estimation of alpha-diversity indices from bacterial communities associated with D. melanogaster lines.

https://doi.org/10.1371/journal.pone.0167726.t001

The QIIME was also used to generate weighted/unweighted UniFrac distance matrices [44] and alpha-diversity metrics, including OTU richness (unique OTUs), ChaoI richness estimation, Shannon’s and Faith’s phylogenetic diversity indices. Alpha diversity differences were compared among lines using an ANOVA (group_significance.py in QIIME [45]). The OTU Venn diagram was constructed using jvenn [46].

All sequencing data have been deposited in the NCBI database under submission KT189679 –KT190693 (http://www.ncbi.nlm.nih.gov/).

Antibiotic treatment for host microbiome manipulation.

To induce a shift in the microbiome, we temporarily treated the flies with antibiotics, and then enabled re-establishment of a larval microbiome through natural colonization processes (i.e., from contact with the food and the faeces of parental flies). The D. melanogaster lines were divided into two groups: control and treatment. The control group was reared on the standard medium, while a combination of antibiotics was added into the medium of the treatment group for three successive generations [19]. The antibiotic mixture contained: rifampycin (400 μg mL^-1), streptomycin (100 μg mL^-1) and tetracycline (200 μg mL^-1) (Sigma-Aldrich^®). After the treatment, the treated D. melanogaster lines were reared for two generations on standard (antibiotic-free) medium in order to eliminate possible toxic effects of the antibiotics on the fly metabolism, and to allow for the establishment of the (shifted) microbiome.

Results

The microbiome composition of D. melanogaster lines

To describe microbial communities associated with D. melanogaster lines, 16 clone libraries were obtained (2–3 per line). A total of 1,044 clones were successfully sequenced. Based on 97% of nucleotide identity, the sequences were binned into 75 OTUs (S2 Table). Classification to the genus level (not possible for some OTUs) revealed the presence of 18 distinct genera (Fig 1), encompassing 42 OTUs. Three genera were represented by a minimum of 5 different OTUs: Acetobacter, Staphylococcus and Wolbachia (S2 Table).

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Fig 1. The relative abundance of bacterial taxa in six D. melanogaster lines, based on the taxonomic affiliation of a 16S rRNA gene fragment.

The relative abundances of (a) bacterial families and (b) bacterial genera in six lines of D. melanogaster, which were derived from natural populations across Europe. Biological replicates are indicated as numbers next to the line abbreviation: ARL, Arles; BAY, Bayreuth; BRE, Bremen; GOTH, Gotheron; GRO, Groningen; STA, St. Andrews.

https://doi.org/10.1371/journal.pone.0167726.g001

The estimated sample coverages (ESC) varied from 0.76 to 0.98 across the libraries (for a detailed description see Table 1). In addition, rarefaction curves are provided (S2 Fig). The richness estimator and the diversity indices (i.e. Chao1 and Shannon, respectively) indicated that the lines differed in their bacterial community richness: BRE had the most diverse and STA the least diverse microbial communities (Table 1). Statistical comparison among lines of the Shannon Index (F_5,9 = 5.916, p = 0.011) and Faith's Phylogenetic Diversity (PD, F_5,9 = 10.554, p = 0.001) indeed revealed significant differences among the lines in alpha diversity (S3 Table). The data on the numbers of OTUs per line were visualized in detail (Fig 2): BRE had 39 OTUs, including 28 that were uniquely found in this line, while STA had only eight OTUs, of which two were unique.

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Fig 2. The number of OTUs in the microbiome of six D. melanogaster lines.

The Venn diagram represents OTUs that are shared among the different subsets of lines, or unique to a single line (Venn diagram constructed with jvenn [46]). The barplot represents the total number of OTUs per line. The following line name abbreviations were used: ARL, Arles; BAY, Bayreuth; BRE, Bremen; GOTH, Gotheron; GRO, Groningen; STA, St. Andrews.

https://doi.org/10.1371/journal.pone.0167726.g002

The family Acetobacteriaceae was the most common taxon, present in each replicate of the lines and accounting for 25–95% of the sampled OTUs (Fig 1A). The genus Wolbachia was also abundantly found in the samples, except for the line STA (Fig 1B). The BRE line was characterized by a higher abundance of families Planococcaceae, Enterobacteriacea, Staphylococcaceae and Moraxellaceae, which were absent or poorly represented in other lines (Fig 1A). The STA line was associated with low diversity, absence of Wolbachia and presence of the family Leuconostocaceae (STA_1) and Micrococcaceae (STA_2). We found only three OTUs to be present across all lines (Fig 2), and these were taxonomically affiliated to the families Acetobacteraceae (two OTUs; Acetobacter and unknown genera) and Lactobacillaceae (one OTU; unknown genus).

Beta-diversity analysis of the D. melanogaster bacterial communities was performed based on UniFrac distances (Fig 3). The unweighted UniFrac PCoA (Fig 3A) segregated BRE and STA from the other lines in the first axis (Principal component 1, explaining 32.12% of the variation). The second axis (Principal component 2, explained 17.75% of the variation) separated BRE and STA and, in addition, provided evidences for differences across replicates within some D. melanogaster lines (that is, BAY, GRO and GOTH) (Fig 3A). In the weighted UniFrac plot (Fig 3B), the three BRE replicates segregated apart from the remaining lines (Principal component 1, explaining 73.78% of the variation). The second axis (principal component 2, explaining 11.36% of the variation) showed some small differences across replicates for the lines (see Fig 3B for details). The UPGMA clustering analysis with Jackknife support (S3 Fig) showed a similar pattern and segregated the BRE_3 replicate from other samples. The fact that BRE_3 replicate clustered apart from the remaining two is likely due to differences in taxonomic resolution of the sequences: BRE_3 had a high abundance (i.e. 45%) of sequences taxonomically affiliated to the bacterial genus Kurthia, from the Planococcaceae family. While the remaining two BRE replicates also had a high number of sequences affiliated to the family Planococcaceae (Fig 1B), in the case of BRE_1 and BRE_2 the Planococcaceae sequences could not be classified up to the genus level.

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Fig 3. Beta-diversity of bacterial communities associated with D. melanogaster lines.

Bacterial communities are clustered using PCoA of (a) unweighted and (b) weighted UniFrac. The ellipsoid shapes indicate interquartile range (IQR). The percentage of the variation explained by the plotted principal components is indicated on the x- and y-axes. The following line name abbreviations were used: ARL, Arles; BAY, Bayreuth; BRE, Bremen; GOTH, Gotheron; GRO, Groningen; STA, St. Andrews.

https://doi.org/10.1371/journal.pone.0167726.g003

Resistance of D. melanogaster lines to A. tabida

To evaluate whether the microbiome had phenotypic effects on parasitoid resistance, we manipulated the microbiome by treating all lines for three generations with a mixture of antibiotics, followed by rearing the lines for two generations off antibiotics. The phenotypic response to antibiotic treatment differed among the lines (Fig 4). This is also reflected in the statistical analysis: a glm model comparing the effects of antibiotic treatment on resistance among all lines indicated a significant interaction between Line × Treatment (dispersion parameter for quasibinomial family = 3.44, F_5,157 = 3.563, p = 0.0045). A multiple comparison on how the encapsulation rate (ER) was affected differently among lines by antibiotic treatment showed that treated larvae from St. Andrews encapsulated eggs less efficiently (adjusted p = 0.0184), while treated larvae of the other lines did not show significant effects of antibiotic treatment on encapsulation. GOTH showed a trend towards increased encapsulation rate (ER) of the parasitoid egg compared to the control group, but this effect was no longer significant after correcting for multiple testing. The antibiotic treatment did not significantly affect pupa-to-adult mortality (dispersion parameter for quasibinomial family = 7.83, glm, F_1,154 = 1.8748541, not significant). All data and R-scripts of the parasitization experiment are added in the Supplementary Material (S4 Table, S1 File).

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Fig 4. Encapsulation rate (ER) in six D. melanogaster lines.

The circles represent the mean levels of encapsulation ability in antibiotic-treated (black) and control (white) Drosophila larvae (with standard errors). The encapsulation rate was measured as the proportion of larvae that successfully encapsulated a parasitoid egg. The following line name abbreviations were used: ARL, Arles; BAY, Bayreuth; BRE, Bremen; GOTH, Gotheron; GRO, Groningen; STA, St. Andrews.

https://doi.org/10.1371/journal.pone.0167726.g004

Connecting Drosophila resistance to bacterial diversity and abundance

In order to quantify Wolbachia and total bacterial loads in larvae from control and treated lines, we performed qPCR analysis. The relative abundance of 16S rRNA gene was not significantly different in the treated groups, compared to the control group, nor did it differ between the tested lines (Treatment: F_1,10 = 2.303, p = 0.160, line: F_2,10 = 1.345, p = 0.304, Fig 5). The qPCR data confirmed our sequencing results in showing that STA line had no Wolbachia infection. GOTH and GRO of the control groups did not differ in their Wolbachia load according to our qPCR results (F_1,6 = 0.459, p = 0.523). An additional qPCR analysis on the remaining three lines (BRE, BAY and ARL) did show differences in Wolbachia load among these lines (S4 Fig). After treatment with antibiotics, all lines either lacked (GOTH, GRO, STA, BRE) or had a significantly lower (ARL, BAY) Wolbachia load (Fig 5B, S2B Fig).

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Fig 5. Wolbachia and total bacterial load in three D. melanogaster lines.

Relative abundance of bacterial 16S rRNA gene (a) and gatB (Wolbachia) (b) genes of control (grey) and antibiotic treatment (white) groups in GOTH (Gotheron), GRO (Groningen) and STA (St. Andrews) lines. The values are normalized against TATA-binding protein (Tbp), and standard deviations are shown.

https://doi.org/10.1371/journal.pone.0167726.g005

To verify whether the bacterial communities were altered by the antibiotics treatment and to search for the possible bacterial taxa responsible for the change in resistance, we performed additional sequencing. We sequenced bacterial communities of GOTH and STA lines (control and antibiotic treatment groups), based on the effects antibiotic treatment had on their resistance. We obtained four clone libraries for STA and GOTH (one per control and treatment groups), comprising 87 sequences.

Although the total bacterial load of the antibiotic-treated larvae was the same as in the control groups (Fig 5A, S4A Fig), the composition of the microbiome was considerably altered (Fig 6). The sequencing data for GOTH and STA control and antibiotic treatment groups showed that the bacterial communities were dominated by the OTUs belonging to the families Acetobacteraceae and, to a lesser extent, Lactobacillaceae (Fig 7). The UniFrac PCoA grouped the antibiotic-treated samples together (Fig 6), while the control groups of STA and GOTH differed from each other, consistent with the earlier presented microbiome characterization results for all the lines. There was no clear association between any particular OTU and resistance levels against parasitoids.

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Fig 6. Beta-diversity of bacterial communities associated with two control and two antibiotic-treated lines of D. melanogaster.

Bacterial communities are clustered using PCoA of (a) unweighted and (b) weighted UniFrac. The percentage of the variation explained by the plotted principal components is indicated on the x- and y-axes. Antibiotic treatment and control groups are indicated as letters, ‘t’ and ‘c’ respectively, next to the line abbreviation: GOTH, Gotheron; STA, St. Andrews.

https://doi.org/10.1371/journal.pone.0167726.g006

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Fig 7. Relative abundance of bacterial taxa in two D. melanogaster lines, Gotheron (GOTH) and St. Andrews (STA).

The relative abundance of taxa was based on taxonomic affiliation of the bacterial 16S rRNA gene fragment. Antibiotic treatment (t) and control (c) treatment are indicated next to the line abbreviation.

https://doi.org/10.1371/journal.pone.0167726.g007

Discussion

The first aim of this study was to determine whether host population background affects the microbiome composition. Our results show that six D. melanogaster lines derived from natural populations differ in their composition and diversity of the bacterial communities, despite being kept on the same standard diet for four consecutive years. This contradicts the earlier findings that D. melanogaster microbiome is mainly shaped by diet [21,23] and confirms the idea that hosts can either maintain or acquire different bacterial communities [27]. We observed that 2 lines out of 6 tested had distinct and characteristic microbial communities: one relatively species-poor and lacking the common endosymbiont Wolbachia (STA), and one relatively species-rich with many unique OTUs (BRE). The other 4 lines (GOTH, BAY, ARL, and GRO) shared intermediate species-richness and more similar microbiomes. The findings in this study show that there is little evidence for a core microbiome in D. melanogaster, as was also previously suggested elsewhere [21].

What caused the differences among lines in microbiome composition is not solved in our study. Possibly the hosts exercised a certain degree of control over the associated microbiome [23,27,51], and host population genetic differences influenced the microbiome composition [28]. Differences in the host microbial communities could be associated with genetic and physiological differences in the host. The microbiome can be sensitive to slight variations in pH level and availability of oxygen in the gut, or to genetic differences in the host immune system [12,52]. For Hydra, a genus of freshwater Cnidarian animals, it was demonstrated that the diverse microbial communities in different species were determined by the differential expression of antimicrobial peptides by the host [51]. Similar to the Drosophila-microbiome system, these various Hydra species do not have an obligate association with their bacteria, and yet are capable of maintaining a consistent host-specific microbiome.

Alternatively, the observed specificity of the Drosophila microbiome among the various lines may arise from the original collections from natural populations. Possibly, the microbiome differences originate from these natural populations, and then persisted over the many generations that the flies were cultured in the lab. Diet is unlikely to have caused the differences, as all lines were reared on identical medium and under identical conditions for over four years. Environmental microbes that grew on the medium are also unlikely to have caused the observed differences among lines, because the medium was heat-sterilized, prepared in batches and distributed among all samples simultaneously. When the founders of the lines had different microbiomes and transmitted these to successive generations, this could provide support for stable and heritable associations of microbiomes, which is one of the requirements for the hologenome theory of evolution. This theory postulates that when the host can maintain a prolonged association with its microbiome, together they form one unit—a holobiont——upon which selection can act [2].

In the ongoing debate about what determines the composition of the Drosophila microbiome (diet versus taxonomy versus random events), it is now clear that various factors play a role and that their relative influences vary across sample types (e.g. instar stages, population background, etc). To fully elucidate the contributing factors would require extensive and carefully designed follow-up experiments, including the characterization of the microbiome of the founders of each line, cross-exposure to the microbiome of different lines, extensive replication within lines (to determine the degree of stochasticity), and laboratory experiments to determine the degree of selection from diet and external conditions. The relative importance of each of the factors probably depends on hosts’ biology, and the role that the microbiome plays in its evolution. Maintaining bacteria that enhance host survival and/or reproduction can give a population a fitness advantage and promote its success. Therefore, the genetic (e.g. immunity, physiology) or ethological (e.g. food preference) mechanisms that promote the growth of the beneficial bacteria can evolve. We already know some of these mutualistic bacteria: for instance, Lactobacillus plantarum and acetic acid bacteria enhance growth and starvation resistance [53], exogenous bacteria colonizing the fly’s body surface can contribute to longevity and pheromonal communication among flies [17,19] and Wolbachia can enhance stem cell proliferation [54].

The second aim of this study was to investigate the effect of the established microbiome of D. melanogaster on parasitoid resistance. The natural variation among D. melanogaster field lines in their resistance to parasites [30,31] and pathogens [26] has been a hallmark for substantial genetic variation in immunity genes. Importantly, our data suggests that an alternative hypothesis also needs to be considered for the natural variation in parasite resistance: perhaps is not only (directly) related to genetic variation among the lines, but it may also be (indirectly) related to the variation in host-microbiome interactions [55].

The ability of the lines to resist the parasitoid A. tabida was affected in at least one of the lines when treated with antibiotics. After the antibiotic treatment, we quantified and characterized both Wolbachia and the total bacterial community abundance and composition. From that, we showed that the manipulation of the microbiome was successful, as demonstrated by substantial shifts in the microbiome of treated flies. Treated flies had not gone through a bottleneck, nor experienced any strong mortality during the antibiotics treatment, which implies that alternations in the host population genetic composition are not to be expected. The observed effects of antibiotic-treatment on parasitoid resistance, therefore, were most likely due to the shifts in the microbiome. While this suggests the influence of the microbiome on parasitoid resistance, only the reintroduction of different OTUs or complete microbiomes could provide a valuable and definitive proof.

We showed that different Drosophila lines responded differently to the antibiotic treatment.—STA decreased in their resistance while the other lines did not respond to treatment, with the exception of GOTH, which showed a trend towards increased resistance in response to antibiotic treatment. This finding supports the hypothesis that various bacterial OTUs can have a different function depending on the host genotypic background and/or the total microbiome composition. Therefore removal or acquisition of a certain bacterial taxon could lead to the opposite phenotypic effect (or no effect) in different Drosophila populations. For instance, one microbial genus found in Drosophila, i.e. Clostridium, can modulate host immune response in vertebrates by promoting T_reg cell accumulation [56]. Moreover, the genus Wolbachia had been reported to enhance Drosophila resistance to viruses [13], and both Wolbachia and Spiroplasma can affect resistance to parasitoids [10,15]. Most studies used an antibiotic treatment to remove endosymbionts followed by a resistance assay. However, we re-emphasize that total bacterial community changes after antibiotic treatment, so it may not be justified to assume that Wolbachia or any OTU alone is the cause of the change in the host phenotype [57]. Importantly, in our study, we observed significant changes in the resistance after the antibiotic treatment in the STA line that naturally lacks Wolbachia infection. Therefore, we conclude that the intracellular endosymbiont was not the cause of the observed phenotype, i.e. the changed ability to resist the parasitoid.

Although antibiotic treatment itself can also have a negative effect on the host (e.g. [58]), we argue that this was not the case in our study. By keeping the fruit fly cultures off the antibiotics for two generations, we eliminated or reduced the possible negative effect of the treatment on host physiology, and allowed the reestablishment of an altered microbiome, as demonstrated by the recovery in bacterial abundance. This step is often neglected in studies (e.g. [7]), but could jeopardizes the findings when it becomes impossible to make a distinction between the effects of the two factors—stress caused by the antibiotic treatment and the removal or alteration of bacterial types.

We determined resistance to parasitoids based on the flies carrying a melanotic encapsulated parasitoid egg. This measure of resistance ensures that larvae had been parasitized and had successfully defended themselves against the parasitoid. Adult flies that did not carry a capsule were considered to be un-parasitized. In other species of Drosophila, larvae can resist parasitoids without forming melanotic capsules (e.g. [14, 15, 59]), which could imply that basing our resistance measure on the presence of visible capsules could underestimate the true level of resistance. In these other Drosophila species, however, melanotic capsules have never been found, which reflects the independent evolution of different defense strategies in the Drosophila phylogeny [14, 15, 59]. In experiments in which we confirmed parasitizations by behavioural observations, we unambiguously find that larvae that survive parasitization carry visible capsules as adults. We are therefore confident that in D. melanogaster, the only defense mechanism to survive parasitoid attack relies on melanotic encapsulation.

Finally, in our resistance assays, we observed a relatively high mortality rate among tested Drosophila larvae—something that has to be addressed in the future experimental setups. Possibly, the high mortality was caused by super-parasitism, when larvae were parasitized more than once during the assay. We did observe multiple melanotic capsules in several of the adult flies, indicating that superparasitism had indeed occurred. The high mortality may also have led to some slight shifts in ranking of these six lines in terms of resistance, compared to our earlier measurements for these same lines [31]. The high mortality was similar, however, between the antibiotic and control groups, suggesting that it is unlikely that it has caused the observed patterns of altered resistance after antibiotic treatment.

Conclusions

Our results revealed pronounced differences in the microbiome of genetically differentiated D. melanogaster lines. Since the tested lines have been maintained on an identical diet for four years, our finding provides an argument against the widely accepted view that diet is the key determinant in Drosophila-microbiome system. Our data clearly shows that host population background is an important factor determining bacterial community composition. The question remains, however, what it is in the hosts’ background that caused this strong effect. Part of the variation among lines could be caused by genetic variation (e.g. in immunity and/or gut physiology), and/or part of it may reflect the composition of the microbiome that the founders of these lines acquired in their native environments. Irrespective of which factors caused the microbiome differences in our study, an important implication from our research is that studies that are performed on multiple strains or lines may not only reflect their differences in genotype, but also in the composition of their microbiome, even when these lines were reared in the same laboratory under standardized conditions for many years. Moreover, our data highlights the importance of the Drosophila microbiome in shaping host resistance to parasitism, pointing out that Wolbachia is not the only determinant of this host phenotype.

Supporting Information

Acknowledgments

We thank W-J. Ma for kindly providing the A. tabida strain; R. Houwerzijl, A. Rensink, A. de Haan, M. C. Pereira e Silva and S. Grizard for help with the experiments; A. Paspati for helpful comments on the qPCR experiment; RuG Evolutionary Genetics and Microbial Ecology groups for fruitful discussions; two anonymous referees for their constructive comments on an earlier draft of the manuscript.