Skip to main content
Download PDF

Entomopathogenic nematode-associated microbiota: from monoxenic paradigm to pathobiome

Microbiome volume 8, Article number: 25 (2020) Cite this article

Abstract

Background

The holistic view of bacterial symbiosis, incorporating both host and microbial environment, constitutes a major conceptual shift in studies deciphering host-microbe interactions. Interactions between Steinernema entomopathogenic nematodes and their bacterial symbionts, Xenorhabdus, have long been considered monoxenic two partner associations responsible for the killing of the insects and therefore widely used in insect pest biocontrol. We investigated this “monoxenic paradigm” by profiling the microbiota of infective juveniles (IJs), the soil-dwelling form responsible for transmitting Steinernema-Xenorhabdus between insect hosts in the parasitic lifecycle.

Results

Multigenic metabarcoding (16S and rpoB markers) showed that the bacterial community associated with laboratory-reared IJs from Steinernema carpocapsae, S. feltiae, S. glaseri and S. weiseri species consisted of several Proteobacteria. The association with Xenorhabdus was never monoxenic. We showed that the laboratory-reared IJs of S. carpocapsae bore a bacterial community composed of the core symbiont (Xenorhabdus nematophila) together with a frequently associated microbiota (FAM) consisting of about a dozen of Proteobacteria (Pseudomonas, Stenotrophomonas, Alcaligenes, Achromobacter, Pseudochrobactrum, Ochrobactrum, Brevundimonas, Deftia, etc.). We validated this set of bacteria by metabarcoding analysis on freshly sampled IJs from natural conditions. We isolated diverse bacterial taxa, validating the profile of the Steinernema FAM. We explored the functions of the FAM members potentially involved in the parasitic lifecycle of Steinernema. Two species, Pseudomonas protegens and P. chlororaphis, displayed entomopathogenic properties suggestive of a role in Steinernema virulence and membership of the Steinernema pathobiome.

Conclusions

Our study validates a shift from monoxenic paradigm to pathobiome view in the case of the Steinernema ecology. The microbial communities of low complexity associated with EPNs will permit future microbiota manipulation experiments to decipher overall microbiota functioning in the infectious process triggered by EPN in insects and, more generally, in EPN ecology.

Background

Host-microbe symbioses, which range from mutualistic to pathogenic interactions, are ubiquitous in both marine and terrestrial environments. The host microbiota and its variations have recently been shown to be important for the outcome of interaction between symbiotic microbes and plants [1], vertebrates [2] or invertebrates [3]. When symbiotic microbes are transmitted by macro-organism hosts, symbiont dispersion may also be influenced by the cohort of micro-organisms carried by the host [4, 5]. If we are to understand host-microbe interactions, we must therefore consider the cohort of micro-organisms associated with the host.

The pathobiome is defined as the pathogenic agent together with its overall microbial environment, which contributes to pathogenesis [6]. Within the context of mammalian gut microbiota, this concept leads to the definition of several new categories on the commensal-to-pathogen spectrum: accessory pathogens, which act synergistically, under certain conditions to enhance the virulence of the main pathogenic bacteria and pathobionts, which are generally benign within the indigenous community and become pathogenic when homeostasis is disrupted [7].

Invertebrates rely on symbionts for many of their life history traits [8, 9]. Moreover, their microbial communities can be analysed with methodologies that cannot be used on vertebrates. Invertebrates are therefore the favoured models for explorations of the pathobiome concept. For instance, experimental inoculations or transverse transplantations of gut microbiota in honey and bumble bees have been shown to lead to changes in susceptibility to the protozoan Lotmaria passim [10] and the trypanosomatid parasite Crithidia bombi [11]. In the lepidopteran Spodoptera littoralis, the silencing of host immune pathways by RNAi revealed that Serratia and Clostridium species in the gut microbiota switched from asymptomatic symbionts to haemocoel pathogens during Bacillus thuringiensis infection [12].

Invertebrates may themselves be parasites. The infectious process results not only from their own virulence, but also from their cohort of associated micro-organisms. Many examples are provided by parasitic helminths and nematodes in particular [13]. For instance, the obligate endosymbiont Wolbachia plays a key role in the pathogenesis of the filarial nematodes responsible for lymphatic filariasis and river blindness [14]. Other emblematic nematode-bacterium associations include those between entomopathogenic nematodes Heterorhabditis and Steinernema and the ϒ-Proteobacteria Photorhabdus and Xenorhabdus, which inhabit their gut. The bacteria are involved in both insect killing and the lifecycle of the nematodes [15].

Steinernema and its intestinal symbiotic bacterium, Xenorhabdus, have long constituted a model system for investigating pathogenesis and mutualism [16,17,18]. They establish a sustainable association in lifecycles with three main steps [16,17,18]. The infective juveniles (IJs) of Steinernema are the free soil-dwelling forms of the nematode harbouring the symbiotic bacteria in their gut (step 1). On encountering a living insect larva, the IJ enters the insect gut and perforates the insect gut wall, which allows the injection of the Xenorhabdus symbiont into the insect hemocoel (body cavity) before the passage of the IJ through the insect midgut [19]. In the hemolymph (insect blood), Xenorhabdus multiplies and subsequently kills the insect (step 2). The penetration of IJs also permits insect gut microbiota translocation and proliferation into the insect hemocoel [20]. Within the insect cadaver (step 3), Xenorhabdus bacteria contribute to nematode maturation and reproduction, by degrading the insect tissues, feeding the nematode and outcompeting soil micro-organisms for nutrient acquisition in the insect cadaver. Once the nutrient resources are depleted, new IJs are produced, which specifically re-associate with their cognate Xenorhabdus symbiont and return to the soil (step 1).

The Steinernema IJs, which are the vector of Xenorhabdus between insect hosts, have a number of unusual features. They are non-feeding third-stage larvae enclosed within the second-stage cuticle, which closes the intestinal orifices (mouth and anus) and is lost only when the nematodes reach the insect gut [19, 21, 22]. The Xenorhabdus symbiont is carried in a dedicated part of the anterior intestine of the IJ, known as the receptacle [23,24,25]. Observations of these anatomical and histological aspects led to the idea of natural monoxeny in the Xenorhabdus-Steinernema symbiosis [26]. However, several culture-based studies have reported associations of bacteria other than the cognate Xenorhabdus symbiont with Steinernema IJs [26,27,28,29,30,31,32]. Unidentified bacteria have been observed in the intercellular space between the second- and third-stage cuticles [26].

Community profiling has progressed considerably over the last 10 years, making it possible to circumvent some of the limitations of culture-based methods (see for example [33,34,35]). These methods have been used to describe the microbiota of the free soil-dwelling nematodes Acrobeloides maximus and Caenorhabditis elegans [36,37,38]. Next-generation sequencing has recently been used to profile bacterial community dynamics in the cadavers of insect larvae infected with the entomopathogenic nematode Heterorhabditis, but the bacterial community associated with the IJs has not been extensively studied [39], and such approaches have never been used on Steinernema.

In this study, we investigated the hypothetical monoxeny of the Xenorhabdus-Steinernema symbiotic interaction. We first used a metabarcoding approach to describe the microbial community associated with several Steinernema species reared in the laboratory. We then focused on the well-characterised S. carpocapsae nematode, for which we have access to diverse strains and multiplication batches from different laboratories. We provide the first demonstration that, in this model, bacteria other than the symbiont X. nematophila are sustainably associated with S. carpocapsae IJs. We validated the pertinence of this frequently associated microbiota (FAM) by performing metabarcoding analysis on IJs freshly isolated from their natural habitat, the soil. We isolated a large range of bacterial strains from the FAM and explored some of their possible functions in the parasitic lifecycle of Steinernema. Two of the species identified, Pseudomonas protegens and Pseudomonas chlororaphis, kill insects as efficiently as the cognate Xenorhabdus symbiont, suggesting that they may belong to the IJ Steinernema pathobiome.

Results

The IJs of Steinernema are living in soils but cannot directly be isolated from their habitat. They are first captured by ex situ Galleria trap (Fig. 1a). Once isolated, they are stored and multiplied on laboratory Galleria trap (Fig. 1b).

Fig. 1
figure 1

Ex situ isolation, storage and laboratory multiplication of Steinernema entomopathogenic nematodes. a Ex situ Galleria trap methodology. Soil was sampled at a depth of 0–20 cm and transferred to 1 L plastic containers. Five last-instar Galleria mellonella larvae were placed in each container (ex situ Galleria trap). The containers were stored in the dark at 18 °C. After 7 days, dead G. mellonella were placed in a White trap consisting of an overturned 35-mm Petri dish covered with a piece of material (in blue) and placed in a larger Petri dish containing Ringer solution. After the production of a few generations of nematodes in G. mellonella cadavers, the IJs left the cadaver and migrated into the Ringer solution via the wet material (black dashes in Ringer solution). b IJ storage and laboratory Galleria trap method. IJs emerging from Steinernema and Heterorhabditis species were stored in 250-mL flasks containing 80 mL of Ringer solution supplemented with 0.1% formaldehyde at 9 °C, and flasks containing 80 mL of Ringer solution supplemented with 0.01% formaldehyde at 15 °C, respectively. Every 6 months, stocks were multiplied by adding 50–100 IJs to Galleria larvae placed on filter paper in a Petri dish (laboratory Galleria trap). Once the insects had died, their cadavers were placed in a White trap, as described above. Multiplication batches were labelled with the infestation date

Full size image

Optimization of a metabarcoding pipeline for molecular description of the bacterial microbiota of Steinernema IJs reared in laboratory

DNA was extracted from an IJ nematode sample (S. carpocapsae SK27 batch 23_08_16; Additional file 1) from our laboratory stock, from controls (Kitome_QE, Kitome_MN, Tap water and Ringer) and from Galleria larvae. Amplification and sequencing of the V3V4 region of the 16S rRNA gene generated a total of 1,907,256 reads (mean of 45,411 reads per sample; number of samples = 42). Based on rarefaction curves, an adequate sequencing depth was reached at 15,000 reads per sample (Additional file 2). α-Diversity indicators for the bacterial communities associated with the IJs, Galleria and control samples highlighted the low diversity of the bacterial communities within the different samples (Additional file 3).

The community structure observed differed significantly between IJs, Galleria and control samples (Additional file 4). The IJ sample and control samples contained mostly Proteobacteria, including many members of the Burkholderiaceae. However, only one OTU, affiliated to the Sphingomonas genus, was common to both the control and IJ samples (Fig. 2a). As previously reported in different Lepidoptera [20, 39, 40], the Galleria samples consisted mainly of Firmicutes, and an OTU affiliated to the genus Enterococcus was consistently detected (Additional file 4). No OTU common to the IJ and Galleria samples was identified (Fig. 2a). We therefore (i) removed the OTUs affiliated to the genus Sphingomonas from subsequent analysis and (ii) concluded that the multiplication of IJs in Galleria larvae does not influence the Steinernema microbiota. In each replicate of the IJ samples, the OTU identified as X. nematophila, the S. carpocapsae symbiont, was one of the 30 most abundant OTUs (Top30), but other OTUs were also present (Fig. 2b), demonstrating the association of a bacterial community much more complex than the symbiont with the IJ samples.

Fig. 2
figure 2

Bacterial communities associated with IJs of S. carpocapsae SK27, Galleria mellonella larvae and control samples. The IJ microbiota was investigated by metabarcoding with the V3V4 region of the 16S rRNA gene. a Venn diagram of the OTU detected in of S. carpocapsae SK27 batch SK27_23_08_16 samples, G. mellonella samples and control samples (Kitome_QE, Kitome_MN, Tap water and Ringer). OTUs (frequency cut-off > 80% and abundance cut-off > 0.01%) were assigned to genus level. Non-core set embeds OTUs that are not shared by all the control, the Galleria or the Steinernema replicates. b Heatmap showing the composition of the microbiota of S. carpocapsae SK27 batch SK27_23_08_16 samples. Each column represents a technical replicate. The 30 most abundant OTUs across the samples at the genus level of affiliation (Top30 Genus) are listed on the left. The percentage relative abundance is indicated by the gradient of blue hues.

Full size image

In metabarcoding analysis based on the V3V4 region of the 16S marker, only 57 and 12% of the OTUs could be associated with a genus or species name, respectively (Additional file 5). For refinement of the taxonomic assignment of OTUs, we used the rpoB marker, a 435-bp region of the housekeeping rpoB gene [41]. The sequencing of rpoB marker amplicons generated 96,825 reads (mean = 16,137 reads per sample; number of samples = 6). Rarefaction curves generated on quality-filtered reads indicated that sequencing depth was sufficient (Additional file 2). As previously observed [41], the rpoB marker gave a better taxonomic assignment, with 56% and 32% of the OTUs associated with a genus or species name, respectively (Additional file 5). Based on these results, we pursued the two-marker approach.

Microbial community patterns segregate according to the Steinernema species

Are IJ microbiota composition and richness influenced by the host species? We tested this hypothesis by describing the microbial communities associated with IJ from different Steinernema species (Fig. 3a). As an outgroup of the genus Steinernema [42], we included Heterorhabditis bacteriophora TT01, another entomopathogenic nematode harbouring symbiotic Photorhabdus luminescens bacteria. After quality filtering, we were left with a mean of 21,635 sequences per sample (n = 45) corresponding to 317 OTUs for the V3V4 marker, and 14,935 sequences per sample (n = 34) corresponding to 241 OTUs for the rpoB marker. Rarefaction curves confirmed that sequencing coverage was adequate (Additional file 2). Alpha diversity was similar for the two markers (Additional file 3). The community structure found in the various IJs (Fig. 3b and Additional file 6) differed significantly between the outgroup H. bacteriophora and Steinernema samples (Permanova V3V4, Df = 1, R2 = 0.28, p value = 10−4; Permanova rpoB, Df = 1, R2 = 0.38, p value = 10−4), regardless of the marker considered, but also between the Steinernema species (Permanova V3V4, Df = 3, R2 = 0.37, p value = 10−4; Permanova rpoB, Df = 3, R2 = 0.59, p value = 10−4). As previously shown for S. carpocapsae SK27_batch 23_08_16, Proteobacteria were prominent members of the IJ microbiota (Fig. 3c and Additional file 6). Microbial patterns seem to be correlated to the nematode host’s phylogenic tree (Fig. 3a), suggesting co-evolution of the microbiome and its host. Further analyses including a larger number of strains would be necessary to confirm this phylosymbiosis signal.

Fig. 3
figure 3

Bacterial communities associated with IJs from different species of Steinernema. a Simplified phylogeny of Steinernema. Phylogenetic relationships between 16 Steinernema strains based on a maximum likelihood (ML) analysis of partial internal transcribed spacer (ITS) regions (~ 850 bp). Heterorhabditis bacteriophora was used as outgroup. Branch support values (estimated by the aLRT [SH-like] method) are shown at the nodes (percentages of 100 replicates). The branch length scale bar below the phylogenetic tree indicates the number of nucleotide substitutions per site. The accession numbers of the sequences are indicated after the names of the nematodes. The sequences of strains used for metabarcoding studies are shown in colour. b Principal co-ordinates analysis (PCoA) based on Bray-Curtis distances for the IJ microbiota. Heterorhabditis and Steinernema samples are indicated by circles and triangles, respectively. Colours indicate the different Steinernema species. Each point represents a technical replicate. The proportion of the variance explained by each axis is shown. The five samples are significantly different (Permanova rpoB, Df = 4, R2 = 0.73, p value = 10−4). c Heatmap showing the microbiota composition of IJ samples. Each column represents an IJ species. The 30 most abundant OTUs across the samples at the genus level of affiliation (Top30 Genus) are listed on the left. The percentage relative abundance is indicated by the gradient of blue hues. For b and c, IJ microbiota were described by metabarcoding with the 435 bp rpoB region. The EPN strains used here belong to the following batches: S. carpocapsae SK27_23_08_16 and B10_27_04_16; S. weiseri 583_09_06_15, TCH02_11_08_16 (t1), S. glaseri SK39_09_06_15; S. feltiae FRA200_09_06_15 and FRA200_12_08_15 and H. bacteriophora TT01_22_06_16 and TT01_15_03_16. Three to six technical replicates per batch were performed

Full size image

A bacterial community is frequently associated with S. carpocapsae IJs

Is there a core community associated with the laboratory-reared Steinernema species? We explored this question by focusing on S. carpocapsae species and comparing the community composition of IJs from six S. carpocapsae strains reared in the laboratory for several years (Additional file 1). SK27 (from four different multiplication batches), B10, EGY03, CREA, All_DGIMI and DD136_DGIMI were all maintained in our laboratory (DGIMI, Montpellier, France). All_USDA and DD136_USDA were maintained at the USDA-ARS, Fruit and Tree Nut Research Unit at Byron, Georgia, USA. The community structure of the various S. carpocapsae samples (Fig. 4 and Additional file 7) revealed that strain, multiplication batch and storage laboratory accounted for a significant proportion of the variance of IJ microbiota (Permanova rpoB_strains, Df = 5, R2 = 0.87, p value = 10−4; Permanova V3V4_strains, Df = 5, R2 = 0.75, p value = 10−4; Permanova rpoB_batch, Df = 3, R2 = 0.75, p value = 10−4; Permanova V3V4_batch, Df = 3, R2 = 0.74, p value = 3.10−4; Permanova rpoB_laboratory, Df = 1, R2 = 0.25, p value = 10−4; Permanova V3V4_laboratory, Df = 1, R2 = 0.28, p value = 10−4). Likewise, when we compared the IJ microbiota of the American strains on reception at our laboratory (All_USDA_t0 and DD136 USDA_t0) and after just one round of multiplication in Galleria larvae in our laboratory (All_USDA_t1 and DD136 USDA_t1), we found that a quarter of the variance depended on batch (t0 versus t1 in Fig. 4c and Additional file 7) (Permanova V3V4, t0 versus t1, Df = 1, R2 = 0.27, p value = 10−4; Permanova rpoB, t0 versus t1, Df = 1, R2 = 0.24, p value = 2.10−4). Our results thus suggest that the rearing and storage of IJs in the laboratory lead to a drift.

Fig. 4
figure 4

Bacterial communities associated with different samples from the species S. carpocapsae. Principal co-ordinate analysis (PCoA) based on Bray-Curtis distances for IJ microbiota obtained by metabarcoding with the 435-bp rpoB region. Each point represents an individual sample replicate. The different strains, multiplication batches and origins of S. carpocapsae are indicated by colours and symbols. The proportion of variance explained by each axis is shown. a Comparison of six S. carpocapsae strains. The six samples are significantly different (Permanova, Df = 5, R2 = 0.87, p value = 10−4). b Comparison of four multiplication batches of S. carpocapsae SK27. The four samples are significantly different (Permanova, Df = 3, R2 = 0.75, p value = 10−4). c Comparison of two laboratory origins of S. carpocapsae DD136 and S. carpocapsae All (Permanova: DD136 versus All, Df = 1, R2 = 0.12, p value = 10−4; DGIMI versus USDA, Df = 1, R2 = 0.25, p value = 10−4)

Full size image

Despite these variations and regardless of the marker used, we detected recurrent OTUs in addition to the cognate symbiont X. nematophila among the 30 most abundant OTUs of the whole set of S. carpocapsae samples (Additional file 8). For each abundant OTU (read number > 0.1% of the sample read), we represented the frequency of abundance as a function of frequency (Fig. 5). For OTUs with a high occurrence (present in at least 70% of the samples), we identified two groups. In the first group, the X. nematophila OTU was always abundant (present in at least 90% of the samples), confirming its status as the core symbiont of S. carpocapsae (Fig. 5). In the second group, the OTUs were abundant in less than 90% of the samples. We named this set of OTUs the “frequently-associated microbiota” (FAM). After removal of the OTU found in the controls (Sphingomonas), the FAM common to the two markers encompassed the genera Alcaligenes, Stenotrophomonas, Pseudomonas and the Rhizobiaceae family (Fig. 5).

Fig. 5
figure 5

Occurrence (x axis) and frequency of high abundance (y axis) of OTUs associated with laboratory-reared S. carpocapsae IJs. Abundant OTUs with read numbers accounting for more than 0.1% of the total reads for the sample were plotted. The darker the dot, the more OTUs with these characteristics are found at the position concerned. Among the OTUs with high occurrence rates (present in more than 70% of the samples), the yellow and orange areas delimit two groups. Orange group: frequency of high abundance > 90% (core symbiont). Yellow group: frequency of high abundance < 90% (frequently associated microbiota or FAM). OTU identity is indicated to the right of the graph (bootstrap confidence ≥ 0.9). *OTU present in the control samples

Full size image

We checked for the FAM of laboratory-reared S. carpocapsae in the IJs of S. carpocapsae GRAB recently sampled from soils by the ex situ Galleria trap method (Fig. 1a) in an apple orchard in November 2017 (Additional file 1). Metabarcoding analysis with both V3V4 and the rpoB markers was performed on GRAB IJs shortly after trapping (only one round of multiplication in Galleria larvae). Bacterial diversity was higher than that in nematode lineages multiplied for long periods in the laboratory, but both markers identified the core symbiont Xenorhabdus nematophila and the FAM members Stenotrophomonas, Pseudomonas and Rhizobiaceae, but not Alcaligenes, suggesting that the identification of this last taxon might be due to a bias caused by laboratory rearing (Additional file 9).

Bacteria associated with S. carpocapsae IJs are cultivable

We performed a large survey of the isolation on nutrient-rich culture media of bacteria from ground S. carpocapsae IJs or from the content of G. mellonella cadavers infested with S. carpocasae IJ. We isolated only Gram-negative taxa. We regularly sampled isolates from the following eight genera: Brevundimonas, Ochrobactrum, Pseudochrobactrum, Achromobacter, Alcaligenes, Stenotrophomonas, Xenorhabdus and Pseudomonas (Additional file 1 and Fig. 6). These taxonomic groups matched with the OTUs identified by metabarcoding in 70% of the S. carpocapsae samples with one of the two markers (Fig. 5). Bacterial isolation on rich agar medium cannot be used for the quantification of taxa. However, for some samples, Xenorhabdus colonies were less abundant on NBTA plates than the other taxa, such as Stenotrophomonas, Alcaligenes and Pseudomonas. Culture-dependent approaches thus validate the S. carpocapsae FAM identified by the molecular approach.

Fig. 6
figure 6

Distance phylogenetic tree of 62 bacterial isolates from S. carpocapsae IJs. The phylogenetic tree of taxa isolated from S. carpocapsae IJs was constructed with the 16S rRNA gene sequences (1377 nucleotides), with the Kimura two-parameter model [43] and the neighbour-joining method [44] included in SeaView 4.7 software. Bootstrap values (percentages of 1000 replicates) of more than 90% are shown at the nodes. Twelve type strains (in bold) of the Xenorhabdus, Pseudomonas, Stenotrophomonas, Alcaligenes, Ochrobactrum, Pseudochrobactrum, Achromobacter and Brevundimonas genera were added. The bar represents 1% sequence divergence

Full size image

Investigation of FAM functions potentially involved in the parasitic lifecycle of S. carpocapsae

The ability of S. carpocapsae to kill insects is thought to be due to the presence of the symbiont X. nematophila [18]. However, other bacteria associated with the S. carpocapsae IJs might also contribute to entomopathogenicity. We assessed the pathogenicity of several of the bacteria frequently associated with the S. carpocapsae IJs. When 102–103 CFU/mL were injected into larvae of the lepidopteran insect Spodoptera littoralis, the highly pathogenic symbiont Xenorhabdus nematophila XnSc_F1 and Pseudomonas protegens strain PpSc_PP-SC-10, both isolated from S. carpocapsae SK27, took similar time to kill 50% of the larvae (LT50): 27 and 29 h, respectively (S. carpocapsae microbiota in Table 1). The larvae were not killed by other bacteria from the S. carpocapsae FAM (S. maltophilia, Alcaligenes faecalis, Pseudochrobactrum, Ochrobactrum), even at doses of 105–106 CFU/mL.

Table 1 Lethal time 50 values (LT50) for Spodoptera littoralis following the injection of bacterial strains into the haemocoel of last-instar larvae (incubated at 23 °C)
Full size table

It is also generally asserted that X. nematophila outcompetes other bacterial strains or species during its lifecycle, particularly during the infestation phase of insect cadavers [20, 45]. We therefore monitored the in vitro antibiosis of the X. nematophila XnSc_F1 strain against the cultivable bacterial members of the S. carpocapsae microbiota. Only two of the six bacteria isolated (Alcaligenes faecalis Alcf_SC and Ochrobactrum sp. OchSc_ALL4) displayed slight susceptibility to the production of antimicrobials by X. nematophila XnSc_F1 (Additional file 10). By contrast, five of these six bacteria were susceptible to the antimicrobial production of P. protegens PpSc_PP-SC-10 (Additional file 10).

P. protegens and P. chlororaphis are important functional members of the Steinernema FAM

During this study, we also recovered a panel of bacterial strains from IJs of three other entomopathogenic nematodes: S. glaseri SK39 and S. weiseri 583 that carried the symbionts X. poinarii XpSg_G6 and X. bovienii XbSw_CS03, respectively, and H. bacteriophora TT01 that carried the symbiont the symbiont P. luminescens PholHb_TT01. X. poinarii XpSg_G6 and X. bovienii XbSw_CS03 were previously reported to be attenuated in virulence [46,47,48], while P. luminescens PholHb_TT01 is highly virulent [49]. As in S. carpocapase, bacterial isolates from Steinernema IJs were generally more susceptible to the antimicrobials produced by P. protegens or P. chlororaphis than those produced by Xenorhabdus (Additional file 10). We assessed the pathogenicity of these bacterial isolates by injection in S. littoralis larvae (Table 1). For isolates from S. glaseri SK39 and S. weiseri 583, only P. protegens and Pseudomonas chlororaphis strains were able to kill the insect larvae (Table 1). They had LT50 values between 19 and 29 h, in the same range as the values obtained with reference strains isolated from rhizosphere environments, P. protegens CHAOT and P. chlororaphis CFBP2132T [50,51,52,53] (Table 1 and Fig. 7).

Fig. 7
figure 7

Survival curves of the insect Spodoptera littoralis after the direct injection of IJ-associated Pseudomonas protegens and Pseudomonas chlororhaphis. IJ-associated Pseudomonas: P. protegens strains PPSg_SG6 APO, PpSw_SW4, PpSw_TCH07 2-2, PpSc_PP-SC-10 and P. chlororaphis strain PcSg_SK39 ApoA. Rhizosperic strains: P. protegens CHAOT, P. chlororaphis CFBP2132T. Positive controls: X. nematophila strain XnSc_F1 (bold purple curve). Negative control (bold red curve): E. coli CIP7624. We injected 102 or 103 bacterial cells in the exponential growth phase and injected each of 20 last-instar larvae

Full size image

Discussion

This exploration of the monoxenic paradigm for the Xenorhabdus-Steinernema symbiotic interaction provided us with an opportunity to explore, for the first time, the bacterial microbiota associated with entomopathogenic nematodes of the genus Steinernema. To this end, we used two approaches to analyse a large panel of Steinernema infective stages. We first molecularly characterised the microbiota of four Steinernema species with two markers, the V3V4 region of the 16S rRNA gene and a 435 bp region of the rpoB gene, because multigene approaches have been shown to improve the reliability of bacterial community description in ecosystems of low complexity [41]. We also characterised the Steinernema microbiota by culture-based methods. This approach differed from that used in previous culture-dependent studies in the considerable effort made to isolate a large set of colonies (71, selected from more than 350 isolates, in this study) and to perform molecular taxonomic characterization on these colonies.

In the previous culture-dependent studies, questions were frequently raised about the source of the other bacteria than the symbiont found in Steinernema IJs. The authors often concluded that these taxa were contaminants [26], except for Ochrobactrum strains that were considered to be involved in a dixenic association with Photorhabdus in the EPN Heterorhabditis [54]. Indeed, as the isolated taxa are frequently detected in the healthy insect gut, in soils and in the haemolymph of insects infected with Steinernema [20, 32, 55, 56], it is generally assumed that the non-symbiotic bacteria randomly “hitchhike” in IJ vectors via the cuticle or intercuticular space and are introduced in the insect haemocoel during IJ penetration [27, 29].

Here, we clearly described the microbiota associated with Steinernema carpocapsae reared in laboratory as composed of the core symbiont, Xenorhabdus, and a frequently associated microbiota (FAM) consisting of cultivable Proteobacteria also identified in IJs freshly isolated from soil, their natural habitat. The only exception was Alcaligenes, which was detected only in laboratory-reared IJs. A large number of reads attributed to Alcaligenes was generally correlated with a high level of abnormal morphology (Ogier and Pagès, unpublished data). Poinar also described morphological and behavioural abnormalities in the nematode Heterorhabditis heliothis when IJs developed in presence of Alcaligenes faecalis [57], suggesting that a high abundance of this genus reflects drift due to laboratory rearing in batches of sick nematodes.

The Proteobacteria of the FAM are frequently encountered in the microbiota of organisms or compartments associated with soils. For instance, we detected taxa previously identified by culture-independent studies on other parasitic or soil nematodes. Pseudomonas has been detected and isolated from soil-dwelling forms of the nematode Pristionchus, a parasite of scarab beetles [58]. Stenotrophomonas, Ochrobactrum and Pseudomonas have repeatedly been identified in the soil-dwelling Acrobeloides maximus and Caenorhabditis elegans nematodes [36,37,38]. Likewise, Proteobacteria is the most abundant phylum in bacterial communities associated with plant roots [59, 60] as well as in plant-covered soils, such as the rhizosphere or litterfall [61, 62].

The composition of the Steinernema microbiota seems to be shaped by the ecological niche, but the question remains as to whether the establishment of the association between the bacterial community and the nematode is due to the chance alone or to specific mechanisms of association. In this study, we provide an array of arguments in favour of specific association with a larger microbiota than the core symbiont alone. First, Proteobacteria frequently encountered in the microbiota of organisms or compartments associated with soils were identified in nematodes that had been reared in the laboratory for years, suggesting that the nematode-microbiota association is robust enough to be sustained in non-natural (laboratory rearing in the non-natural host Galleria) and very different (France and USA) environments. Second, microbiota profiles distinguished between the genera Heterorhabditis and Steinernema, and also between the four Steinernema species studied here. Further studies are required to determine whether IJ microbiota composition actually reflects the entomopathogenic species phylogeny, as demonstrated for the gut bacteria of the wasp Nasonia [63], for coral-associated bacteria [64] and for insect viromes [65]. However, these preliminary data support the notion that some members of the Steinernema microbiota may be vertically (or pseudovertically) transmitted to the progeny, as reported for the core symbiont Xenorhabdus [66]. This would not rule out the possibility of hybrid transmission, with more “accessory” microbiota members transmitted horizontally, as reported for a nematode colonising the vertebrate gastrointestinal tract, Haemonchus contortus [13].

The description of an IJ-associated microbiota for S. carpocapsae and other Steinernema species is important because it improves our understanding of the ecological functioning of EPN. Indeed, the killing efficiency of nematodes was long thought to depend principally on the action of Xenorhabdus [16,17,18]. However, several studies have cast doubt on this view, because some entomopathogenic pairs have been found to have attenuated virulence or to be non-virulent when injected alone into insects [46,47,48, 67, 68]. These previous findings and the results presented here call into question the notion of the “pathobiome” in the case of the EPN ecology. Pseudomonas protegens and Pseudomonas chlororaphis, two rhizospheric and entomopathogenic species from this genus [69], are two good candidate members of the IJ pathobiome. An entomopathogenic Pseudomonas fluorescens strain has already been isolated by Lysenko and Weiser from S. carpocapsae IJs [28]. At the time P. fluorescens taxonomy was far from fully elucidated, but we assume that the authors actually identified a strain of P. protegens or P. chlororaphis.

The EPN lifecycle is not restricted to the killing of the insect. The nematode completes its sexual reproduction within the killed insect, then re-associates specifically with the symbiont, and possibly other members of its microbiota, after which it survives in the soils [16,17,18]. If P. protegens or P. chlororaphis belong to the IJ-associated microbiota, how do they interact with the other members of the IJ microbiota? An assessment of in vitro antimicrobial activity on agar plates showed that the Xenorhabdus strains have weak antimicrobial activity against bacterial members of Steinernema microbiota, whereas P. protegens and P. chlororaphis strains displayed higher levels of antimicrobial activity against both Xenorhabdus strains and other bacterial members of the Steinernema microbiota. If antibiotic activity measured in vitro accurately reflects the antibiotic activity in vivo, it suggests that antimicrobial production from species other than Xenorhabdus may be involved in in vivo interspecies competition. These data also suggest that the various members of the Steinernema microbiota might be subject to spatial compartmentation.

Conclusions

Finally, increasing numbers of scientists in the community working on microbial ecology are stressing the importance of working on natural microbial communities of low genotypic, functional or environmental complexity for the purposes of experimentation and theoretical modelling [70]. We are convinced that the microbial communities associated with EPNs meet this need perfectly. Three main questions could be explored in future studies of EPN models. Where are the members of this frequently IJ-associated microbiota located? How are these bacteria transmitted between EPN generations? What impact does each member of the microbiota have on entomopathogenic nematode fitness, survival and the capacity for adaptation in changing environments (soils, insects, etc.)?

Methods

Biological material

All the entomopathogenic nematodes (EPN) strains used here are listed in Additional file 1. Bacteria isolated from EPNs or EPN-infested Galleria cadavers and used for pathology and/or antibiosis assays are listed in Additional file 11. Bacteria used for the construction of the phylogenetic distance tree are listed in Additional file 12. Galleria mellonella (Lepidoptera: Pyralidae) insect larvae were reared at 28 °C in the dark, on honey and pollen. Spodoptera littoralis (Lepidoptera: Noctuidae) insect larvae were reared on an artificial diet [71] at 23 ± 1 °C, with a photoperiod of 16 h of light and 8 h of darkness (L16:D8) and relative humidity (RH) of 40 ± 5%.

Isolation, multiplication and storage of EPNs

EPN were isolated by ex situ Galleria trap (Fig. 1a) as previously described [72]. We used laboratory-reared EPN stocks, except S. carpocapsae GRAB, which was isolated in November 2017 from an apple orchard near of Avignon, France (43°54′20.4″ N 4°53′09.0″ E). Briefly, soil samples were collected at a depth of 0–20 cm (temperature 5.5 to 8.2 °C; RH 15.2 to 21.4%) and placed in 1-L plastic containers containing five last-instar Galleria larvae, which were then stored in the dark at 18 °C for seven days. The Galleria cadavers were placed on White traps (Fig. 1a). IJs emerging from cadavers and their cognate symbiont were identified molecularly (see below for details) as S. carpocapsae (100% identity for the ITS1-2 region) and X. nematophila (100% identity for the 16S rRNA), respectively. IJs were stored in Ringer’s solution (Merck). Laboratory-reared EPN stocks were multiplied every six months by infestation of last instar of Galleria as previously described [73]. Briefly, the EPNs were placed in contact with the Galleria larvae in Petri dishes. When the Galleria larvae died, the cadavers were placed on a White trap (Fig. 1b). Emerging IJs of Steinernema and Heterorhabditis species were stored in Ringer’s solution at 9 °C and 15 °C, respectively.

Isolation, culture and storage of bacteria isolated from EPNs

The bacteria were isolated by the hanging drop technique [23] (for Xenorhabdus and Photorhabdus only), from content of G. mellonella cadavers after IJ infestation or by crushing IJs. For the crushing method, we placed 20 IJs in a 1.5-mL Eppendorf tube containing 200 μL of LB broth and three 3-mm glass beads and subjected them to three cycles of grinding (1 min, 30 Hz, followed by 1 min without agitation) in a TissueLyser II apparatus (Qiagen, France). Bacteria were isolated on nutrient agar (Difco) plates or on nutrient bromothymol blue agar (NBTA) plates [74] and incubated at 28 °C for 48 h. Bacteria were also routinely grown at 28 °C in Luria-Bertani (LB) broth. The bacteria were stored at − 80 °C with 16% glycerol (v/v).

Molecular identification of EPNs and isolated bacteria

Steinernema species were identified by amplifying and sequencing 850 bp of the ITS1-2 region of the ribosomal DNA sequence as previously described [75], with the following primers, ITS_jc_F: 5′-GGA-CTG-AGC-TGT-TTC-GAG-A-3′ and ITS_jc_R: 5′-TAC-TGA-TAT-GCT-TAA-GTTCAG-CG-3′. Bacterial isolates were identified as previously described [76] by amplifying and sequencing a near full-length 16S rRNA gene (1372 bases). PCR amplifications were conducted in a Bio-Rad thermocycler (Bio-Rad, USA) and sequencing was performed by Eurofins, Germany.

Pathogenicity assays

Bacterial pathogenicity was assessed by injection into S. littoralis, as previously described [77]. Briefly, bacterial cultures in LB broth (#OD = 0.8) were diluted in the culture medium and 20 μL of the resulting bacterial suspension, containing 103 to 106 colony-forming units (CFUs), was injected into the haemolymph of 20 fifth-instar larvae of S. littoralis. The number of bacterial cells injected into the larvae was determined by plating on nutrient agar and counting the CFUs. After the bacterial injection, the insect larvae were incubated at 23 °C and mortality was monitored for up to 72 h. The pathogenicity of bacterial isolates was determined by measuring the time required for 50% of the insect larvae to be killed (LT50).

Antimicrobial activity

The antimicrobial activity of the Xenorhabdus, Pseudomonas protegens and Pseudomonas chlororaphis strains against other members of the EPN microbiota was assessed in vitro as previously described [74]. Briefly, 5 μL of an exponential growing culture of the producer strain was spotted onto Mueller-Hinton agar plates (Biokar). The producer strain was grown at 28 °C for 48 h and killed by exposure to chloroform for 30 min, followed by air drying for 15 min. Indicator strains were diluted (1.5% overnight culture) in 10 mL of top agar (LB broth; 0.7% agar) and poured over the producer strain plates to form an overlay. Plates were incubated for 24 h at 28 °C for all indicator strains and at 37 °C for Micrococcus luteus, which was used as a positive control. The diameter of the zone of inhibition was measured in millimetres. The assays were performed three times.

DNA extractions from IJ

We sampled 5000 IJs from a storage batch (Fig. 1b). In preliminary protocol tests, we treated nematodes with bleach solutions of different concentrations (from 2% to 10%) as previously performed [19, 32], but microscope observation showed that it resulted in the destruction of the external cuticle in many IJs. To avoid the alteration of the IJ integrity and consequently the bias in their microbiota composition, we decided to minimize contamination with micro-organisms from the body surface by rinsing the IJs thoroughly with tap water, on a filter. The washed IJs were recovered from the filter with a sterile pipette, transferred to 10 mL of sterile ultrapure water and immediately frozen at − 80 °C for future use. We compared several different DNA preparation methods, because EPNs are resistant to lysis because of their double cuticle. DNA recovery rates were highest for mechanical grinding of IJs with three 3-mm glass beads (three cycles of 30 Hz for 2 min, followed by 1 min without agitation) in a TissueLyser II apparatus (Qiagen, France) followed by DNA extraction with the Quick Extract kit from Epi-centre, USA (QE method) or with the Tissue extraction kit from Macherey-Nagel, Hoerdt, France (MN method). Briefly, for the QE method, frozen samples were rapidly thawed, heated at 80 °C for 20 min and centrifuged (3.000×g, 10 min) to collect the IJs as a pellet. The IJ pellet was placed in 200 μL of QE lysis buffer and ground. We then added 2 μL of Ready-Lyse Lysozyme Solution (Epi-centre, USA) and incubated the samples at room temperature until the solution cleared (48 to 72 h). Samples were again heated at 80 °C for 20 min, and IJs lysis was checked under a light microscope. When lysis was complete, the sample was treated with 20 μL of RNaseA 20 mg/ml (Invitrogen PureLinkTM RNaseA, France). It was then subjected to phenol-chloroform extraction followed by chloroform extraction alone. DNA was precipitated in 70% ethanol, resuspended in 50 μl ultrapure water and stored at − 20 °C. The MN method was performed according to the kit manufacturer’s instructions, except that the column step was replaced by phenol-chloroform and chloroform extractions, as described above. We assessed contaminant bacterial DNA levels, by generating several control DNA preparations: in ultrapure water with the QE method (Kitome_QE control), in ultrapure water with the MN method (Kitome_MN control), in the tap water used during the IJ washing step, with the QE method (Tap water control) and in the Ringer medium used for IJ storage, with the QE method (Ringer control). For each sample, three to six technical replicates were prepared.

DNA extraction from G. mellonella larvae

As IJ multiply within Galleria larvae, we also extracted DNA from insect larvae. Three G. mellonella larvae were surface-sterilised with 70% (vol/vol) ethanol, were placed in a pot with a 2.5 mm tungsten bead and 1.5 mL PBS buffer and were crushed by three grinding cycles (1 min at 30 Hz followed by 1 min without agitation) in the TissueLyzer II apparatus. The homogenate was centrifuged (400×g, 2 min) to remove larval debris and the supernatant was immediately frozen at − 80 °C until use. Samples were rapidly thawed, heated at 80 °C for 20 min and split into five fractions. The QE method (see the procedure for IJs described above) was used for the preparation of independent DNA extracts from each technical replicate.

Library preparation and sequencing

Amplicon libraries were constructed following two rounds of PCR amplification. The first amplification step was performed on 10 to 100 ng of DNA, with iProofTM DNA Polymerase (Bio-Rad), in a Bio-Rad thermocycler. The hyper variable V3V4 region of the 16S rRNA gene was targeted with the universal primers F343 (5′-CTTTCCCTACACGACGCTCTTCCGATCTTACGGRAGGCAGCAG-3′) and R784 (5′-GGAGTTCAGACGTGTGCTCTTCCGATCTTACCAGGGTATCTAATCCT-3′), and the rpoB fragment was targeted with the primers Univ_rpoB_F_deg (5′-CTTTCCCTACACGACGCTCTTCCGATCTGGYTWYGAAGTNCGHGACGTDCA-3′) and Univ_rpoB_R_deg (5′-GGAGTTCAGACGTGTGCTCTTCCGATCTTGACGYTGCATGTTBGMRCCCATMA-3′) [41]. The Illumina adapters are indicated in non-bold characters. We performed 30 amplification cycles with annealing temperatures of 65 °C and 60 °C for the V3-V4 region (~ 460 bases), and the rpoB region (~ 435 bp), respectively. Single multiplexing was performed at the Genomic and Transcriptomic Platform (INRA, Toulouse, France), with 6-bp index sequences, which were added to the reverse primers during a second 12-cycle PCR. Amplicon libraries were sequenced with Illumina MiSeq technology (MiSeq Reagent Kit v2) according to the manufacturer’s instructions. FastQ files were generated at the end of the run. The program FastQC [78] was used for quality control checks on raw sequence data and the quality of the run was internally checked by adding 30% of PhiX sequences at 12.5pM. Each pair-end sequence was assigned to its sample with the help of the previously integrated index, and pair-end reads were assembled with FLASH [79]. Unassembled reads were discarded.

Sequence data processing and analyses

The sequencing reads obtained were processed according to the FROGs pipeline [80]. A pre-processing tool was used to remove sequences that did not contain both primers, to trim the primers and to remove all sequences containing an ambiguous base. Sequence clustering was performed with the Swarm algorithm [81]. Chimeric sequences were detected with the VSEARCH algorithm, by the de novo UCHIME method [82, 83] and were removed. A filtering tool was used to remove clusters, which had a read number abundance of less than 0.005 % of all reads. Sequences were assigned with RDP Classifier [84] and the 16S rRNA database Silva [85] for V3V4 reads. For sequence assignment with the rpoB marker, we constructed a reference database including 45,000 sequences; this database is available from the FROGs website (http://frogs.toulouse.inra.fr/).

Bacterial community and statistical analyses

OTU diversity analysis and statistical analyses were carried out with the R packages Phyloseq [86], Vegan [87] and Ampvis 2 (https://github.com/MadsAlbertsen/ampvis2). In brief, Phyloseq was used for rarefaction curves, α-diversity indices and multivariate PERMANOVA. Ampvis 2 was used for Bray-Curtis distance matrices (square root-transformed OTU abundance data), principal co-ordinate analyses (unconstrained ordination plots), Venn diagrams, heat maps and figures showing the frequency of abundant OTUs. Plots were generated with the ggplot2 package [88].

Phylogenetic analysis

Phylogenetic analysis of the ITS sequences and of the V3V4 and rpoB amplicons, sequence alignment and maximum likelihood analysis with the GTR model were performed as previously described [47]. The Mega7 tool [89] was used to generate a circular phylogenetic tree. Phylogenetic analysis of the 16S rRNA genes from 47 bacterial isolates from S. carpocapsae IJs, sequence alignment and neighbour-joining analysis with the Kimura two-parameter model were performed as described by Tailliez et al. [90].

Availability of data and materials

Raw data of the MiSeq sequencing are available at the European Archive database (BioProject PRJEB24936, http://www.ebi.ac.uk/ena/data/view/PRJEB24936). Accession numbers of the 16S RNA genes used for phylogenetic tree in Fig. 6 are listed in Additional file 12. The abundance tables of OTUs with the V3V4 and the rpoB markers as well as the lists of OTUs used for Figs. 2a and 5 are available in Additional file 13.

References

  1. Larousse M, Galiana E. Microbial partnerships of pathogenic oomycetes. PLoS Pathog. 2017;13:e1006028.

    PubMed  PubMed Central  Google Scholar 

  2. Clavel T, Lagkouvardos I, Blaut M, Stecher B. The mouse gut microbiome revisited: From complex diversity to model ecosystems. Int J Med Microbiol. 2016;306:316–27.

    CAS  PubMed  Google Scholar 

  3. Ma D, Storelli G, Mitchell M, Leulier F. Studying host-microbiota mutualism in Drosophila: harnessing the power of gnotobiotic flies. Biomed J. 2015;38:285–93.

    PubMed  Google Scholar 

  4. Cirimotich CM, Ramirez JL, Dimopoulos G. Native microbiota shape insect vector competence for human pathogens. Cell Host Microbe. 2011;10:307–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Romoli O, Gendrin M. The tripartite interactions between the mosquito, its microbiota and Plasmodium. Parasit Vectors. 2018;11:200.

    PubMed  PubMed Central  Google Scholar 

  6. Vayssier-Taussat M, Albina E, Citti C, Cosson J-F, Jacques M-A, Lebrun M-H, et al. Shifting the paradigm from pathogens to pathobiome: new concepts in the light of meta-omics. Front Cell Infect Microbiol. 2014;4:29.

    PubMed  PubMed Central  Google Scholar 

  7. Hajishengallis G, Lamont RJ. Dancing with the stars: how choreographed bacterial interactions dictate nososymbiocity and give rise to keystone pathogens, accessory pathogens, and pathobionts. Trends Microbiol. 2016;24:477–89.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Engel P, Moran NA. The gut microbiota of insects - diversity in structure and function. FEMS Microbiol Rev. 2013;37:699–735.

    CAS  PubMed  Google Scholar 

  9. Douglas AE. Multiorganismal insects: diversity and function of resident microorganisms. Ann Rev Entomol. 2015;60:17–34.

    CAS  Google Scholar 

  10. Schwarz RS, Moran NA, Evans JD. Early gut colonizers shape parasite susceptibility and microbiota composition in honey bee workers. Proc Natl Acad Sci USA. 2016;113:9345–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Mockler BK, Kwong WK, Moran NA, Koch H. Microbiome structure influences infection by the parasite Crithidia bombi in bumble bees. Appl Environ Microbiol. 2018;84.

  12. Caccia S, Di Lelio I, La Storia A, Marinelli A, Varricchio P, Franzetti E, et al. Midgut microbiota and host immunocompetence underlie Bacillus thuringiensis killing mechanism. Proc Natl Acad Sci USA. National Acad Sciences. 2016;113:9486–91.

    CAS  Google Scholar 

  13. Jenkins TP, Brindley PJ, Gasser RB, Cantacessi C. Helminth microbiomes - a hidden treasure trove? Trends Parasitol. 2019;35:13–22.

    CAS  PubMed  Google Scholar 

  14. Slatko BE, Luck AN, Dobson SL, Foster JM. Wolbachia endosymbionts and human disease control. Mol Biochem Parasitol. 2014;195:88–95.

    CAS  PubMed  Google Scholar 

  15. Goodrich-Blair H, Clarke DJ. Mutualism and pathogenesis in Xenorhabdus and Photorhabdus: two roads to the same destination. 2007;64:260–268.

  16. Herbert EE, Goodrich-Blair H. Friend and foe: the two faces of Xenorhabdus nematophila. Nat Rev Microbiol. 2007;5:634–46.

    CAS  PubMed  Google Scholar 

  17. Richards GR, Goodrich-Blair H. Masters of conquest and pillage: Xenorhabdus nematophila global regulators control transitions from virulence to nutrient acquisition. Cell Microbiol. 2009;11:1025–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Nielsen-LeRoux C, Gaudriault S, Ramarao N, Lereclus D, Givaudan A. How the insect pathogen bacteria Bacillus thuringiensis and Xenorhabdus/Photorhabdus occupy their hosts. Curr Opin Microbiol. 2012;15:220–31.

    PubMed  Google Scholar 

  19. Sicard M, Brugirard-Ricaud K, Pagès S, Lanois A, Boemare NE, Brehélin M, et al. Stages of infection during the tripartite interaction between Xenorhabdus nematophila, its nematode vector, and insect hosts. Appl Environ Microbiol. 2004;70:6473–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Singh S, Reese JM, Casanova-Torres AM, Goodrich-Blair H, Forst S. Microbial population dynamics in the hemolymph of Manduca sexta infected with Xenorhabdus nematophila and the entomopathogenic nematode Steinernema carpocapsae. Appl Environ Microbiol. 2014;80:4277–85.

    PubMed  PubMed Central  Google Scholar 

  21. Poinar GO, Himsworth PT. Neoaplectana parasitism of larvae of the greater wax moth, Galleria mellonella. J Invertebr Pathol. 1967;9:241–246.

    Google Scholar 

  22. Poinar GO. Leutenegger. Anatomy of the infective and normal third-stage juveniles of Neoaplectana carpocapsae Weiser (Steinernematidae: Nematoda). J. Parasitol. 1968;54:340–50.

    Google Scholar 

  23. Poinar GO, Thomas GM. Significance of Achromobacter nematophilus Poinar and Thomas (Achromobacteraceae: Eubacteriales) in the development of the nematode, DD-136 (Neoaplectana sp.: Steinernematidae). Parasitology. 1966;56:385–90.

    PubMed  Google Scholar 

  24. Snyder H, Stock SP, Kim S-K, Flores-Lara Y, Forst S. New insights into the colonization and release processes of Xenorhabdus nematophila and the morphology and ultrastructure of the bacterial receptacle of its nematode host, Steinernema carpocapsae. Appl Environ Microbiol. 2007;73:5338–5346.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Sugar DR, Murfin KE, Chaston JM, Andersen AW, Richards GR, Deléon L, et al. Phenotypic variation and host interactions of Xenorhabdus bovienii SS-2004, the entomopathogenic symbiont of Steinernema jollieti nematodes. Environ Microbiol. 2012;14:924–39.

    CAS  PubMed  Google Scholar 

  26. Bonifassi E, Fischer-Le Saux M, Boemare N, Lanois A, Laumond C, Smart G. Gnotobiological study of infective juveniles and symbionts of Steinernema scapterisci: a model to clarify the concept of the natural occurrence of monoxenic associations in entomopathogenic nematodes. J Invertebr Pathol. 1999;74:164–72.

    CAS  PubMed  Google Scholar 

  27. Poinar GO. The presence of Achromobacter Nematophilus in the infective stage of a Neoaplectana Sp. (Steinernematidae: Nematoda). Nematologica. 1966;12:105–8.

    Google Scholar 

  28. Lysenko O, Weiser J. Bacteria associated with the nematode Neoplectana carpocapsae and the pathogenicity of this complex for Galleria mellonella larvae. J Invertebr Pathol. 1974;24:332–6.

    CAS  PubMed  Google Scholar 

  29. Weiser J. Insects of medical importance in Europe. Bull Wld Hlth Org. 1963;28:121–7.

    CAS  Google Scholar 

  30. Aguillera MM, Hodge NC, Stall RE, Smart GC. Bacterial symbionts of Steinernema scapterisci. J Invertebr Pathol. 1993;62:68–72.

    Google Scholar 

  31. Boemare N. Recherches sur les complexes némato-bactériens entomopathogènes : étude bactériologique gnotobiologique et physiopathologique du mode d'action parasitaire de Steinernema carpocapsae weiser (Rhabditida Steinernematidae). Thèse de l'Université Montpellier 2; 1983.

  32. Gouge DH, Snyder JL. Temporal association of entomopathogenic nematodes (Rhabditida: Steinernematidae and Heterorhabditidae) and bacteria. J Invertebr Pathol. 2006;91:147–57.

    PubMed  Google Scholar 

  33. Neelakanta G, Sultana H. The use of metagenomic approaches to analyze changes in microbial communities. MBI. 2013;:37.

  34. Waldor MK, Tyson G, Borenstein E, Ochman H, Moeller A, Finlay BB, et al. Where next for microbiome research? PLoS Biol. 2015;13:e1002050.

    PubMed  PubMed Central  Google Scholar 

  35. Zhou J, He Z, Yang Y, Deng Y, Tringe SG, Alvarez-Cohen L. High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. mBio. 2015;6.

  36. Baquiran J-P, Thater B, Sedky S, De Ley P, Crowley D, Orwin PM. Culture-independent investigation of the microbiome associated with the Nematode Acrobeloides maximus. PLoS ONE. 2013;8:e67425.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Berg M, Stenuit B, Ho J, Wang A, Parke C, Knight M, et al. Assembly of the Caenorhabditis elegans gut microbiota from diverse soil microbial environments. ISME J. 2016;10:1998–2009.

    PubMed  PubMed Central  Google Scholar 

  38. Dirksen P, Marsh SA, Braker I, Heitland N, Wagner S, Nakad R, et al. The native microbiome of the nematode Caenorhabditis elegans: gateway to a new host-microbiome model. BMC Biol. 2016;14:38.

  39. Wollenberg AC, Jagdish T, Slough G, Hoinville ME, Wollenberg MS. Death becomes them: bacterial community dynamics and stilbene antibiotic production in Galleria mellonella cadavers killed by Heterorhabditis/Photorhabdus. Appl Environ Microbiol. 2016.

  40. Mason KL, Stepien TA, Blum JE, Holt JF, Labbe NH. Rush JS, et al. From commensal to pathogen: translocation of Enterococcus faecalis from the midgut to the hemocoel of Manduca sexta. mBio. 2011;2:e00065–11.

    Google Scholar 

  41. Ogier J-C, Pages S, Galan M, Barret M, Gaudriault S. rpoB, a promising marker for analyzing the diversity of bacterial communities by amplicon sequencing. BMC Microbiol. 2019;19:171–16.

  42. Blaxter ML, De Ley P, Garey JR, Liu LX, Scheldeman P, Vierstraete A, et al. A molecular evolutionary framework for the phylum Nematoda. Nature. 1998;392:71–75.

    CAS  PubMed  Google Scholar 

  43. Kimura M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol. 1980;16:111–20.

    CAS  PubMed  Google Scholar 

  44. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4:406–25.

    CAS  PubMed  Google Scholar 

  45. Singh S, Orr D, Divinagracia E, McGraw J, Dorff K, Forst S. Role of secondary metabolites in establishment of the mutualistic partnership between Xenorhabdus nematophila and the entomopathogenic nematode Steinernema carpocapsae. Appl Environ Microbiol. 2015;81:754–64.

    PubMed  PubMed Central  Google Scholar 

  46. Akhurst RJ. Xenorhabdus nematophilus subsp. poinarii: its Interaction with Insect Pathogenic Nematodes. System Appl Microbiol. 1986;8:142–7.

    Google Scholar 

  47. Ogier J-C, Pages S, Bisch G, Chiapello H, Médigue C, Rouy Z, et al. Attenuated virulence and genomic reductive evolution in the entomopathogenic bacterial symbiont species, Xenorhabdus poinarii. Genome Biol Evol. 2014;6:1495–1513.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Bisch G, Pages S, McMullen JG, Stock SP, Duvic B, Givaudan A, et al. Xenorhabdus bovienii CS03, the bacterial symbiont of the entomopathogenic nematode Steinernema weiseri, is a non-virulent strain against lepidopteran insects. J Invertebr Pathol. 2015;124:15–22.

    CAS  PubMed  Google Scholar 

  49. Brillard J, Duchaud E, Boemare N, Kunst F, Givaudan A. The PhlA hemolysin from the entomopathogenic bacterium Photorhabdus luminescens belongs to the two-partner secretion family of hemolysins. 2002;184:3871–3878.

  50. Flury P, Aellen N, Ruffner B, Péchy-Tarr M, Fataar S, Metla Z, et al. Insect pathogenicity in plant-beneficial pseudomonads: phylogenetic distribution and comparative genomics. ISME J. 2016.

  51. Jousset A, Schuldes J, Keel C, Maurhofer M, Daniel R, Scheu S, et al. Full-genome sequence of the plant growth-promoting bacterium pseudomonas protegens CHA0. Genome Announc. 2014;2.

  52. Delorme S, Lemanceau P, Christen R, Corberand T, Meyer J-M, Gardan L. Pseudomonas lini sp. nov., a novel species from bulk and rhizospheric soils. Int J Syst Evol Microbiol. 2002;52:513–23.

    CAS  PubMed  Google Scholar 

  53. Achouak W, Sutra L, Heulin T, Meyer JM, Fromin N, Degraeve S, et al. Pseudomonas brassicacearum sp. nov. and Pseudomonas thivervalensis sp. nov., two root-associated bacteria isolated from Brassica napus and Arabidopsis thaliana. Int J Syst Evol Microbiol. 2000;50:9–18.

    CAS  PubMed  Google Scholar 

  54. Babic I, Fischer-Le Saux M, Giraud E, Boemare N. Occurrence of natural dixenic associations between the symbiont Photorhabdus luminescens and bacteria related to Ochrobactrum spp. in tropical entomopathogenic Heterorhabditis spp. (Nematoda, Rhabditida). Microbiology. 2000;146:709–18.

  55. Walsh KT, Webster JM. Interaction of microbial populations in Steinernema (Steinernematidae, Nematoda) infected Galleria mellonella larvae. J Invertebr Pathol. 2003;83:118–26.

    PubMed  Google Scholar 

  56. Isaacson PJ, Webster JM. Antimicrobial activity of Xenorhabdus sp. RIO (Enterobacteriaceae), symbiont of the entomopathogenic nematode, Steinernema riobrave (Rhabditida: Steinernematidae). J Invertebr Pathol. 2002;79:146–53.

    CAS  PubMed  Google Scholar 

  57. Poinar GO. Morphological and behavioral abnormalities in Heterorhabditis heliothidis (Rhabditida) associated with the bacterium, Alcaligenes faecalis. Revue de Nématologie. 1988;11:381–383.

  58. Rae R, Riebesell M, Dinkelacker I, Wang Q, Herrmann M, Weller AM, et al. Isolation of naturally associated bacteria of necromenic Pristionchus nematodes and fitness consequences. J Exp Biol. 2008;211:1927–36.

    CAS  PubMed  Google Scholar 

  59. Hartman K, van der Heijden MG, Roussely-Provent V, Walser J-C, Schlaeppi K. Deciphering composition and function of the root microbiome of a legume plant. Microbiome. Microbiome; 2017;:1–13.

  60. Larousse M, Rancurel C, Syska C, Palero F, Etienne C, Industri B, et al. Tomato root microbiota and Phytophthora parasitica-associated disease. Microbiome. 2017;5:56.

    PubMed  PubMed Central  Google Scholar 

  61. Mendes R, Garbeva P, Raaijmakers JM. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol Rev. 2013;37:634–63.

    CAS  PubMed  Google Scholar 

  62. Lacerda Júnior GV, Noronha MF, de Sousa STP, Cabral L, Domingos DF, Sáber ML, et al. Potential of semiarid soil from Caatinga biome as a novel source for mining lignocellulose-degrading enzymes. FEMS Microbiol Ecol. 2017;93:fiw248.

    PubMed  Google Scholar 

  63. Brucker RM, Bordenstein SR. The hologenomic basis of speciation: gut bacteria cause hybrid lethality in the genus Nasonia. Science. 2013;341:667–9.

    CAS  PubMed  Google Scholar 

  64. Pollock FJ, McMinds R, Smith S, Bourne DG, Willis BL, Medina M, et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat Commun. 2018;9:4921.

    PubMed  PubMed Central  Google Scholar 

  65. Leigh BA, Bordenstein SR, Brooks AW, Mikaelyan A. Bordenstein SR. Finer-scale phylosymbiosis: insights from insect viromes. mSystems. 2018;3:e00131–18.

    CAS  PubMed  Google Scholar 

  66. Bright M, Bulgheresi S. A complex journey: transmission of microbial symbionts. Nat Rev Microbiol. 2010;8:218–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Kim I-H, Aryal SK, Aghai DT, Casanova-Torres AM, Hillman K, Kozuch MP, et al. The insect pathogenic bacterium Xenorhabdus innexi has attenuated virulence in multiple insect model hosts yet encodes a potent mosquitocidal toxin. BMC Genomics. 2017;18:927.

  68. McMullen JG, McQuade R, Ogier J-C, Pages S, Gaudriault S, Patricia SS. Variable virulence phenotype of Xenorhabdus bovienii (γ-Proteobacteria: Enterobacteriaceae) in the absence of their vector hosts. Microbiology. 2017;163:510–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Keel C. A look into the toolbox of multi-talents: insect pathogenicity determinants of plant-beneficial pseudomonads. Environ Microbiol. 2016;18:3207–9.

    PubMed  Google Scholar 

  70. Tecon R, Mitri S, Ciccarese D, Or D, van der Meer JR, Johnson DR. Bridging the holistic-reductionist divide in microbial ecology. mSystems. 2019;4.

  71. Poitout S, Bues R. Elevage de plusieurs espèces de lépidoptères (Noctuidae) sur milieu artificiel riche et sur milieu artificiel simplifié. Ann Zool Ecol Anim. 1970;2:79–91.

    Google Scholar 

  72. Bedding RA, Akhurst RJ. A simple technique for the detection of insect parasitic rhabditid nematodes in soil. Nematologica. 1975;21:109–10.

    Google Scholar 

  73. Kaya HK, Stock SP. Techniques in insect nematology. In: Lacey LA, editor. Manual of techniques in insect pathology. San Diego, CA: Academic Press; 1997. p. 281–324.

    Google Scholar 

  74. Boemare NE, Thaler JO, Lanois A. Simple bacteriological tests for phenotypic characterization of Xenorhabdus and Photorhabdus phase variants. Symbiosis. 1997;22:167–75.

    Google Scholar 

  75. Stock SP, Campbell JF, Nadler SA. Phylogeny of Steinernema travassos, 1927 (Cephalobina: Steinernematidae) inferred from ribosomal DNA sequences and morphological characters. J. Parasitol. 2001;87:877–89.

    CAS  PubMed  Google Scholar 

  76. Tailliez P, Pagès S, Ginibre N, Boemare N. New insight into diversity in the genus Xenorhabdus, including the description of ten novel species. Int J Syst Evol Microbiol. 2006;56:2805–18.

    CAS  PubMed  Google Scholar 

  77. Givaudan A, Lanois A. flhDC, the flagellar master operon of Xenorhabdus nematophilus: requirement for motility, lipolysis, extracellular hemolysis, and full virulence in insects. 2000;182:107–15.

  78. Andrews S. FastQC: a quality control tool for high throughput sequence data. httpwww.bioinformatics.babraham.ac.ukprojectsfastqc. 2010.

  79. Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27:2957–63.

    PubMed  PubMed Central  Google Scholar 

  80. Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, et al. FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics. 2017;:1–8.

  81. Mahé F, Rognes T, Quince C, de Vargas C, Dunthorn M. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ. PeerJ Inc; 2014;2:e593.

    PubMed  PubMed Central  Google Scholar 

  82. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. PeerJ Inc; 2016;4:e2584.

    PubMed  PubMed Central  Google Scholar 

  84. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.

    CAS  PubMed  Google Scholar 

  86. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. Public Library of Science; 2013;8:e61217.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Dixon P. VEGAN, a package of R functions for community ecology. J Vegetation Sc. 2003;14:927–30.

    Google Scholar 

  88. Wickham H. ggplot2: Elegant graphics for data analysis. Springer-Verlag, editor. New York; 2016.

  89. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for Bigger Datasets. Mol Biol Evol. 2016;33:1870–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Tailliez P, Laroui C, Ginibre N, Paule A, Pagès S, Boemare N. Phylogeny of Photorhabdus and Xenorhabdus based on universally conserved protein-coding sequences and implications for the taxonomy of these two genera. Proposal of new taxa: X. vietnamensis sp. nov., P. luminescens subsp. caribbeanensis subsp. nov., P. luminescens subsp. hainanensis subsp. nov., P. temperata subsp. khanii subsp. nov., P. temperata subsp. tasmaniensis subsp. nov., and the reclassification of P. luminescens subsp. thracensis as P. temperata subsp. thracensis comb. nov. Int J Syst Evol Microbiol. 2010;60:1921–37.

  91. Gouy M, Guindon S, Gascuel O. SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol. 2010;27:221–4.

    CAS  PubMed  Google Scholar 

  92. Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–321.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank David Shapiro for sending S. carpocapsae DD136_USDA and All_USDA strains, Vladimir Puza for S. carpocapsae EGY03, Marie Berling for S. carpocapsae CREA3, Alexandre Jousset for Pseudomonas protegens CHAOT and Jérome Lambion for providing us with access to the orchard soil used for S. carpocapsae GRAB baiting. We also thank Alain Givaudan, Matthieu Barret and Mathieu Sicard for helpful comments on the manuscript. We thank the insect quarantine platform (PIQ), a member of the Vectopole Sud network, for providing the infrastructure required for experiments on pest insects.

Funding

This work was funded by Plant Health and Environment Department of INRA (grant 2015-2017 “Characterization of the entomopathogenic nematode microbiota required for parasitic succes of Steinernema in the lepidopteran insect Spodoptera littoralis”) and the MEM metaprogram of INRA (grant 2016 “Development of a methodology for taxonomic assignment to species level in the microbiota of the entomopathogenic nematode Steinernema”).

Author information

Author notes

  1. Jean-Claude Ogier and Sylvie Pagès contributed equally to this work.

Authors and Affiliations

  1. DGIMI, INRAe-Université de Montpellier, 34095, Montpellier, France

    Jean-Claude Ogier, Sylvie Pagès, Marie Frayssinet & Sophie Gaudriault

Authors
  1. Jean-Claude Ogier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sylvie Pagès
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marie Frayssinet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sophie Gaudriault
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JCO, SP and SG conceived the study. JCO carried out the metagenomics analyses. SP carried out the bacterial isolation, the pathology and the antibiosis experiments. MF and SP carried out the nematode isolation. JCO, SP and SG analysed the data and prepared the figure. SG supervised the project. JCO, SP and SG wrote the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Sophie Gaudriault.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1.

List of entomopathogenic nematodes used in this study

Additional file 2.

Rarefaction curves obtained by Illumina-amplicon sequencing of the 16S (a and c) and rpoB (b and d) markers in various community samples. Rarefaction curves were assembled, with an estimation of species richness (x-axis), defined with a sequence identity cutoff of 97%, relative to the total number of bacterial sequences identified (y-axis). Samples are presented separately. Sample identities are indicated by specific colours (see legend below the figures).

Additional file 3.

Box-plots illustrating alpha diversity (observed OTUs and Shannon diversity index) in the microbiota of Steinernema IJ samples, Galleria samples and control samples obtained by Illumina-amplicon sequencing of the V3V4 region of the 16S rRNA gene (Panels A and B) and the rpoB region (Panel C). Median values and interquartile ranges are indicated on the plots. Sample identities are indicated by specific colours (see legend below the figures), three to six technical replicates per sample type were performed. A. V3V4 amplicon sequencing. Estimated OTU richness and diversity indices of S. carpocapsae SK27 (Batch_23_08_16), Galleria mellonella larvae used for IJ multiplication and experimental control samples (Kitome_QE, Kitome_MN, Tap water and Ringer); B. V3V4 amplicon sequencing. Estimated OTU richness and diversity indices of S. carpocapsae (SK27_23_08_16 and B10_27_04_16); S. weiseri (583_09_06_15, TCH02_11_08_16, TUR03_21_01_16 and TUR03_09_06_15); S. glaseri (SK39_09_06_15); S. feltiae (FRA200_09_06_15 and FRA200_12_08_15) and H. bacteriophora (TT01_22_06_16 and TT01_15_03_16). C. rpoB amplicon sequencing. Estimated OTU richness and diversity indices of S. carpocapsae (SK27_23_08_16 and B10_27_04_16); S. weiseri (TCH02_11_08_16); S. glaseri (SK39_09_06_15); S. feltiae (FRA200_09_06_15 and FRA200_12_08_15) and H. bacteriophora (TT01_22_06_16 and TT01_15_03_16).

Additional file 4.

Diversity and composition of bacterial communities associated with IJs of S. carpocapsae SK27, to Galleria mellonella larvae used for IJ multiplication and to control samples (Kitome_QE, Kitome_MN, Tap water and Ringer). IJ microbiota were studied by metabarcoding with the V3V4 region of the 16S rRNA gene. A. Principal co-ordinates analysis (PCoA) based on Bray-Curtis distances of S. carpocapsae SK27 batch SK27_23_08_16 samples (squares), G. mellonella samples (triangles) and control samples (circles). Each point represents an individual replicate. The proportion of the variance explained by each axis is indicated as a percentage. Segregation between the three sample sets is statistically significant (Permanova, Df=2, R2=0.51, p-value=10-4). B and C. Bacterial composition of S. carpocapsae SK27 batch SK27_23_08_16 samples, G. mellonella samples and control samples. Bar plots, each representing an individual replicate, showing the relative abundance of (B) the five most frequently represented OTUs at the phylum level (Top 5 phylum), (C) the nine most frequently represented OTUs at the family level (Top 9 family). D. Heatmap showing the microbiota composition of Galleria samples (whole insects). Each column represents a Galleria sample. The 30 most abundant OTUs across the samples at the genus affiliation level (Top30 Genus) are listed on the left. The percentage relative abundance is indicated by the gradient of blue hues.

Additional file 5.

Comparison of the microbiota of S. carpocapase SK27 batch SK27_23_08_16 (six technical replicates) based on two taxonomic markers, the V3V4 region of the 16S rRNA gene (A) and the 430bp-rpoB region (B). Phylogenetic trees of OTUs based on the rpoB 435 bp region or the V3V4 region of the 16S rRNA gene were inferred with SEAVIEW 4.0 [91], using a PhyML-based maximum likelihood algorithm [92] and the GTR model. The sum of read numbers for the six replicates is indicated after the OTU name. Only abundant OTUs with read numbers accounting for more than 0. 1% of the reads for the sample in at least one replicate are included in the phylogenetic tree. OTUs belonging to the same bacterial genus are shown in the same colour.

Additional file 6.

Comparison of bacterial communities associated with IJs from different genera (Steinernema and Heterorhabditis) and different species (S. carpocapsae, S. feltiae, S. weiseri and S. glaseri). A. Principal coordinates analysis (PCoA) based on Bray-Curtis distances for IJ microbiota based on the V3V4 region of the 16S rRNA gene. Heterorhabditis and Steinernema samples are indicated with circles and triangles, respectively. Colors indicate the different Steinernema species. Each point represents a technical replicate. The proportion of the variance explained by each axis is shown. The five samples are statistically different (Permanova rpoB, Df=4, R2=0.73, p-value=10-4). B. Heatmap showing the microbiota composition of IJ samples based on the V3V4 region of the 16S rRNA gene. Each column represents an IJ species. The 30 most abundant OTUs across the samples at the genus affiliation level (Top30 Genus) are listed on the left. The percentage relative abundance is indicated by the gradient of blue hues. C. Heatmap showing the microbiota composition of different strains of Steinernema carpocapsae based on the V3V4 region of the 16S rRNA gene and the 435 bp rpoB region. Each column represents an IJ species. The 30 most abundant OTUs across the samples at the species affiliation level (Top30 species) are listed on the left. The percentage relative abundance is indicated by the gradient of blue hues. The nematode strains used here belong to the following batches: S. carpocapsae SK27_23_08_16 and B10_27_04_16; S. weiseri 583_09_06_15, TCH02_11_08_16 (t1), TUR03_21_01_16 and TUR03_09_06_15; S. glaseri SK39_09_06_15; S. feltiae FRA200_09_06_15 and FRA200_12_08_15 and H. bacteriophora TT01_22_06_16 and TT01_15_03_16. Three to six technical replicates per batch were performed.

Additional file 7.

Principal coordinates analysis (PCoA) based on Bray-Curtis distances for IJ microbiota obtained by metabarcoding with the V3V4 region of the 16S rRNA gene. Each point represents an individual sample replicate. The different strains, multiplication batches and origins of S. carpocapsae are indicated by colours and symbols. The proportion of the variance explained by each axis is shown. A. Comparison of six S. carpocapsae strains. The six samples are significantly different (Permanova, Df=5, R2=0.75, p-value=10-4). B. Comparison of four multiplication batches of S. carpocapsae SK27. The four samples are significantly different (Permanova, Df=3, R2=0.73, p-value=10-4). C. Comparison of two laboratory origins of S. carpocapsae DD136 and S. carpocapsae All (Permanova: DD136 versus All, Df=1, R2=0.09, p-value=10-4; DGIMI versus USDA, Df=1, R2=0.28, p-value=10-4).

Additional file 8.

Heatmap showing the microbiota composition of Steinernema carpocapsae strains. Each column represents a replicate (strain or batch). The 30 most abundant OTUs across the samples at the species affiliation level (a and c) and at the genus affiliation level (b and d) for the the 435 bp rpoB region (A and B) and the V3V4 region of the 16S gene (c and d) are listed on the left. The percentage of relative abundance is indicated by the gradient of blue hues.

Additional file 9.

Heatmap showing the microbiota composition of the S. carpocapsae GRAB strain freshly isolated (November 2017) from the soil of an apple orchard in Gard, France. Each column represents a technical replicate. The 30 most abundant OTUs across the samples at the species affiliation level for the 435 bp rpoB region or the V3V4 region of the 16S gene are listed on the left. The percentage of relative abundance is indicated by the gradient of blue hues.

Additional file 10.

Antimicrobial activities of Xenorhabdus (a) and Pseudomonas (b) strains against bacterial taxa from the IJ-associated microbiota of S. carpocapsae SK27, S. glaseri SK39 and S. weiseri 583.

Additional file 11.

Bacterial strains isolated in the study and used in pathology and/or antibiosis assays.

Additional file 12.

List of the 62 bacterial strains, including 50 bacterial isolates from S.carpocapsae IJs, used in this study to build the 16S rRNA gene phylogenetic tree.

Additional file 13. a

. List of OTUs obtained with the V3V4 marker. b. List of OTUs obtained with rpoB marker. c. Venn diagram table detailing compositions of core and non-core OTUs of Figure 2a. d. List and frequency of OTUs associated with the S. carpocapsae IJs obtained with the V3V4 marker for which abundance in the whole dataset exceeded 0.1% (plotted in Figure 5.b). e. List and frequency of OTUs associated with S. carpocapsae IJs obtained with the rpoB marker for which abundance in the whole dataset exceeded 0.1% (plotted in Figure 5.a).

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ogier, JC., Pagès, S., Frayssinet, M. et al. Entomopathogenic nematode-associated microbiota: from monoxenic paradigm to pathobiome. Microbiome 8, 25 (2020). https://doi.org/10.1186/s40168-020-00800-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40168-020-00800-5

Keywords

Microbiome

ISSN: 2049-2618

Contact us