Both host and diet shape bacterial communities of predatory mites

doi:10.21203/rs.3.rs-2433166/v1

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Both host and diet shape bacterial communities of predatory mites

https://doi.org/10.21203/rs.3.rs-2433166/v1

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published 19 Jul, 2023

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Microbial communities, derived from food, ambient and inner, can affect host ecological adaption and evolution. Comparing with most of phytophagous arthropods, the diet of predators is pretty complex and varied. At a higher trophic level, there may be more opportunity for predators to develop specific microbiota which may also depend on the level of prey host specialization shown by the predators. To explore how diet sources affect host microbial communities and vary across predators species, we considered three types predators from the family of Phytoseiidae: polyphagous (Amblyseius orientalis Ehara, Neoseiulus barkeri Hughes and Amblyseius swirskii Athias-Henrio), oligophagous (Neoseiulus californicus McGregor), and monophagous (Phytoseiulus persimilis Athias-Henriot) predatory mites. The polyphagous species were fed on two types of diets, natural prey (Tetranychus urticae Koch) and alternative prey (Carpoglyphus lactis Linnaeus). By using 16S rRNA sequencing, we found that diet was the main source of microbiota in predatory mites, and that there was no clear effect of prey specialization. For the polyphagous mites, species had a larger impact than prey type on microbial composition. Unlike A. orientalis or N. barkeri which showed consistency in their microbiota regardless of prey type, prey switching significantly affected β-diversity of bacterial composition in A. swirskii, with 56% of the microbiota changing. In short, our results confirmed the substantial influence of diet on host microbial construction in predatory species but also highlighted species differences in shaping the microbiota which are not necessarily related to prey specialization.

invertebrate microbiota

feeding habits

diet switching

16S rRNA

predator‒prey interaction

Host-associated microbial communities can contribute to host ecology and evolution [23]. The microbiota in a host may be determined primarily by stochastic neutral processes or by deterministic and selective processes [7]. Host deterministic selection is generally regarded as a strong force in shaping and stabilizing the bacterial communities of animals [6, 10, 48]. A specialist with narrow diet width may consistently acquire a specific set of microbes from food and form a long coevolutionary process with core microbiota [36]. While generalist hosts are more likely to sample a wider range of microbes associated through a variable diet [38, 46, 48]. Thus diet width may impact microbiota of a host directly or (particularly in specialist species) through a process of evolutionary adaptation.

A growing number of studies have emphasized that diet diversity could affect the diversity of microbial communities in generalist arthropods [27, 29, 45]. Particularly in phytophagous insects like Lepidopteran caterpillars, the microbial communities present at the surface of food plant influence host microbiota [14]. However, if these gut microbes are transient and unstable, such patterns may not signify evolved host-microbiome association [15].

Predatory arthropods are a group of beneficial natural enemies feeding on various diets and/or preys. They are considered to bring few microbes in the gut or entire body [15] or lack core microbiota in a relatively simple gut [41]. Due to limited research on carnivorous arthropods, their microbial composition can be unpredictable. Prey-associated microbiota could cause temporary changes in resident microbiota in host predators, while environmental microbiota could also stochastically integrate into predator’s gut both by ingestions of prey and predators. Changes in the microbiome of spider guts show the pronounced fluctuations in the microbiome associated with variation in prey; spider microbial assembly is dictated by the consumed prey, and different prey taxa remodel host microbiota, resulting in abundance shifts of rare microbial taxa in the spider's gut [23].

As a group of economically important biocontrol agents, predatory mites occupy a wide range of ecological niches from underground to aboveground where they feed on invertebrate prey which are often of similar body size to themselves. Here we consider whether predator mite microbiota comes mainly from the prey they consume or from inherent species differences which may be related to prey width. Specifically, how does the microbiota change when predatory mites alter diet, and how do species compare when exposed to the same diet?

We explored these issues in predatory mites which are carnivore arthropods with a highly variable degree of trophic specialization. Predatory mites from Phytoseiidae are commonly classified into several diet preference types, including Type I, specialized predators of only Tetranychus mite; Type II, selective predators of tetranychid mites and a few other preys as well as Type III, generalist predators without prey preference [31, 32]. Due to their tiny body size, simple structure of their guts, and fast transition of ingested substances, predatory mites are considered to change their microbome frequently in unpredictable environments [34]. We have already revealed that bacterial community is determined by both species and diet in two predatory mites [47], however, to our knowledge, little research has been undertaken on microbiota in response to diet width in predatory mites.

In this study, we considered a specialized predator (Phytoseiulus persimilis Athias-Henriot), a selective predator (Neoseiulus californicus McGregor), and three generalist predators (Amblyseius orientalis Ehara, Neoseiulus barkeri Hughes and Amblyseius swirskii Athias-Henrio) feeding on two types of diets, general Tetranychus prey (Tetranychus urticae Koch) and alternative prey (Carpoglyphus lactis L.) which was only fed by generalists. By investigating the bacterial community composition through high-throughput sequencing of the 16S rRNA gene, we explored the bacterial communities of predatory mites from the perspective of diet width and diet variation, addressing the question of how diet influences microbial community in predatory arthropods.

2.1 Mite husbandry

Five predatory mites, including P. persimilis, N. californicus, A. orientalis, N. barkeri, A. swirski had been maintained on T. urticae on bean plants in the Laboratory of Predatory Mites, Institute of Plant Protection, Chinese Academy of Agricultural Sciences over 10 years. Alternative prey C. lactis feeding on yeast were provided by Shoubonong Biotechnology Company (Beijing, China). P. persimilis and N. californicus only survived on T. urticae rather than C. lactis. All mites were reared at 25 ± 1 ℃, 70 ± 5% RH and L14:D10 photoperiod in the incubators (MLR-352H-PC, Ningbo, China).

2.2 Diet treatments and sample collection

We selected 100 gravid adults of each of the above five predatory mites to lay eggs in small arenas [47]. Approximately 200 eggs were collected for each species within 24 hours. To reduce maternal and environmental effects, we disinfected the surface of predatory mite eggs by soaking in 0.5% sodium hypochlorite for 1 min and rinsing with sterile water three times. Eggs were maintained on plastic film (d = 7 cm) on top of a soaked sponge (13 × 13 × 4 cm) in a plastic box (17.5 × 17.5 × 7 cm) covered by a mesh cloth (d = 5 cm, 250 mesh). To explore the microbiota transient after switching prey, the eggs of generalists A. orientalis, N. barkeri, A. swirski were randomly divided into two colonies feeding on T. urticae or C. lactis, respectively. We supplemented prey items every two days. The predatory mites were fed for three generations before collecting adult individuals. For each of 8 groups of predators and 2 groups of preys, we sampled 5–8 biological replicates, each of which involved a pool ogf 15–20 adult mites of both sexes. Samples were preserved in 100% ethanol in a − 20°C freezer.

2.3 Total DNA extraction and high-throughput sequencing

The total DNA from each pooled sample was extracted using the Qiagen DNeasy Blood & Tissue Kit (Cat#69506). The V4 region of the bacterial 16S rRNA gene was amplified with the primers 515F (5’-GTGCCAGCMGCCGCGG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) using DNA Polymerase (Phusion HF DNA Polymerase M0530S, NEB) under the following procedure: 98 ℃ for 30 s; 35 cycles of 98 ℃ for 10 s, 55 ℃ for 20 s, and 72 ℃ for 15 s; and 72 ℃ for 5 min [47]. PCR products were then purified using the Omega Bio-tek E.Z.N.A Cycle-Pure Kit (REF#D6492-02). All barcoded amplicon libraries were pooled on an Illumina NovaSeq 6000 system for PE250 paired-end sequencing (Sinobiocore Inc., Beijing, China).

Raw fastq files were filtered by Trimmomatic version 0.38 [4] and USEARCH fastaq_filter parameter in version 11 [9] to remove low quality reads. Merged sequences were then assigned to operational taxonomic units (OTU) using a 97% similarity cutoff in USEARCH UPARSE [8]. The unique sequences were denoised and de novo clustered into zero-radius operational taxonomic units (ZOTUs) with the unoise3 algorithm. The ZOTU table was normalized to the same number of reads per sample (20,000) to compare data from different samples. Processed sequences were mapped onto ZOTU sequences to calculate the presence and relative abundance of each ZOTU in every sample using the otutab command. The taxonomy was predicted for ZOTU sequences using the SINTAX classifier against RDP training set using confidence threshold of 0.8.

2.4 Data analysis

Both α-diversity and β-diversity metrics were calculated using the USEARCH package. The normalized abundances of ZOTUs for each sample were used to determine Shannon and Chao indices for each sample. Based on Shapiro-Wilk tests for data normality, we used Mann-Whitney tests or independent t tests to examine the effect of diet types on microbial diversity in different mite species. Kruskal-Wallis tests and one-way ANOVAs with Tukey’s multiple-comparison test were used to compare microbial diversity differences when mites with different feeding habits fed on the same diet. For comparing community structure, Vegan 2.4 package was used to calculate the Bray–Curtis dissimilarity metric and perform permutational multivariate analysis of variance (PERMANOVA), principal co-ordinates analysis (PCoA), and Anosim analysis. A phylogenetic tree of key microbial groups was built using the Neighbor-joining method in MEGA X with 1000 bootstraps. A likelihood score was edited on the interactive tree of life (iTOL) website [28]. UPGMA (Unweighted pair-group Method with Arithmetic Mean) was used to construct a cluster tree of the samples. A network analysis was performed using R and visualized in Gephi [2]. Spearman correlation scores were calculated and only kept robust (Spearman’s r > 0.6 or r < -0.6) and statistically significant (p < 0.05) correlations. Differential abundance analysis was conducted in DESeq2 to calculate read counts when predatory mites feeding on different prey and the data were visualized in R [30]. We then used SourceTracker (v1.0) based on Bayesian approach to estimate the microbiota source of mite species from their preys or progenitor [26]. Sloan’s neutral community model (NCM) was also constructed to evaluate the contribution of neutral processes in community structure [39]. Niche width analysis was performed on Tutools platform (http://www.cloudtutu.com).

3.1 Overview of sequencing results

A total of 3,058,293 reads were obtained from 49 samples, averaging 62,414 reads per samples after quality control. The rarefaction analysis indicated that sufficient sampling depth was achieved for all samples. Based on the threshold of 97% similarity and 0.1% relative abundance, the cluster analysis yielded 2,709 ZOTUs belonging to 21 phyla. In general, Firmicutes (43.37%) were the most abundant phylum in all samples; followed by Bacteroidetes (25.72%) and Proteobacteria (22.41%).

3.2 Phylogenetic and UniFrac analyses of core ZOTU

Compared to polyphagous predators (A. swirskii, N. barkeri and A. orientalis), ZOTUs in P. persimilis (the monophagous predator) were closer to N. californicus (the oligophagous predator) in terms of the evolutionary relationship among bacteria (Fig. 1A). Most ZOTUs (51/91) were shared among predators. The numbers of unique ZOTUs from the monophagous (1) or oligophagous predator (2) were fewer than for the polyphagous (12) predator (Fig. 1A; Fig. 2C). Eight ZOTUs from polyphagous predators were enriched for the clade belonging to the Bacteroidia. Bacterial communities from the three types of predators (polyphagous, monophagous, oligophagous) showed no strong clustering in the PCoA analysis (Fig. 1B). After the change of diet, bacteria in A. swirskii showed obvious diet-mediated differentiation (Fig. S1) but this was less apparent for the other generalist predators.

3.3 Host effect on predator microbiota

No difference was found in microbial diversity among the eight groups of predatory mites (Shannon index: χ²₍₄₎ = 6.957, p = 0.138; Chao index: χ²₍₄₎ = 2.993, p = 0.559) (Fig. 2). Furthermore, microbial α-diversity of three polyphagous species was altered after switching to C. lactis prey (Shannon index: F_(2,12) = 8.743, p = 0.005). Diet change decreased the microbial diversity of N. barkeri (Shannon index: t = 2.708, p = 0.029), whereas no change was found in A. orientalis (Shannon index: U = 12.000, p = 0.917) and A. swirskii (Shannon index: t = 0.755, p = 0.472) (Fig. 2A & B).

Predatory mites showed significant differentiation in bacterial communities when feeding on T. urticae (R = 0.304, p = 0.001, Fig. 3A). Among them, bacterial composition showed no difference between the monophagous predator P. persimilis and the two polyphagous predators A. orientalis and N. barkeri (R = 0.156, p = 0.118; R = 0.284, p = 0.055, respectively). In contrast, microbiota of the monophagous predator was significantly different from A. swirskii (R = 0.284, p = 0.005). The oligophagous predator had a similar microbiota to the polyphagous predators A. orientalis and N. barkeri (R = 0.004, p = 0.375; R = 0.096, p = 0.128), but not A. swirskii (R = 0.272, p = 0.034). The three polyphagous predators had a different microbiota when feeding on T. urticae (R = 0.388, p = 0.004, Fig. 3B), compared with a consistent microbiota after changing to the prey C. lactis (R = 0.061, p = 0.105, Fig. 3C). Prey switching only affected the β-diversity of bacterial composition of A. swirskii (R = 0.340, p = 0.044, Fig. 3F), but not A. orientalis or N. barkeri (Fig. 3D & E).

The relative abundance of core bacteria in monophagous and polyphagous species was different. The most abundant microbes in monophagous and oligophagous predatory mites were Gammaproteobacteria (41% and 23% respectively) and Clostridia (27% and 33% respectively), whereas Clostridia and Bacteroidia were the top two classes in the polyphagous species (Fig. 4A, Table. S1). Even though the overall microflora was different among polyphagous species, the main core microflora did not change after switching prey (Table. S1).

3.4 Diet effect on bacterial communities of polyphagous predators

When altering prey, body color and shape was changed in the three polyphagous predators (Fig. 4B). We further tracked bacterial source in relation to primary and alternative prey based on the similarity of the microbiota. Predatory mites with different feeding habits obtained a similar proportion of bacteria from their prey. However, they not only obtained microbial taxa from prey, but also from unknown sources (Fig. 4C-F). Different patterns were observed in the three polyphagous species (Fig. 4B). For example, although T. urticae provided 74% of microbes to predator the A. orientalis, the alternative prey C. lactis only transferred 18% of microbes to this predator, while 68% of bacteria were retained from the original colony which fed on T. urticae and 14% came from unknown sources (Fig. 4D). Conversely, 56% of the bacteria of A. swirskii were from the new C. lactis prey, while only 10% was passed on from progenitors feeding on the original prey, suggesting that microbiota changed more substantially in A. swirskii feeding on the new prey when compared to the other two polyphagous predators (Fig. 4F).

Diet change led to significant changes in the composition and content of bacterial communities (Fig. S2). The bacterial content from 5 orders changed significantly in A. orientalis, including reductions of Alteromonadales, Micrococcales, Xanthomonadales while there were increases in Micrococcales and Selenomonadales (Fig. 5A, p < 0.001). The changing relative abundance of the different microbial groups was not consistent across the predators (Fig. 5B & C).

3.5 Effect of host and diet on network complexity

In contrast to polyphagous predators, 65 common ZOTUs from the monophagous and oligophagous mites had highest network density of 33.03 and least modularity of 0.09 (Table 1; Fig. 6A-C). For the generalist species, diet switching had a strong effect on bacterial diversity and network complexity. After changing prey, common ZOTUs reduced from 71 to 63, resulting in network density changing from 0.40 to 0.49 and modularity from 0.27 to 0.20 (Fig. 6D & E, Table 1). This indicates that bacteria connectivity was closer among polyphagous species after feeding on C. lactis. When prey switched to C. lactis, the 67 common ZOTUs in A. swirskii was the lowest value among three polyphagous species, while the microbial network density of 0.58 and the average degree of 38.00 was the highest, even though the degree of modularity was not altered at 0.18 (Fig. S3, Table S2). The network results indicated that bacterial structure influenced by diet was only evident in A. swirskii rather than being common to all polyphagous species.

Table 1

Bacterial co-occurrence network characteristics in different diet treatments. C: polyphagous predators feeding on *C. lactis*; T: polyphagous predators feeding on *T. urticae.*
Group	Average degree	Diameter	Density	Modularity
Monophagous and Polyphagous	25.55	9	0.29	0.22
Oligophagous and polyphagous	23.66	6	0.27	0.27
Monophagous and Oligophagous	33.03	6	0.42	0.09
Polyphagous (C)	29.58	5	0.49	0.20
Polyphagous (T)	27.83	4	0.40	0.27

The neutral community model (NCM) successfully estimated a large fraction of the relationship between the occurrence frequency of ZOTUs and their relative abundance variation, with 10.5% and 63.8% of explained community variance for monophagous, oligophagous predators, and 69.0%, 46.1%, 61.1% for polyphagous predators, respectively (Fig. 7A-G). These results suggest that stochastic processes were important in shaping the microbial community assembly in oligophagous and polyphagous predators. The Nm-value was higher for microbial taxa in the polyphagous predators (Nm = 17,970; 11,841; 21,168) than the monophagous predator (Nm = 9,627), suggesting higher microbial dispersal in polyphagous predators than in the monophagous predator. There was no difference with diet widths in niche width. However, the niche width of A. swirski was significantly lower than other species (on T. urticae: F_{(4, 18)} = 3.232; p = 0.036; on C. lactis: F_{(2, 12)} = 6.608; p = 0.012, Fig. 7H, I). The niche width of prey C. lactis was significantly higher than T. urticae (t = -4.671; p = 0.002, Fig. 7J).

Diet is the main source of microbiota in predatory mite hosts

Many studies have found dietary patterns can introduce different bacteria to the hosts [1, 7, 40], thus diets might most likely determine the host-specific intestinal bacterial community. In our study, diets also shape the microbiota in predatory mites from the results of microbial source track and microbiota comparisons among different mites. The comparison of microbiota source indicated about half of microbes from their prey T. urticae regardless of the types of predatory mites. In our hypothesis, we expected to see exactly independent microbial profiles in specialist mite. Whereas only one distinct ZOTU was found in comparison with generalist mites, suggesting large part of microbiota was provided by diet which is the same feeding to other mites. Although some microbes are reported to adjust host’s diet adaptation [20, 43], we did not find any signal of diet-associated microbes in response to specific diet in predatory mites. Generally, little relationship occurred between mite feeding types and their overall microbial species in predatory mites. Theoretically, impacts of the microbes on their hosts may range along a continuum from strong to weak dependence until to no association. Fruit flies and stick insects was found less dependency on gut microbes for digestion [11, 37], indicating that not all animals need a microbiome [15]. However, different core microbiota from diets is worth exploring whether some unique microbes could regulate the diet metabolism in predatory mites in further study.

Host species resulted in microbiota differentiation among generalist predators

In our study, we found that microbiota in polyphagous predators was only stable in A. orientalis and N. barkeri rather than A. swirskii. During the interaction of microbiota and hosts, various factors including stochastic, neutral, or selective processes could determine microbial community compositions of the host [7]. Among them, hosts deterministic selection for specific diet-acquired microbiota indicate functional association and strong dependence [24]. In our study, we also highlighted the host-imposed selection to the microbiota of predatory mites that different host species affected the core microbiota and network structure. For example, microbiota of A. swirskii was strikingly affected by their diet, suggesting fast replacement of microbiome in response to diet alteration. In predatory insects, dragonfly also do not have consistent diet-specific or stage-specific associations with gut bacterial communities, and dietary specialization and spatial variation in bacterial communities suggest the passive process [7]. Although microbiota in A. swirskii could be changed with the diets, host species itself is determinative to fluctuation of microbial community after prey switching. Different predator species had various adaptations to alternative preys. The niche width of A. swirskii was lower than other predators. Niche width refers to the sum of different resources used by a population in a community. In the case of less available resources, the species with higher niche width would generally enable to get sufficient resources. However, rapid changes in microbiome may help A. swirskii to obtain sufficient resources in disadvantageous environment [12].

Unknown environment influences bacterial community of the predators

In addition to dietary effect, environmental factor cannot be ignored in shaping the host microbial community. Studies from animals have shown that an important part of the microbiome is originated from non-dietary sources [35], such as herbivorous insects take up specific symbiont bacteria from the environment or directly from the soil [25]. Interestingly, caterpillars acquire a large part of their microbiome directly from the soil, rather than from the plants they are feeding [16]. In predatory mites, the source of a part of bacteria was unknown, indicating environmental and stochastic factors in microbiota construction. This “unknow” part was variable in predators, equivalent to 14% in A. orientalis, 27% in N. barkeri and 34% in A. swirski. It suggested the stochastic role of the environment played in microbiota construction of predatory mites.

Core microbiota of predatory mites

Even though host-associated microbial communities generally present substantial fluctuations with different conditions [17, 18, 33], hosts often associate with a specific set of microbes as core microbiota [13, 21, 44]. The taxa and abundance of these core microbes are often stable and conserved. It can be considered as a host species-, genus- or family-specific trait that may play key roles in determining host fitness and evolutionary potential [5, 19, 42]. The relationship between core microbiota and host diet width has been studied in insects. Moraxellaceae, Enterobacteriaceae, and Pseudomonadaceae were highly prevalent in specialist beetle species, while Rickettsiaceae associated exclusively with generalist beetles [3]. In our study, Clostridia was abundant and consistent in polyphagous predatory mite species, no matter changed diet or not. On the other hand, Gammaproteobacteria was abundant in monophagous predator, suggesting a possible relationship between diet width and these microbes. The high classification level of Gammaproteobacteria and Clostridia reveals a long-term and stable evolutionary relationship between microbiota and host diet width.

In addition to stable core microbiota, some typical bacterial taxa are enriched or depleted in response to different preys [23]. The abundance change of host bacterial taxa may due to the alteration of microbiota content in different preys, or the proliferation of some bacteria caused by different food nutrients. In mammals, different microbial groups are believed to specialize in the utilization of specific dietary substrates, in part because they tend to increase in abundance when these substrates are enriched in the host’s diet. For example, Bacteroidetes are associated with high-protein diets, while Firmicutes are associated with high-fiber diets [22]. Typical bacterial taxa were also enriched or depleted in predatory mites when they feeding on different preys. When feeding on T. urticae, bacterial species increased in predatory mites were most of plant endophytic bacteria, indicating the microbial transferences in tritrophic relationship among plant, spider mites and predatory mites.

In short, our research revealed that both host species and dietary variation modify the microbial composition of predatory mites. Predator feeding width shapes its core microbiota, but not overall microbiome. Although predatory mites had stable core microbiota, fluctuation of microbiome indicated that predatory mites have weak dependence on their microbial communities which were greatly influenced by diet. The association is closely related to predatory mite species.

Acknowledgments

We appreciate Prof. Ary Hoffmann to edit our paper. This work was supported by National Natural Science Foundation of China (Grant No. 32070402), Beijing Natural Science Foundation (Grant No. 6222052), Beijing Agriculture Innovation Consortium (BAIC01-2022) and Agricultural Science and Technology Innovation Program, CAAS “Protection Application of Insect Natural Enemies”.

Author Contributions

Hong Yan: Investigation, Formal analysis, Writing- Original draft preparation, reviewing and editing. Bo Zhang: Conceptualization, Formal analysis, Supervision, Writing- Original draft preparation, reviewing and editing. Guoshu Wei: Validation, Supervision and Editing. Xuenong Xu: Validation, Supervision and Editing. Endong Wang: Validation, Supervision and Editing.

Conflict of interest

The authors have declared that no competing interest exits.

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Both host and diet shape bacterial communities of predatory mites

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Abstract

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1. Introduction

2. Materials And Methods

2.1 Mite husbandry

2.2 Diet treatments and sample collection

2.3 Total DNA extraction and high-throughput sequencing

2.4 Data analysis

3. Results

3.1 Overview of sequencing results

3.2 Phylogenetic and UniFrac analyses of core ZOTU

3.3 Host effect on predator microbiota

3.4 Diet effect on bacterial communities of polyphagous predators

3.5 Effect of host and diet on network complexity

Discussion

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References

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