Introduction
Insects, with their inclusive variety of species, ability to acclimate to different environments, and significant biomass, play a vital role in global ecosystems. The complex link that exists between insects and the bacteria, fungi, viruses, protozoa, and archaea that live in their symbiotic relationship affects the diversity and adaptability of these organisms (Naveed et al., Reference Naveed, Siddiqui, Longqin and Xiaolei2020). Microbial symbionts associated with insects significantly contribute to their diversity and evolutionary pathways (Ali et al., Reference Ali, Muhammad, Bala, Wang, Chen, Peng and Hou2018; Id et al., Reference Id, Zhu, Zhou, Wu, Chen and Cao2021). These symbionts perform numerous vital roles, such as influencing the use of host plants (Liu et al., Reference Liu, Zhang, Zeng, Yu, Lin and Huang2021; Naveed et al., Reference Naveed, Liu, Lu and Huang2024), providing resistance to parasites or fungal pathogens (Tsuchida et al., Reference Tsuchida, Koga, Horikawa, Tsunoda, Maoka, Matsumoto, Simon and Fukatsu2010), enhancing tolerance to thermal stress (Liu et al., Reference Liu, Zhang and Huang2022), modifying body colouration (Tsuchida et al., Reference Tsuchida, Koga, Horikawa, Tsunoda, Maoka, Matsumoto, Simon and Fukatsu2010), and influencing reproduction dynamics (Ali et al., Reference Ali, Muhammad, Sanda, Huang and Hou2019; Liu et al., Reference Liu, Zhang, Zeng, Yu, Lin and Huang2021, Reference Liu, Zhang and Huang2022). There is ample evidence regarding the evolutionary dynamics of the symbiotic relationships that occur among individual microbial symbionts and their animal hosts (Bennett and Moran, Reference Bennett and Moran2015). However, the mechanisms governing community structure in long-term host–microbiome symbiosis still need to be understood (Groussin et al., Reference Groussin, Mazel, Sanders, Smillie, Lavergne, Thuiller and Alm2017). While a number of studies have planned diet as the primary driver influencing bacterial symbiotic composition (Ley et al., Reference Ley, Hamady, Lozupone, Turnbaugh, Ramey, Bircher, Schlegel, Tucker, Schrenzel and Knight2008; Yatsunenko et al., Reference Yatsunenko, Rey, Manary, Trehan, Dominguez-Bello, Contreras, Magris, Hidalgo, Baldassano and Anokhin2012; Carmody et al., Reference Carmody, Gerber, Luevano, Gatti, Somes, Svenson and Turnbaugh2015), debates persist regarding the relative contributions of host genetics vs. environmental factors. Some authors suggest a minor impact of host genetics compared to ecological factors (Degnan et al., Reference Degnan, Pusey, Lonsdorf, Goodall, Wroblewski, Wilson, Rudicell, Hahn and Ochman2012; Yatsunenko et al., Reference Yatsunenko, Rey, Manary, Trehan, Dominguez-Bello, Contreras, Magris, Hidalgo, Baldassano and Anokhin2012), whereas others offer evidence supporting a more significant role of host genetics (Blekhman et al., Reference Blekhman, Goodrich, Huang, Sun, Bukowski, Bell, Spector, Keinan, Ley and Gevers2015). Furthermore, the configuration of bacterial populations within insects can be inclined by various factors, including food resources, exposure to pathogenic microbes, environmental conditions, and host plants (Wang et al., Reference Wang, Berdy, Velasquez, Jovanovic, Alkhalifa, Minbiole and Brucker2020; Naveed et al., Reference Naveed, Liu, Lu and Huang2024).
Many different poisonous and indigestible substances can be found in plant tissues, which forces insects that are herbivorous to develop adaptive strategies for different plant types. These strategies often involve symbiotic relationships that allow insects to exploit various host plants effectively (Liu et al., Reference Liu, Shen, Yu, Li, Liu and Xu2020). Latex, an antifeedant primarily found in plants, is a potent deterrent among the many defences against herbivores that plants produce (Dillon and Dillon, Reference Dillon and Dillon2004). The latex of various Ficus species contains several proteins, such as oxidases, protease inhibitors, chitinases, and cysteine protease, which are crucial in defending against herbivores (Konno, Reference Konno2011). Cysteine proteases such as ficin and bromelain, abundant in the latex of Ficus, exhibit toxicity against herbivores, contributing to the plant's defence mechanism against insects (Konno et al., Reference Konno, Hirayama, Nakamura, Tateishi, Tamura, Hattori and Kohno2004). Ficus microcarpa, a widely cultivated ornamental tree, thrives in tropical regions worldwide and is commonly used in landscaping. It is frequently observed in cultivated and naturalised specimens near human activities, such as houses, buildings, and gardens (Starr et al., Reference Starr, Starr and Loope2003). Various pests, mainly insects, pose a significant threat to the development of this plant species. However, this plant species faces substantial threats from multiple pests, primarily insects. For example, Mycopsylla sp. is known to nourish exclusively on Ficus, and its nymphs yield a gummy substance at the inferior exterior of the leaves (Newman, Reference Newman2004). A species of Ormyrus Westwood (Ormyridae) was also reported on F. microcarpa in Europe (Koutsoukos et al., Reference Koutsoukos, Compton, van Noort, Avtzis and Askew2024).
Despite clear evidence of latex's deterrent effect on herbivory and the prevalence of latex-producing plants, the question of how herbivores navigate the inevitable inclusion of latex in their diets still needs to be explored. We hypothesise that bacterial symbionts enable insects to overcome plant defences by detoxifying plant toxins, such as latex, and facilitating nutrient acquisition. We predict that different insect species will harbour distinct bacterial symbionts specialised for these adaptations, reflecting their specific phylogenetic and dietary adaptations. Exploring these associations, with a keen consideration of taxonomy and feeding habits, could unlock crucial insights into this intriguing facet of herbivory. Some insects have evolved specific bacterial symbiotic relationships that are pivotal in combating phytotoxins (Naveed et al., Reference Naveed, Siddiqui, Longqin and Xiaolei2020). These symbionts are adept at breaking down chemical compounds found in latex, potentially serving as a mechanism for herbivores to adapt to latex-producing plants. Moreover, certain insects harbour bacterial symbionts that offer vital nutrients (Zhang et al., Reference Zhang, Lin, Liu, Lu, Qiao and Huang2023). This partnership not only aids in the breakdown of plant compounds but also enhances the adaptability of the insect's hosts to challenging environmental conditions. The complicated web of connections between the insects and their bacterial symbionts could provide a comprehensive understanding of how herbivores cope with latex-induced challenges and shed light on the broader dynamics of plant–insect interactions.
The expansion of high-throughput sequencing makes it possible to discover complete bacterial populations in insects with great ease. This work used 16S rRNA gene amplicon sequencing in order to examine the bacterial communities living within insects that are feeding on F. microcarpa. Furthermore, insights gained from studying these symbiotic relationships may also offer promising avenues for controlling and managing these insects’ pests, contributing to more sustainable and effective pest management strategies.
Materials and methods
Sample collection
From 2022 to 2023, we collected extensive insects from the latex-producing plant F. microcarpa in Fuzhou, Fujian, China. In this study, samples comprised of a diverse range of species, comprising Perina nuda, Ocinara albicollis, Teleiodes saltuum, Bambusiphila vulgaris, Homoeocerus angulatus, Harmostes reflexulus, Aphis aurantii, Gynaikothrips uzeli, and Gynaikothrips ficorum. These specimens were considerately selected to comprehensively represent insects that feed on F. microcarpa. Table 1 describes the comprehensive sample data used in a recent study.
Detailed sample information used in this study
DNA extraction
Larger insect samples only needed one individual, but we used two to three individuals for DNA extraction from small-bodied insects. All of the collected samples experienced a detailed washing process that involved three washes by using ultra-pure water. Afterwards, the total genomic DNA were extracted by using a DNeasy Blood and Tissue Kit from Qiagen. To maintain the integrity of our data, a sample of deionised water was used for negative control. The extraction of DNA was performed at an ultra-clean worktable to evade DNA contagion through environmental factors. The bacterial universal primers 8F and 1492R (Ledbetter et al., Reference Ledbetter, Connon, Neal, Dohnalkova and Magnuson2007) were used to ensure that the DNA extraction was successful, a polymerase chain reaction (PCR) amplification method was employed for this purpose. Deionised water served as an additional negative control to further guarantee data accuracy. A 25 μl reaction mixture of 1 μl of DNA, 2.5 μl of 10× LA PCR Buffer-II (Mg2+), 0.5 μl of dNTP, 0.5 μl of each primer (10 μM), 0.5 μl of TaKaRa LA-Taq (5 μM μl−1) by TaKaRa Bio-Inc., Japan, and 19.5 μl of water was used for PCR amplification by ProFlex-TM Base (Applied Biosystems, Inc., Waltham, USA). PCR cycling conditions were used as follows: preliminary denaturation for 4 min at 94°C; also includes 30 rounds of denaturation for 30 s at 94°C; annealing at 65°C for 40 s; 90 s for the process of extension at 72°C; and 10 min of eventual extension at 72°C. PCR results were observed on a 1% agarose gel, and positive insect samples at about 1500 bp were stored at −20°C in a refrigerator for further 16S library synthesis. Particularly, the samples that were used as a negative control yielded no bands, demonstrating that DNA extraction has a lack of impurity during the process.
16S rRNA gene sequencing and amplification
Our focus was on amplifying the V3–V4 hypervariable area of the 16S rRNA gene using specific primers 338F and 806R (Liu et al., Reference Liu, Zhang and Huang2022). The amplification process involved two PCR steps, first targeting the gene regions of interest and the second incorporating indices and adapter sequences. Positive PCR underwent purification and homogenisation to create a sequencing library, facilitating high-throughput sequencing.
Analysis of sequencing data
Sequencing data underwent merging of paired-end readings using FLASH and subsequent refinement and filtering by Trimmomatic (Bolger et al., Reference Bolger, Lohse and Usadel2014). Chimaera sequences were eliminated, and remaining sequences were listed into operational taxonomic units (OTUs) using USEARCH. To interpret the differences in sequence depth between samples, rarefaction normalisation was used. The enduring sequences were categorised into OTUs with 97% similarity by USEARCH v10.0. OTUs that were counted for less than 0.005% of all sequences were removed (Bokulich et al., Reference Bokulich, Subramanian, Faith, Gevers, Gordon, Knight, Mills and Caporaso2013). Taxonomic classifications were applied using the RDP classifier, supplemented by BLAST searches against GenBank sequences for validation.
Diversity and statistical analysis
Various diversity indices were calculated using Mothur (Schloss et al., Reference Schloss, Westcott, Ryabin, Hall, Hartmann, Hollister, Lesniewski, Oakley, Parks and Robinson2009), including abundance-based coverage estimator (ACE), Chao1, Shannon, and Simpson indices. These indices included the ACE and Chao1 for estimating richness of species and the Simpson and Shannon directories for calculating community diversity. Higher values of Chao1, ACE, and Shannon, as well as lower Simpson values, imply greater diversity. Given the diverse symbiotic compositions, insect samples were compared using the Student's t-test.
We used beta diversity analysis to compare symbiotic bacterial compositions across different sample groups. For this goal, we used weighted UniFrac distances, which take into account phylogenetic information on the OTUs. These metrics help out to evaluate community variation and evaluation of different indices of beta diversity, taking into account both the existence and abundance of bacteria (Lozupone et al., Reference Lozupone, Lladser, Knights, Stombaugh and Knight2011).
To investigate bacterial community changes across all samples, as well as three different insect orders, non-metric multidimensional scaling (NMDS) was employed to cluster the communities of bacteria in the package vegan v2.3.0 (Looft et al., Reference Looft, Johnson, Allen, Bayles, Alt, Stedtfeld, Sul, Stedtfeld, Chai and Cole2012) and generated two-dimensional plots using ggplot2 v3.1.1 (Wickham, Reference Wickham2016) by using R programming environment version 3.1.1 (Team RC, 2014). NMDS is trustworthy when the stress value is less than 0.2. The Circos sample and species relationship map was created using the Circos-0.67-7 (http://circos.ca/). We employed permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) to identify statistically noteworthy differences in symbiotic bacterial composition among the above explained classification systems. The weighted UniFrac and Bray–Curtis distance matrices were utilised in these studies. ANOSIM and PERMANOVA were achieved by the anosim and adonis functions with the vegan v2.3.0 package (Oksanen et al., Reference Oksanen, Blanchet, Kindt, Legendre, Minchin, O'Hara, Simpson, Solymos and Wagner2015) in the R v3.1.1 (Team RC, 2014). The R value in ANOSIM nearer to one recommends greater variation across groups than within them (Anderson and Walsh, Reference Anderson and Walsh2013). A higher value of R 2 in PERMANOVA showed that the grouping variable is very important in clarifying overall variation (Anderson, Reference Anderson2014). P-values of less than 0.05 for PERMANOVA and ANOSIM represent high test reliability.
PICRUSt2 v2.3.0b0 (Douglas, Reference Douglas2006) was used to forecast the composition of the functional genes in order to investigate prospective functional changes in symbiotic bacterial communities. This entailed comparing bacterial species configuration data achieved from 16S rRNA gene sequences. Data obtained through PICRUSt2 were analysed at Kyoto Encyclopedia of genes and genomes (KEGG) (Kanehisa et al., Reference Kanehisa, Goto, Sato, Furumichi and Tanabe2012) orthology level 3. We used STAMP (Parks et al., Reference Parks, Tyson, Hugenholtz and Beiko2014) v2.1.3 to analyse relative abundance differences. Individual t-tests were done between groups, with a P-value of ≤0.05 representing statistical significance.
Results
Analysis of 16s rDNA sequencing results
We obtained 2,433,868 high-quality sequences through 16S rRNA gene amplicon sequencing from 33 distinct DNA samples of various insects. On average, each sample yielded approximately 73,753 reads. After sequence filtering, 2,294,886 reads were retained, per sample averaging around 69,542 reads (table S1). The gene assemblies paired sequences of the 16S rRNA represent a typical length of 421 base pairs.
Cluster analysis, using a 97% sequence match threshold, identified 1493 OTUs (table S2). These OTUs encompassed 24 phyla, 49 classes, 130 orders, 257 families, and 552 genera. Evaluation through the Shannon index rarefaction curve (fig. S1) signifies the adequacy of sequencing volume and saturation of sequencing depth. Further increasing the sample volume is not anticipated to yield additional OTUs.
Comparison of alpha and beta diversity in samples
Alpha diversity indices were engaged to assess species richness (ACE and Chao1) and bacterial diversity (Simpson and Shannon) across all the samples (table S2). The analysis exposed prominent differences in the microbial communities between the several samples (fig. 1, table S2). The four indices of alpha diversity were widely spread through all samples, extending from 177.61 to 1362.67 (927.12 ± 59.91) for the ACE index, from 169.2 to 1374.5 (938.34 ± 63.09) for the Chao1 index, from 0.72 to 8.68 (6.27 ± 0.42) for the Shannon index, and from 0.10 to 0.99 (0.85 ± 0.04) for the Simpson index (table S2). The ace and Chao1 indexes demonstrated the abundance of symbionts in P. nuda (summer) was maximum monitored by T. saltuum, P. nuda (winter), G. ficorum (winter), O. albicollis, G. ficorum (summer), G. uzeli, H. angulatus, A. aurantii, H. Reflexulus, and B. vulgaris, which showed the lowermost richness. The values of samples in the Chao1 and ace indices were expressively diverse from each other (fig. 1A, B). All of the previously mentioned indices were proved that P. nuda (summer) had the maximum levels of the diversity and microbial community richness (fig. 1C, D). Significant variances were detected when relating alpha diversity indexes through distinct insect orders and seasons (table S2).
Alpha diversity indices across all groups: (A) ACE index, (B) Chao1 index, (C) Shannon index, and (D) Simpson index.
At the OTU level, the samples' beta diversity showed similarities and variations in the species composition and community structure. We considered NMDS to relate the bacterial community differences centred on Weighted UniFrac distance metrics across all the studied samples and three insect orders. The stress value was 0.0285 for both categories (fig. 2). The abscissa and ordinate axes in scatter plots represent two vital variables determining sample differences. We employed PERMANOVA and ANOSIM to statistically judge the differences in bacterial populations across various samples and insect orders (table 2). These studies revealed significant alterations in bacterial populations through all sample groups.
Beta diversity based on weighted UniFrac distance of bacterial population displayed in NMDS plots for (A) all insect groups and (B) three different insect orders.
Results of ANOSIM and PERMANOVA based on Bray–Curtis and weighted UniFrac distances
Statistically significant P values (P < 0.01) are highlighted in italics; see table 1 for detailed sample information.
Comparison of microbial community composition at different taxonomic levels
The composition of the community for each insect sample was analysed at the phylum and genus levels (fig. 4). The ‘Others’ category contains a limited number of phyla (0.01%) that are only existent in specific samples which have less abundance. At the phylum level, insect samples were primarily confined to Proteobacteria (54.31%), Firmicutes (23.60%), Bacteroidota (8.73%), Actinobacteriota (3.32%), and Acidobacteriota (2.58%). Proteobacteria were principally detected across all samples, mainly in the species of hemipteran with higher abundance and one lepidopteran species (B. vulgaris) (figs 3A and 4A).
Plot shows the OTUs distribution in ternary form: (A) across three distinct insect orders and (B) Venn diagram at the OTU level for three distinct insect orders.
Taxonomic diversity and number of symbiotic bacteria associated with each sample: (A) at the phylum level and (B) on the genus level.
At the genus level, 22 highly prevalent genera were identified in all samples (fig. 4B), with the top ten genera being Burkholderia and Pseudomonas, followed by Acinetobacter, Muribaculum, Bacteroides, Buchnera, Dubosiella, Lactobacillus, Staphylococcus, and Oscillospiraceae. Notably, Burkholderia was found in only two kinds of bugs (98% in H. reflexulus and 61% in H. angulatus) with higher abundance (fig. 5, table S3); on the other hand Acinetobacter was mainly detected in one species of Lepidoptera (79% in B. vulgaris) and also found in different species of insects with low abundance (fig. 5, table S3). Pseudomonas was detected in all insect orders, but its relative abundance varied in all samples except for H. reflexulus. Muribaculum was detected in two insect orders, Thysanoptera and Lepidoptera, with high abundance, but in hemipterans, these were detected in very low abundance. Intriguingly, Buchnera was exclusively detected in aphid species with high abundance (figs 4B and 5). In contrast, all the genera Bacteroides, Dubosiella, Lactobacillus, Staphylococcus, and Oscillospiraceae were detected in all samples but with less abundance (fig. 5, table S3).
Circos displays the distribution percentage of the top ten genera in each sample and the distribution percentage across samples. GFw, G. ficorum (winter); PNw, P. nuda (winter); PNs, P. nuda (summer); TS, T. saltuum; OA, O. albicollis; GFs, G. ficorum (summer); HA, H. angulatus; GU, G. uzeli; AA, A. aurantii; BU, B. vulgaris; HR, H. reflexulus.
Notably, Proteobacteria were prevalent among all samples, predominantly in species of hemipteran with the higher abundance rate (fig. 3A). From the 1493 OTUs, maximum OTUs were detected in P. nuda (summer and winter), followed by T. saltuum, G. ficorum (winter), O. albicollis, G. ficorum (summer), G. uzeli, A. aurantii, H. angulatus, H. reflexulus, and B. vulgaris (table S2). In total, 1387 OTUs were common between the three orders, as represented in Venn diagram, while only two OTUs were specific to Lepidoptera. In contrast to the comparisons, Thysanoptera and Lepidoptera shared more OTUs (fig. 4B).
Deviation of dominant symbionts and their functional prediction
All collected samples showed differences in relative abundance and composition of dominating symbionts (table S3). Among the five samples, G. ficorum (summer), G. uzeli, G. ficorum (winter), P. nuda (summer), and O. albicollis, the dominant symbiont was Pseudomonas. Buchnera was detected in A. aurantii, Burkholderia was found in H. reflexulus and H. angulatus, and Acinetobacter was found in T. saltuum and B. vulgaris. In contrast, Muribaculum was found dominant in P. nuda (winter) (fig. 5, table S3). Seasonal variations were observed in the abundance of bacterial genera in two species of insects, G. ficorum and P. nuda. Specifically, Bacteroides and Pseudomonas showed higher levels in summer than in winter, whereas Muribaculum and Lacticaseibacillus were more abundant in winter than in summer (fig. S2).
Results of the PICRUSt2 examination are represented in fig. S3. The t-test conducted on prevalent functional gene pathways across the five comparison groups, categorised by dominant symbionts, discovered a major rise in the abundance of genes linked with ‘metabolic pathways’ (fig. S4). Pseudomonas displayed remarkably higher relative abundance in ‘biosynthesis of secondary metabolites’, ‘metabolic pathways’, and ‘biosynthesis of antibiotics’ compared to Burkholderia, Buchnera, and Muribaculum (figs S4D, E, G). Acinetobacter displayed particularly higher richness in ‘metabolic pathways’, ‘biosynthesis of secondary metabolites’, ‘biosynthesis of antibiotics’, and the biosynthesis of amino acids associated with Burkholderia (fig. S4B), but Burkholderia unveiled especially higher abundance in ‘microbial metabolism in diverse environments’, ‘ABC transporters’, and ‘Quorum sensing’ when equated with Acinetobacter, Pseudomonas, and Muribaculum (fig. S4B–D).
Discussion
This study symbolises the initial investigation of bacterial symbiotic communities in various insect species that feed on latex-rich toxic plants. The symbiotic relationships between insects and their bacterial communities are vital for survival. It is essential to recognise insects' morphology and physicochemical conditions, which can vary significantly (Zhao et al., Reference Zhao, Zhang, Chen, Wang, Lu and Cheng2018). These deviations may influence the distinctive bacterial ecology particular to each host (Mariño et al., Reference Mariño, Ospina, Verle Rodrigues and Bayman2018). Accepting these dynamics is important for an inclusive understanding of insect ecology. Recent advancements in microbiome investigation using high-throughput sequencing have made it easier to explore host–microbe interactions. Using 16S rRNA amplicon sequencing, this study provides the first in-depth look at the bacterial diversity and relationships among the insects that feed on the latex-producing plant F. microcarpa. However, a distinguished gap in the study differentiates variations in symbiotic bacteria among different species and their relationship with their host (Liu et al., Reference Liu, Zhang and Huang2022). Our understanding of the interface among diverse insects and their associated symbionts is currently limited due to the exploration of only a few models. Compared to other insects and herbivores, our samples exhibit a vibrant and diverse bacterial population. This underscore the exclusive and intricate bacterial ecosystems connected with these insects.
Studies have highlighted the significant impact of host diet and taxonomy on shaping symbiotic communities (Ley et al., Reference Ley, Hamady, Lozupone, Turnbaugh, Ramey, Bircher, Schlegel, Tucker, Schrenzel and Knight2008). While some research emphasises the predominant role of diet in structuring microbiomes, others suggest that common ancestry holds sway in determining microbiome community (Karasov et al., Reference Karasov, Martinez del Rio and Caviedes-Vidal2011; Zhao et al., Reference Zhao, Irwin and Dong2016). However, there is still a need for more studies on this subject. In this investigation, we examined symbiotic bacterial communities across different species of insects that share the same plant species. Notably, while observing the same plant species, variations in bacterial community structures were evident between different insect orders and within the same order. Previous studies found that Proteobacteria, Firmicutes, and Bacteroidota are normally establish inside numerous insects and mammals (Chen et al., Reference Chen, Du, Sun, Vimalanathan, Liang, Li, Wang, Lu, Li and Shao2018), as we reported Proteobacteria (54.3%), Firmicutes (23.6%), Bacteroidota (8.7%), and Actinobacteriota (3.32%) in all insect samples but their abundance was varied in all samples (fig. 4A). Proteobacteria often dominate the microbiota of wide range of invertebrates (Zhang et al., Reference Zhang, Gao, Zheng and Wang2022) including Harpalus pensylvanicus, Aspidimorpha sanctaecrucis (Schmid et al., Reference Schmid, Lehman and Lundgren2014), Hypothenemus hampei (Mariño et al., Reference Mariño, Ospina, Verle Rodrigues and Bayman2018), and Bactrocera dorsalis (Andongma et al., Reference Andongma, Wan, Dong, Li, Desneux, White and Niu2015). Proteobacteria are pivotal in carbohydrate degradation in the Wood Borer, Saperda vestita (Delalibera et al., Reference Delalibera, Handelsman and Raffa2005), synthesis of vitamins (McCutcheon and Moran, Reference McCutcheon and Moran2007), and pesticide detoxification in the fruit fly B. dorsalis (Itoh et al., Reference Itoh, Tago, Hayatsu and Kikuchi2018). The second most prevalent phylum, Firmicutes, abundant in termites and honeybees (Martinson et al., Reference Martinson, Danforth, Minckley, Rueppell, Tingek and Moran2011; Auer et al., Reference Auer, Lazuka, Sillam-Dussès, Miambi, O'Donohue and Hernandez-Raquet2017), enhances food resource metabolism, aiding energy conversion from the diet and assisting in cellulose and hemicellulose consumption (Brown et al., Reference Brown, Lamed, Morag, Borovok, Shoham, Klingeman, Johnson, Yang, Land and Utturkar2012). Bacteroidota, the third predominant phylum in our 16S rRNA dataset, is recognised for producing crucial enzymes like xylanase, glucanase, and mannanase, contributing to intricate carbohydrate metabolism (Dai et al., Reference Dai, Zhu, Luo, Song, Liu, Liu, Chen, Wang, Li and Zeng2012). Actinobacteria, also abundant, augment metabolic versatility, enabling the exploitation of diverse nutritional resources such as cellulose and hemicelluloses in termites (Schäfer et al., Reference Schäfer, Konrad, Kuhnigk, Kämpfer, Hertel and König1996), and they produce secondary metabolites with antibiotic properties (Gawande et al., Reference Gawande, Anandhan, Ingle, Roylawar, Khandagale, Gawai, Jacobson, Asokan and Singh2019). These fluctuations in phyla abundance might stem from varying species taxonomies and their respective feeding habitats.
At the genus level, we found that thysanopteran insects harbour Pseudomonas in high abundance except for G. ficorum (winter) (fig. 4B, table S3). This genus was also previously reported in the symbiotic bacteria of Thrips tabaci, accounting for 4.5% abundance (Gawande et al., Reference Gawande, Anandhan, Ingle, Roylawar, Khandagale, Gawai, Jacobson, Asokan and Singh2019). Pseudomonas, known for its ability to degrade latex, dominates in thrips species and other insect species (fig. S2B, table S3). This bacterial genus plays a vital role in helping its host detoxify harmful compounds, enabling the insect to feed on the host plant and obtain essential nutrients. On the other hand, Pseudomonas emerged as the most prevalent genus in Nesothrips brevicollis and Elaphrothrips curvipes, consistent with earlier studies on thrips (Dickey et al., Reference Dickey, Trease, Jara-Cavieres, Kumar, Christenson, Potluri, Morgan, Shatters, Mckenzie and Davis2014; Gawande et al., Reference Gawande, Anandhan, Ingle, Roylawar, Khandagale, Gawai, Jacobson, Asokan and Singh2019). The dietary differences among these species are notable: G. ficorum and G. uzeli primarily feed on F. microcarpa leaves, extracting phloem, while Ayyaria chaetophora, Dendrothripoides innoxious, and Nepenthes gracilips prefer flowers, and T. tabaci forages on onion leaves. These distinct dietary preferences may significantly impact the abundance of specific genera within their associated bacterial communities.
In hemipteran insects, we found that two species of bugs, H. reflexulus and H. angulatus, were highly abundant with Burkholderia (fig. 5, table S3). Largidae species were explicitly associated with Burkholderia strains. Riptortus pedestris consistently showed 93.1% Burkholderia presence over a 2-year survey, aligning with findings from a field study in Japan (Kikuchi et al., Reference Kikuchi, Meng and Fukatsu2005). Additionally, Burkholderia presence was reported in populations of the leafhopper Macrosteles striifrons (Ishii et al., Reference Ishii, Matsuura, Kakizawa, Nikoh and Fukatsu2013). The evolutionary origin of Burkholderia is believed to be traced back to the last common ancestor of Lygaeoidea, Coreoidea, and Pyrrhocoroidea (Gordon et al., Reference Gordon, McFrederick and Weirauch2016). However, exceptions were observed; for instance, Stenomacra tungurahuana displayed a substantial number of reads from Rickettsia, and a Dysdercus specimen exhibited an unusual microbiome with a high concentration of Bartonella (Gordon et al., Reference Gordon, McFrederick and Weirauch2016). Burkholderia strains are recognised for conferring various advantages to host fitness, comprising increased fecundity, provision of nutrients, enhanced insecticide resistance, and larger body size (Takeshita and Kikuchi, Reference Takeshita and Kikuchi2017). Aphid species in our study were abundant with Buchnera (fig. 5, table S3), which is crucial in providing nutrition (fig. S2A, table S3). Earlier studies stated that Pseudoxenodon bambusicola has lost its capability to get feed from Bambusa independently, relying on its symbiotic bacteria for nutritional support (Zhang et al., Reference Zhang, Lin, Liu, Lu, Qiao and Huang2023), enhanced insecticide resistance (Naveed et al., Reference Naveed, Siddiqui, Longqin and Xiaolei2020), the diversity of bacterial communities in hemipterans varied, potentially influenced by their feeding habitats, thus affecting the abundance of specific genera within their hosts and these symbionts help them to get nutrition from the indigestible host (Naveed et al., Reference Naveed, Liu, Lu and Huang2024).
Our findings revealed that lepidopteran insects were predominantly associated with three bacterial phyla: Proteobacteria, Firmicutes, and Bacteroidota (fig. 4A, table S3). However, prior examination has also identified Proteobacteria and Firmicutes as prevailing phyla in certain lepidopteran insects such as, Helicoverpa armigera, Lymantria dispar, Bombyx mori, and Manduca sexta (Gao et al., Reference Gao, Li, Luo, Zhang, Ji, Zhu, Wang, Zhang and Cui2019). Furthermore, Firmicutes symbolise a significant part of Solenopsis invicta and many other insects (Ishak et al., Reference Ishak, Plowes, Sen, Kellner, Meyer, Estrada, Dowd and Mueller2011), the same as some lepidopterous insect species harbouring Firmicutes. At the genus level, Pseudomonas substantially contributes to the symbiotic community of some lepidopterous samples (fig. 5, table S3). This genus is known for harbouring species capable of degrading natural latex, rubber, and alkaloids (Vilanova et al., Reference Vilanova, Baixeras, Latorre and Porcar2016). Staphylococcus is predominant in Lepidoptera of the families Noctuidae and Sphingidae and was detected in some lepidopterous species such as P. nuda and T. saltuum (fig. 5, table S3) (Visôtto et al., Reference Visôtto, Oliveira, Ribon, Mares-Guia and Guedes2009). We also detected Acinetobacter in lepidopterous species but highly abundant in B. vulgaris and T. saltuum (fig. S2B, table S3). This genus has been reported in H. armigera, demonstrating robust esterase activity promoting the metabolism of the insecticide cypermethrin, thus increasing insect resistance (Malhotra et al., Reference Malhotra, Dua, Saxena, Sangwan, Mukherjee, Pandey, Rajagopal, Khurana, Khurana and Lal2012). Additionally, Acinetobacter, isolated from Plutella xylostella larvae, may enhance the nitrogen-fixation capacity (Indiragandhi et al., Reference Indiragandhi, Anandham, Madhaiyan and Sa2008), and has similar functions observed in ants and termites, aiding carbon–nitrogen metabolism (Moreira-Soto et al., Reference Moreira-Soto, Sanchez, Currie and Pinto-Tomás2017). Both Pseudomonas and Acinetobacter genera collectively play a crucial role in detoxifying toxic chemicals for their hosts and facilitating nutrient absorption.
Various insects have been shown to exhibit seasonal changes in their bacterial communities (Wei et al., Reference Wei, Ishida, Miyanaga and Tanji2014; Sandeu et al., Reference Sandeu, Maffo, Dada, Njiokou, Hughes and Wondji2022), highlighting the significant influence of seasonal dynamics on insect-associated microbiota. Our findings illustrate seasonal changes in the bacterial communities linked with different insect species, G. ficorum and P. nuda, collected in both summer and winter. Seasonal deviations in symbiotic bacterial communities have been detected in different insects (Wei et al., Reference Wei, Ishida, Miyanaga and Tanji2014; Chen et al., Reference Chen, Su, Chen, Jiang and Qiao2019; Sandeu et al., Reference Sandeu, Maffo, Dada, Njiokou, Hughes and Wondji2022), this underscores the importance of seasonal dynamics in influencing bacterial communities associated with insects.
To predict metabolic capabilities of bacterial communities, we conducted KEGG ontology analysis, enlightening that many OTUs demonstrated substantial metabolic potential regardless of their relationship with specific insect samples (fig. S3). PICRUSt2 study shows no significant dissimilarities in the number of anticipated gene pathways in symbiotic communities of bacteria. This proposes that, based on the functional assessment generated using the 16S rRNA gene data, that may not be significant differences in the genetic pathways used by different bacterial communities within the examined environment. Proteobacteria was the dominant phylum across all species, mainly in hemipteran species with greater abundance (fig. 4A). Furthermore, phyla such as Proteobacteria and Firmicutes may perform pivotal roles in providing vital nutrition for various biological procedures in hosts, including reproduction, growth, and detoxification of toxic chemicals (Gurung et al., Reference Gurung, Wertheim and Falcao Salles2019; Chamankar et al., Reference Chamankar, Maleki-Ravasan, Karami, Forouzan, Karimian, Naeimi and Choobdar2023). These findings provide further support for the notion that these insects possess the capability to metabolise toxic substances like latex. The presence of these bacteria within insects highlights their need on symbiotic relationships with communities of bacteria to successfully break down and use toxic chemicals in the diet. This ability is critical for such insects, allowing them to extract nutritional compounds derived from plants that would otherwise be indigestible. It emphasises the importance of the bacterial community in regulating host physiology. Simultaneously, PICRUSt2 analysis showed no significant dissimilarities in the proportion of projected gene pathways in the bacterial communities among diverse insect species.
A recent study observed a diverse bacterial composition in all samples. Some insects primarily have detoxifying symbionts (symbiotic organisms alter a toxic substance's chemical composition or structure in a way that benefits the host organism). In contrast, others host nutritional symbionts (symbiotic organisms function to sustain host metabolism by supplying vital nutrients, such as amino acids or vitamin B, enabling hosts to subsist on diets lacking in essential nutrients) (fig. S3). Interestingly, both types of symbionts play a vital role in facilitating their hosts to extract nutrients from toxic plants. Despite this diversity, their core functions seem consistent, potentially explaining why these insects can consume the same host plant. These results enhance our understanding of symbiotic interactions in ecological dynamics and provide practical insights for sustainable pest management strategies.
Conclusions
Our investigation unveils the intricate relationship between diverse insects feeding on the latex-rich plant F. microcarpa and their symbiotic bacterial communities. By comparing bacterial communities through insects from three distinctive orders (Thysanoptera, Hemiptera, and Lepidoptera), all feeding on F. microcarpa, we uncovered substantial deviations in symbiotic bacterial presence, abundance, and composition influenced by multifaceted factors such as seasonal changes and host phylogeny. Remarkably, despite different insect species nurturing on the same plant, they harbour distinct bacterial communities yet exhibit consistent functional capabilities among these symbionts. These crucial symbiotic partnerships are pivotal in aiding hosts in extracting nutrients from toxic plants. Our findings deepen our understanding of the dynamic interactions among insects and bacteria, providing inferences for insect biology, ecology, and beyond. Moreover, these insights pave the way for further exploration into the practical applications of manipulating symbiotic relationships for pest management. By leveraging the functional abilities identified in these symbiotic partnerships, we may uncover innovative approaches for developing environmentally friendly strategies to control pests, contributing to more sustainable agricultural practices and ecosystem conservation efforts. Our study isn't just about insects and microbes; it's a journey into the unseen world beneath our feet, where tiny allies pave the way for a greener tomorrow.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0007485324000439.
Acknowledgements
We extend our sincere gratitude to Zhentao Cheng and other colleagues for their assistance in collecting some of the insect samples.
