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Metagenomic profiling of gut microbiota in Fall Armyworm (Spodoptera frugiperda) larvae fed on different host plants

BMC Microbiology volume 24, Article number: 337 (2024) Cite this article

Abstract

Background

The fall armyworm (FAW, Spodoptera frugiperda) is a polyphagous pest known for causing significant crop damage. The gut microbiota plays a pivotal role in influencing the biology, physiology and adaptation of the host. However, understanding of the taxonomic composition and functional characteristics of the gut microbiota in FAW larvae fed on different host plants remains limited.

Methods

This study utilized metagenomic sequencing to explore the structure, function and antibiotic resistance genes (ARGs) of the gut microbiota in FAW larvae transferred from an artificial diet to four distinct host plants: maize, sorghum, tomato and pepper.

Results

The results demonstrated significant variations in gut microbiota structure among FAW larvae fed on different host plants. Firmicutes emerged as the dominant phylum, with Enterococcaceae as the dominant family and Enterococcus as the prominent genus. Notably, Enterococcus casseliflavus was frequently observed in the gut microbiota of FAW larvae across host plants. Metabolism pathways, particularly those related to carbohydrate and amino acid metabolism, played a crucial role in the adaptation of the FAW gut microbiota to different host plants. KEGG orthologs associated with the regulation of the peptide/nickel transport system permease protein in sorghum-fed larvae and the 6-phospho-β-glucosidase gene linked to glycolysis/gluconeogenesis as well as starch and sucrose metabolism in pepper-fed larvae were identified. Moreover, the study identified the top 20 ARGs in the gut microbiota of FAW larvae fed on different host plants, with the maize-fed group exhibiting the highest abundance of vanRC.

Conclusions

Our metagenomic sequencing study reveals significant variations in the gut microbiota composition and function of FAW larvae across diverse host plants. These findings underscore the intricate co-evolutionary relationship between hosts and their gut microbiota, suggesting that host transfer profoundly influences the gut microbiota and, consequently, the adaptability and pest management strategies for FAW.

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Background

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a formidable threat as an invasive and highly polyphagous agricultural pest that is native to tropical and subtropical regions of the Americas [1,2,3]. Its rapid reproduction in local maize crops has led to population outbreaks, posing severe challenges to crop production [4,5,6].

The microbial community residing within the gut of insects encompasses a diverse array of organisms, including viruses, archaea, bacteria, fungi, and protozoa [7,8,9]. As part of the co-evolutionary dynamics between microbes and their hosts, these entities engage in complex interactions that significantly impact host metabolism and physiological functions [10]. Studies have elucidated the profound influence of gut microbiota on various aspects of insect biology, such as longevity, development, fecundity and behavior regulation. They play critical roles in nutrient provisioning, digestion facilitation, as well as immune defense and detoxification mechanisms [11,12,13,14].

The gut microbiota of insects is influenced by the host plants they feed on; this impact is a critical aspect of understanding insect–microbe interactions and their ecological and evolutionary implications. The feeding habits of insects significantly impact the composition, structure, diversity and function of their gut bacterial communities. Zhu et al. observed distinct bacterial diversity and community structures in Chilo suppressalis corresponding to different diets [15]. Similarly, Yang et al. established a significant correlation between the gut microbiota of Cnaphalocrocis medinalis and its respective host plant [16]. Meng et al. highlighted the influence of host diet on the bacterial community structure of Diaphorina citri [17]. Wang et al. demonstrated significant variations in the gut bacterial community of Locusta migratoria manilensis in response to different host plants [18]. Furthermore, Yuan et al. conducted a study on the gut community structure of Grapholita molesta, revealing noteworthy shifts when transferring from an artificial diet to various host fruits [19]. Collectively, these findings highlight the critical importance of understanding the impact of host plants on insect gut microbiota, given its profound ecological and evolutionary implications.

Several studies have demonstrated the significant influence of host plants on the gut microbiota of FAW larvae. Ma et al. observed that FAW larvae fed on hairy vetch displayed reduced gut microbiota diversity compared to those fed on maize, with specific microbial taxa exhibiting a notable correlation with larval performance [20]. Similarly, Wang et al. found that the richness and diversity of gut bacteria in fifth-instar FAW larvae peaked when fed on corn, whereas sixth-instar larvae showed higher measures when fed on sorghum, highland barley, and citrus [21]. De Oliveira and Cônsoli revealed genus-level differences in FAW larval gut microbiota when feeding on different corn and rice strains, with notable phylum-level differences observed when larvae fed on cotton plants [22]. Furthermore, Ugwu et al. identified distinct patterns in the structure and community of gut bacteria in FAW larvae fed on sugarcane, maize, and onion [23]. Lü et al. compared the gut microbial community structure and diversity in FAW larvae feeding on various hosts, including corn with and without seed coating agent, wild oat, oilseed rape, pepper, and an artificial diet [24]. Their results highlighted that the gut microbiota of FAW larvae is not only influenced by host species but also by various host treatments. These findings underscore the significant impact of host plant selection on the gut microbiota of FAW larvae, emphasizing the importance of considering plant species in understanding insect–microbe interactions. They offer valuable insights into the intricate relationship between host plants, gut microbiota, and FAW development, underscoring the necessity for further research in this area.

Previous studies have extensively investigated the gut microbiota of FAW larvae using both culture-dependent methods [25,26,27,28,29,30] and amplicon-oriented DNA sequencing [29, 31,32,33,34,35]. Metagenomic sequencing offers distinct advantages over traditional culture techniques, as it can detect microbial species that are uncultivable. Moreover, compared to 16S rRNA amplification sequencing, metagenomic sequencing allows for the identification of microbial species at various taxonomic levels, facilitating a more precise analysis of microbial community structure and function [36].

Metagenomic sequencing is a key method for studying the impact of pesticides on soil microbes. It allows researchers to understand the effects on microbial diversity and function, The complex effects of pesticide mixtures on soil microbial communities and functions provide a new perspective for understanding the ecological risks associated with pesticides [37]. This technique has also been effective in water treatment, analyzing microbial dynamics, tracking antibiotic resistance genes, and identifying human pathogens, aiding in strategies to combat antibiotic resistance [38]. When discussing the role of metagenomics in plant disease management, Jyoti Taunk and Umesh Goutam (2021) detailed in Chapter 9 of the book "Role of Metagenomics in Plant Disease Management" how metagenomic techniques can be used to identify novel biological control agents. Through high-throughput sequencing and bioinformatics analysis, they successfully identified a variety of microorganisms and functional genes that have significant effects on the prevention and control of plant diseases, providing an important reference for the innovation of biological control strategies [39]. Additionally, metagenomic has provided insights into insect gut microbiomes, Herbivorous insects like the diamondback moth, Plutella xylostella, have evolved mechanisms to counteract plant defenses, and their gut microbiota, dominated by a few key bacterial species, plays a crucial role in aiding digestion, detoxifying plant compounds, and synthesizing essential amino acids, presenting possible targets for innovative pest control strategies [40]. The gut microbiota of the rice pest Cnaphalocrocis medinalis plays a vital role in its nutritional intake and development, with the core community remaining stable across different diets, and the study's findings on the symbiotic relationship between the insect and its microbes offer new perspectives for pest management strategies [41]. As a powerful tool, metagenomics has demonstrated its unique value across various domains. From soil microbial communities to insect gut microbiota, and from plant diseases to antibiotic resistance gene research, metagenomics provides a comprehensive and in-depth perspective. This aids in better understanding and managing the complex ecological and health issues related to microorganisms.

In this study, we employed metagenomic sequencing to explore the gut microbiota communities of FAW larvae reared in the laboratory using two Poaceae crops (maize and sorghum), two Solanaceous plants (pepper and tomato), and an artificial diet. The findings of the current research revealed that the gut microbial structure of FAW larvae can be influenced by the host plants, subsequently impacting the functional genes associated with gut microbiota and antibiotic resistance. These findings suggest that the gut microbiota of FAW may play a pivotal role in host adaptation.

Methods

Insects rearing

FAW individuals were collected from a maize field near the Guizhou Academy of Agricultural Sciences in June 2019 and were fed an artificial diet, as described by Di et al. [42] to establish a multigenerational laboratory population at the Institute of Entomology, Guizhou University, located in Huaxi, Guiyang, Guizhou, China. For our study, we collected eggs from the FAW population that was fed the artificial diet. Newly hatched larvae were individually placed in 6-well culture plates. These larvae were then provided with fresh leaves from four different host plants: pepper, tomato, maize, sorghum, and the artificial diet on a daily basis. The rearing conditions were maintained in an incubator at 25°C ± 1°C and 60% ± 5% relative humidity, under a photoperiod of 16-h light and 8-h dark. Adult FAWs were provided with a 10% (w/v) honey solution diluted in water.

Host plants and artificial diet formula

The host plants used in this study included pepper, tomato, maize, and sorghum (Table 1). These plants were cultivated in pots filled with soil sourced from a vegetable experimental base and were maintained in a greenhouse at 25°C ± 2°C in Guizhou University, Guizhou, China. Tender leaves from maize, pepper, tomato, and sorghum plants were harvested at the specific 3–4-true-leaf stages for all subsequent experiments. These leaves were then provided daily to feed the newly hatched FAW larvae until they reached the fifth instar stage.

Table 1 Host plants used for feeding FAW larvae
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According to Di et al. [42] an improved artificial diet was used, which consisted of 400 mL of distilled water, 40 g soybean powder, 40 g wheat powder, 16 g yeast powder, 8 g casein, 3.2 g ascorbic acid, 8 g agar powder, 0.4 g choline chloride, 0.8 g sorbic acid, 0.08 g inositol, and 0.08 g cholesterol. In this experiment, two factors were considered that constituted the treatment: host plants and artificial diet.

Sample collection

FAW larvae were fed on various host plants until they reached the fifth instar before being collected for experimentation [29]. Upon collection, the larvae were washed with sterile water and surface-sterilized in 75% ethanol for 90 s, followed by three rinses in sterile water for 30 s each. The entire gut was aseptically dissected using sterile forceps in a sterile petri dish (placed on ice) with a diameter of 90 mmwithin an ultraclean bench. Each of the five treatments (pepper, tomato, maize, sorghum and artificial diet) consisted of 60 individual guts, which were then stored in sterile 1.5-mL centrifuge tubes [43, 44], with three replicates for each treatment. In total, 15 whole gut samples were collected. All gut samples were immediately stored in liquid nitrogen after collection and preserved at − 80°C until DNA extraction.

Sample test

The cetyltrimethylammonium bromide (CTAB) method was used for total metagenomic DNA extraction from each sample according to the protocol. DNA concentration was determined using a NanoDrop 2000 spectrophotometer, and the purity and integrity of the samples were verified via agarose gel electrophoresis prior to sequencing. Sequencing of the total DNA was performed by Wekemo Tech Group Co., Ltd. in Shenzhen, Guangdong, China.

Library construction

A DNA library was constructed using the NEBNext® UltraTM DNA Library Prep Kit according to the manufacturer’s instructions (Illumina, San Diego, CA, USA). Genomic DNA samples were fragmented by sonication to a size of 350 bp. The DNA fragments were subjected to end polishing, A-tailing, and ligation with full-length adapters for Illumina sequencing. Subsequently, polymerase chain reaction (PCR) was performed. The PCR products were purified using the AMPure XP system (Beverly, CA, USA). The quality of the libraries was assessed using the Agilent 5400 system (Agilent, Santa Clara, CA, USA), and quantification was performed using QPCR, achieving a concentration of 1.5 nM.

Sequencing

We opted for a paired-end (PE) library with an insert size of 350 bp. Subsequently, all samples were subjected to sequencing using the PE150 (2 × 150) protocol on the Illumina Novaseq 6000 platform (Illumina, San Diego, CA, USA).

Bioinformatics analysis

Data quality control and de-host sequence

Metagenomic sequencing was conducted using the Illumina Novaseq high-throughput sequencing platform to capture raw data encompassing bacteria, fungi, and viruses across 15 samples. Quality control and sequence trimming were performed using the KneadData toolkit (version 0.7.4) to ensure the reliability of the data. Illumina adapters and low-quality sequences were removed from raw reads using Trimmomatic (version 0.39) [45]. Furthermore, to eliminate any potential contamination from the host S. frugiperda genome, KneadData integrated with the Bowtie2 tool (version 2.3.4.1) was used [46]. Subsequently, the efficacy and impact of the quality control measures were assessed using FastQC to generate clean and reliable data.

Taxonomy annotation

The Kraken2 program was utilized in conjunction with a self-constructed microbial database [47]. This database comprised sequences from bacteria, fungi, archaea, and viruses, which were filtered from the NT nucleic acid database and the RefSeq whole genome database of NCBI. These resources were employed to classify the species present in all samples. Furthermore, the Bayesian Re-estimation of Abundance after Classification with Kraken (BRACKen) was used to estimate the actual abundance of species in the samples [48].

Identification of taxa with differential abundance

The phylogenetic diversity among 15 samples was examined based on alpha diversity, calculated using the vegan (version 2.6.0). This provided insights into the richness and evenness of each sample [49]. For the analysis of beta diversity, principal coordinate analysis (PCoA) was employed based on Bray–Curtis metrics to compare dissimilarities among different groups [50]. The R package Venn diagram (ggplot2 version 3.3.5.9000) was utilized to visualize common and unique taxa at the species level among the five groups. To detect significantly differentially enriched taxa among these groups, linear discriminant analysis (LDA) effect size (LEfSe) was used, LEfSe is a statistical method that combines the Kruskal–Wallis rank sum test with LDA to identify and quantify the differentially abundant microbial taxa and functional genes, offering a way to reveal significant and biologically relevant distinctions within microbial communities, and set thresholds of LDA > 2–4 to differentiate species of high abundance [51].

Common function database annotation of metagenomic DNA sequences

The metagenomic DNA sequences were aligned against the UniProt Reference Clusters 90 (UniRef90) database using the DIAMOND algorithm [52]. Subsequently, a tiered search strategy was implemented in the HMP Unified Metabolic Analysis Network (HUMAnN2) [53]. This strategy was used to quantify the abundance of Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KOs) against the KEGG database [54]. Using LEfSe, significantly differentially abundant KOs were identified among the groups. This process also facilitated the identification of potential functional species associated with different KOs.

Antibiotic resistance gene (ARG) analysis

After quality control, host sequences in each sample were removed using the ARG database. The DIAMOND software was then employed for blasting and annotating clean reads [52]. The Comprehensive Antibiotic Resistance Database (CARDS) algorithm was utilized with specific parameters set to − e 0.001 (e-value < le − 3) and − i 80 (percent identity > 80%) for each sample.

Availability of data and materials

All metagenome sequence reads can be accessed in the Sequence Read Archive (SRA) of NCBI under the BioProject with the accession number PRJNA1123873, available at https://www.ncbi.nlm.nih.gov/sra/PRJNA1123873.

Results

Summary of metagenomic sequencing results

Metagenomic sequencing was employed to analyze 15 samples, with the sequencing performed using the Illumina Novaseq platform. A total of 327,709,326 raw reads were collected, which, after filtering and quality control, were processed to generate 100,370,155 clean reads. These clean reads were distributed across samples, ranging from 3,018,373 to 14,229,565 per sample, with an average of 6,691,344 clean reads per sample (Table 2). Cluster analysis resulted in 1,904 operational taxonomic units (OTUs) at a 97% sequence similarity, encompassing 14 phyla, 40 classes, 92 orders, 182 families, 604 genera, and 1,835 species. The sample rarefaction curve (Fig. 1a) and Shannon index rarefaction curve (Fig. 1b) confirmed that the sequencing depth was sufficient, indicating that increasing the sample volume would not generate additional OTUs.

Table 2 Summary of metagenome sequence statistics for the Illumina Novaseq runs of all samples
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Fig. 1
figure 1

The sample rarefaction curve (a) and Shannon index rarefaction curve (b); Cluster heatmap of the 20 abundant genera in the gut microbial community of FAW larvae fed on an artificial diet (AD), pepper (CA), tomato (SL), sorghum (SM) and maize (ZM) (c)

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Composition of gut microbiota of FAW fed on different host plants

The taxonomic units detected in this study included archaea (119), bacteria (44,039,175), phages (3,997), plants, and fungi (122,422). The proportions of each category were 0.00%, 99.73%, 0.00%, and 0.27%, respectively (Table 3).

Table 3 The relative abundance of 15 samples at the Kingdom level
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At the phylum level, the three dominant phyla were Firmicutes, Proteobacteria, and Actinobacteria, all of which belong to bacteria. These were followed by Microsporidia and Ascomycota, which belong to fungi. Firmicutes was the dominant phylum among the gut microbiota of the majority of the groups, with the notable exception of the sorghum-fed group, especially in the tomato-fed group, accounting for 98.73% ± 0.37%. Proteobacteria was the dominant phylum in the sorghum-fed group, accounting for 59.64% ± 11.16% (Fig. 2a, Table 4).

Fig. 2
figure 2

The ten most abundant gut microbiota of FAW larvae fed on an artificial diet (AD), pepper (CA), tomato (SL), sorghum (SM) and maize (ZM) at the phylum level (c); the family level (d); the genus level (e); and the species level (f)

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Table 4 The relative abundance of five dominant phyla in FAW fed on the four host plants and an artificial diet
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At the family level, the five most prevalent families were Enterococcaceae, Lactobacillaceae, Enterobacteriaceae, Brucellaceae, and Rhizobiaceae (Fig. 2b). Enterococcaceae was present in all samples, with the highest relative abundance observed in samples collected from the tomato-fed group at 98.64% ± 0.36%. Conversely, the lowest relative abundance of Enterococcaceae was found in samples collected from the sorghum-fed group at 37.51% ± 11.96%. Lactobacillaceae only exhibited high abundance (26.42%% ± 6.76%) when FAW larvae were fed on the artificial diet. Additionally, Enterobacteriaceae (12.61% ± 2.54%), Brucellaceae (21.04% ± 2.13%), and Rhizobiaceae (16.73% ± 8.22%) were more abundant when FAW larvae were feeding on sorghum compared to the other groups (Table 5).

Table 5 The relative abundance of five most prevalent families in FAW fed on the four host plants and an artificial diet
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At the genus level, Enterococcus, Lactobacillus, and Ochrobactrum were the most dominant in the gut microbiota across all samples of multiple groups, including the tomato, pepper, maize, and artificial diet-fed groups, followed closely by Klebsiella and Agrobacterium. In the artificial diet-fed group, Lactobacillus was found to be the most abundant genus (Fig. 2c).

Among the five groups observed, the most abundant species were Enterococcus casseliflavus, Enterococcus mundtii, Enterococcus sp. CR-Ec1, Enterococcus gallinarum, and Enterococcus sp. FDAARGOS 375 (Fig. 2d).

To elucidate the changes in the gut microbiota of FAW larvae when fed on different host plants, we selected the top 20 genera based on relative abundances and created a relative abundance cluster heatmap (Fig. 1c). Among the samples, Enterococcus, belonging to the phylum Firmicutes, emerged as the most abundant genus. Ochrobactrum, from the phylum Proteobacteria, was the primary component of the bacterial communities in sorghum-fed FAW. Additionally, the genera Klebsiella (5.64), Agrobacterium (5.53), and Devosia (4.94), all from the phylum Proteobacteria, were also abundant in the gut microbiota of sorghum-fed FAW. Lactobacillus, from the phylum Firmicutes, was the major component of the bacterial communities in FAW larvae fed an artificial diet. Interestingly, Agrobacterium and Rhizobium were exclusively present in the gut microbiota of FAW feeding on Poaceae crops (maize and sorghum). Moreover, within the gut microbiota of FAW larvae feeding on sorghum, the abundance of Agrobacterium and Rhizobium was higher compared to those feeding on maize. However, these two genera were not found in the gut microbiota of FAW fed on an artificial diet or pepper.

Comparison of gut microbiota diversity among FAW fed on different host plants

Alpha diversity

The alpha diversity index was used to identify differences in the gut microbiota of FAW larvae when fed on different host plants. The richness of the gut microbiota was evaluated using the ACE and Chao 1 indices. The FAW larvae feeding on sorghum had the highest ACE and Chao 1 values, indicating a rich gut microbiota. In contrast, the maize-fed FAW larvae had the lowest values for both ACE and Chao 1, suggesting a less rich gut microbiota (Fig. 3a, b). The diversity of the gut microbiota was evaluated using the Simpson and Shannon indices. The sorghum-fed group had the highest values in both Simpson and Shannon, indicating high diversity. Conversely, the pepper-fed group had the lowest Simpson and Shannon diversity, indicating lower diversity (Fig. 3c, 3d).

Fig. 3
figure 3

The alpha diversity index was used to identify differences in the gut microbiota of FAW larvae fed on different host plants. ACE indices (h); Chao 1 indices (i); Simpson indices (j); and Shannon indices (k). The abbreviations in the figure represent different diets, AD, artificial diet-fed; CA, pepper-fed; SL, tomato-fed; SM, sorghum-fed; ZM, maize-fed. Different lowercase letters indicate significant differences (one-way ANOVA, Tukey post-hoc test, P < 0.05) in the mean values

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Differentially enriched taxa associated with FAW fed on host plants

The beta diversity, evaluated by the Bray–Curtis distance, clearly delineated the microbiota composition between samples (Fig. 4a). The gut microbiota of FAW larvae fed on sorghum exhibited a significant difference in composition compared to those fed on other host plants. Additionally, the gut microbiota of FAW larvae fed on an artificial diet and sorghum was distinct from that of FAW larvae fed on maize and pepper. The PCoA plot revealed distinct clustering of samples from pepper-fed and maize-fed larvae, although some overlap was observed.

Fig. 4
figure 4

Differential taxa were enriched in the AD, CA, SL, SM and ZM groups. The PCoA plot showed the beta diversity assessed by the Bray–Curtis distance (a); Venn diagram represented common and unique taxa at the species level (b); Taxonomic cladogram from LEfSe showed different taxa enriched in the AD, CA, SL, and SM group (LDA > 4) (c); LEfSe identified significantly differentially abundant taxa (LDA > 4) (d). The abbreviations in the figure represent different diets, AD, artificial diet-fed; CA, pepper-fed; SL, tomato-fed; SM, sorghum-fed; ZM, maize-fed. PCoA, Principal coordinate analysis; LEfSe, linear discriminant analysis effect size; LDA, linear discriminant analysis

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A Venn diagram was used to represent the core microbiome, consisting of shared taxa among samples within certain groups. At the species level, 84 common species were shared among the five groups. There were 80, 37, 36, 1417, and 2 unique species in the artificial diet-fed, pepper-fed, tomato-fed, sorghum-fed, and maize-fed groups, respectively (Fig. 4b).

To identify biomarkers with significant variations across different samples, we utilized the LEfSe method to analyze various taxonomic orders, including kingdom, phylum, class, order, family, genus, and species. Our approach involved setting a minimum LDA value of four to screen these categories, thereby establishing a benchmark for statistical significance. Nineteen taxa were clustered within Proteobacteria, with the dominant Alpha-proteobacteria being the most highly discriminating taxon in the sorghum-fed group. The gut microbiota of the tomato-fed group showed significant differences across seven taxa, primarily featuring Firmicutes, Enterococcaceae, Enterococcus, and Lactobacillus. Only one microbe, belonging to the species Enterococcus casseliflavus in the phylum Firmicutes, was found in the larvae that fed on pepper. Eight different taxa showed a significant difference in the gut microbiota of the larvae fed on the artificial diet, with a predominant in Enterococcus mundtii, Lactobacillaceae, and Lactobacillus. The absence of microbiome features in the image regarding the larvae feeding on maize indicates no distinguishable species in the gut microbiome between the larvae fed on maize and the other host plants (Fig. 4c, d).

Functional analysis in gut microbiota of FAW larvae fed on different host plants

To investigate the functionality of the gut microbiota across five groups, we annotated the gene catalogs with metagenomic sequencing data using the KEGG database. The KEGG PATHWAY database, which includes areas such as metabolism, genetic information processing, cellular processes, human diseases, environmental information processing, and organismal systems, served as the foundation of the KEGG database. Among these pathways, those related to metabolism were the most extensively annotated, accounting for 70.36% of the total (Fig. 5a, 6a, Supplementary Table S1).

Fig. 5
figure 5

Functional annotation of the gut microbiome across AD, CA, SL, SM and ZM groups at the KEGG first level pathway analysis (a); KEGG second level pathway analysis (b); KEGG third-level pathway analysis (c); and KOs level analysis (d)

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Fig. 6
figure 6

Linear discriminant analysis (LDA) effect size analysis (LEfSe) was used to comparison of KEGG functions differences in gut microbiota of FAW fed on different host plants. KEGG first level analysis (LDA > 2, P < 0.05) (a); KEGG second level analysis (LDA > 2, P < 0.05) (b); KEGG third level pathway analysis (LDA > 3, P < 0.05) (c) and different KOs enriched in the AD, CA, SL and SM group identified by LEfSe (LDA > 3, P < 0.05) (d). The abbreviations in the figure represent different diets, AD, artificial diet-fed; CA, pepper-fed; SL, tomato-fed; SM, sorghum-fed; ZM, maize-fed. KEGG, Kyoto Encyclopedia of Genes and Genomes; KOs, KEGG Orthologous groups. LDA, linear discriminant analysis; LEfSe, linear discriminant analysis effect size

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Our analysis identified 57 pathway categories at the second level. The most prevalent was carbohydrate metabolism, constituting 13.56% of the total relative abundance, with the tomato-fed group exhibiting the highest relative abundance. Additionally, we found that amino acid metabolism made up the second largest proportion, with the sorghum-fed group having the highest relative abundance. The metabolism of cofactors and vitamins constituted the third largest proportion in all pathways (Fig. 5b, 6b, Supplementary Table S2). The top 10 functional annotations at the third-level pathways primarily involved metabolism pathways (terpenoid and steroid biosynthesis; D-alanine metabolism; starch and sucrose metabolism; valine, leucine, and isoleucine biosynthesis), genetic information processing (ribosome), environmental information processing (phosphotransferase system [PTS]), human disease (vancomycin resistance), and cellular processes (bacterial chemotaxis; peptidoglycan biosynthesis; and flagellar assembly) (Fig. 5c, Supplementary Table S3). Using the KEGG Orthology database and performing BLAST analysis, we obtained the top 10 most abundant KOs (Fig. 5d).

Further examination of annotated genes using the KEGG databases revealed significant differences between the groups across various pathways and functions. These differences were particularly evident in the third-level components of KEGG among the five groups. Specifically, the biosynthesis of terpenoid backbone and zeatin was associated with the metabolism of terpenoids and polyketides in the maize-fed group. Valine, leucine, and isoleucine biosynthesis, histidine metabolism associated with amino acid metabolism, and C5-branched dibasic acid metabolism pertain to carbohydrate metabolism. Furthermore, cell cycle-caulobacter, 2-oxocarboxylic acid metabolism, and limonene and pinene degradation were intricately associated with the sorghum-fed group. The PTS and fructose and mannose metabolism were notably linked to the tomato-fed group. Moreover, flagellar assembly, starch and sucrose metabolism, vancomycin resistance, other glycan degradation, and peptidoglycan biosynthesis were closely aligned with the pepper-fed group, whereas D-alanine metabolism, lipoic acid metabolism, streptomycin biosynthesis, thiamine metabolism, and pentose phosphate pathway exhibited strong correlations with the artificial diet-fed group (Fig. 6c, Supplementary Table S4).

Figure 6d illustrates several KOs with significant differences in relative abundance among the sorghum-fed, pepper-fed, and artificial diet-fed groups. Utilizing LEfSe (LDA > 3), we identified 13 KOs enriched in the pepper-fed group, 3 KOs enriched in the sorghum-fed group, and only 1 KO enriched in the artificial diet-fed group. Among the most abundant KOs in the sorghum-fed group were K02033, K02034, and K02032, which are involved in regulating peptide/nickel transport system permease protein and peptide/nickel transport system ATP-binding protein. These KOs are related to quorum sensing. Additionally, K01223, which regulates 6-phospho-β-glucosidase associated with glycolysis/gluconeogenesis and starch and sucrose metabolism, was enriched in the pepper-fed group. Notably, only one KO (K03704: cold shock protein) was enriched in the artificial diet-fed group but was not associated with any specific pathway. To further explore the function of different KOs, we initially annotated the KO gene, clarified the pathway, and then selected representative pathway diagrams for presentation, as depicted in Fig. 7.

Fig. 7
figure 7

The biomarker genes across the AD, CA, SL and SM group in the Glycolysis / Gluconeogenesis pathway (map00010). The abbreviations in the figure represent different diets, AD, artificial diet-fed; CA, pepper-fed; SL, tomato-fed; SM, sorghum-fed; ZM, maize-fed. The gray rectangular box indicates the detection of the gene in the sample, while the colored rectangular box represents characteristic genes corresponding to specific color-coded groups

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Following the selection of feature functions across different groups, we identified functional species using the results from HUMAnN2 (Fig. 8). E. mundtii was found to contribute to the artificial diet-fed group (Fig. 8a). E. casseliflavus was linked to the metabolic dynamics of the pepper-fed group (Fig. 8b) and was similarly associated with metabolism and environmental information processing in the tomato-fed group (Fig. 8c). Agrobacterium tumefaciens was found to be associated with quorum sensing pathways, which were correlated with cellular processes in the sorghum-fed group (Fig. 8d). However, it is important to note that while this association is observed, further experimental evidence is needed to establish a causal link between A. tumefaciens and quorum sensing in this context. Similarly, Enterococcus casseliflavus was identified to have an association with the regulation of the energy-coupling factor transport system permease protein, which is linked to ABC transporters that were more abundant in the maize-fed group (Fig. 8e). Again, this finding points to an association rather than a direct causal relationship, emphasizing the need for additional experimental verification.

Fig. 8
figure 8

Bacterial species contributed to key functions. The ten bacterial species contributed to cold shock protein (K03704) enrichment in the artificial diet-fed (AD) group (a); The ten bacterial species contributed to 6-phospho-β-glucosidase (K01223) enrichment in the pepper-fed (CA) group (b); The ten bacterial species contributed to fructose PTS system EIIBC or EIIC component (K02770) enrichment in the tomato-fed (SL) group (c); The ten bacterial species contributed to peptide/nickel transport system permease protein (K02033) enrichment in the sorghum-fed (SM) group (d). The ten bacterial species contributed to energy-coupling factor transport system permease protein (K16785) enrichment in the maize-fed (ZM) group (e)

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To identify the top 20 ARGs based on relative abundance among 15 samples, we compared our data with the Comprehensive Antibiotic Resistance Database. We calculated the content and percentage of antibiotic resistance ontology (ARO) in each sample and selected the top 20 ARO results with the highest abundance for display (Supplementary Figure S1). ARO:3002922 (vanRC) and ARO:3003948 (efrA) were found to be predominant across the 15 samples, indicating a high level of resistance to antibiotics. Based on the LEfSe analysis, the dominant ARGs of FAW feeding on maize were vanRC, vanSC, and vanC, which confer resistance to glycopeptides. For FAW feeding on pepper, the dominant ARG was TetM, which encodes a ribosomal protection protein that confers resistance to tetracycline. The dominant gut ARG of FAW larvae feeding on sorghum was CAT, a chloramphenicol resistance gene. When the larvae were fed an artificial diet, the dominant ARG was dfrE, a chromosome-encoded dihydrofolate reductase that confers resistance to diaminopyrimidine antibiotics (Fig. 6b). Supplementary Table S5 lists each resistance gene type and its corresponding antibiotics. The study found that the number of glycopeptide antibiotics was the highest among all samples.

Discussion

In this study, we examined the structure, function, and ARGs of the gut microbiota in FAW. The larvae were fed on four host plants: two Poaceae crops (maize and sorghum), two Solanaceae plants (pepper and tomato), and an artificial diet under laboratory conditions. Our findings suggest that the diversity and abundance of the FAW larval gut microbial community, its function, and ARGs are influenced by the host plants. This study is, to our knowledge, the first to use metagenomic analysis, as opposed to the commonly used 16S rRNA amplicon sequencing, to study the gut microbiota in FAW influenced by different host plants.

Previous studies have indicated that the structure of insect gut bacteria can be affected by the host diet and species [43, 44, 55]. Our study supports these findings, showing that the diversity of gut microbiota changes to varying degrees when transitioning from an artificial diet to different host plants. The gut microbiota richness of FAW increased after transitioning from an artificial diet to sorghum. However, when transferred to maize, tomatoes, and especially pepper, the gut microbial diversity was notably lower. This suggests that Poaceae and Solanaceae crops provide different nutritional substances for the growth and development of FAW larvae.

Our previous experimental findings suggest that among the Solanaceae crops consumed, the consumption of pepper by the FAW leads to the shortest adult stage duration, the longest preadult stage, and the smallest pupal weight. In contrast, when compared to the consumption of the Poaceae crop sorghum, there are significant differences in both the adult stage, the preadult stage, and pupal weight [56]. These results align with a previous finding that the microbial diversity of the FAW larvae fed on pepper was the lowest [24].

Significant variations in gut microbiota diversity at the phylum level have been observed. Firmicutes is the dominant phylum in the gut microbiota of FAW larvae feeding on maize, tomato, pepper, and an artificial diet. This aligns with previous FAW studies [21, 22, 24, 34, 57,58,59,60], indicating that Firmicutes could be crucial for the development and fitness of FAW. However, in FAW larvae feeding on sorghum, the dominant phylum is Proteobacteria, followed by Firmicutes. This is in contrast to a previous study suggesting that differences in sorghum varieties could lead to this discrepancy. Furthermore, discrepancies may arise from various growth conditions of sorghum, including temperature, humidity, and light cycles, as well as differing physiological states. Similarly, variations in the fall armyworm's provenance, genetic lineage, and rearing environments are also likely to contribute to these inconsistencies [21]. Studies have shown that the bacterial diversity and abundance in the guts of FAW larvae are influenced by six different corn varieties [21]. Different host plants have varying nutritional substances, digestion facilitation and secondary metabolites, which may affect the growth and development of the fall armyworm [60]. The significant differences in the gut microbiota of fall armyworm larvae feeding on different host plants may be due to the different nutritional components and inhibitory secondary metabolites of these four host plants. Exploring the complex dynamics between crop diversity and insect gut microbiota is crucial for the development of advanced pest management strategies. Targeting the gut microbiota of sorghum-fed pests with specialized biopesticides presents a refined approach to enhance pest control efficacy [61]. Develop effective pest control strategies by understanding the preferences and selection behaviors of pests towards their host plants. Strategically designed crop rotation and intercropping systems, when precisely implemented, can effectively mitigate pest issues by disrupting their life cycles and foraging behaviors, thereby diminishing pest prevalence and damage [62]. Firmicutes, a dominant phylum in the gut microbiota, are known for their ability to encode enzymes that are integral to energy metabolism and potentially the biosynthesizing of vitamin B. They play pivotal roles in nutritional supplementation, energy absorption, and host immunity [63,64,65]. On the other hand, Proteobacteria, another significant phylum, are associated with a broad spectrum of metabolic processes. These include the decomposition and fermentation of complex sugars and the production of vitamins. They also play an essential role in the degradation of various aromatic compounds, thereby enhancing the absorption of nutrients by the host [64, 66]. Both these phyla, Firmicutes and Proteobacteria, are crucial in regulating carbohydrate and amino acid metabolism. They also facilitate host membrane transport pathways [30, 67, 68]. The stable colonization of gut bacteria, particularly Proteobacteria, is known to play a functionally crucial role in the adaptation of insects to specific host plants [43]. However, the role of Proteobacteria in the metabolic pathway of FAW larvae and their adaptation to hosts requires further research.

Enterococcaceae, a unique bacterial family, exhibits a significant presence in the gut microbiota of FAW larvae that feed on tomato, pepper, and maize. In contrast, its presence is relatively low in the gut microbiota of FAW larvae that feed on an artificial diet and sorghum, particularly in the sorghum-fed group. Enterococcaceae is known to play a crucial role in degrading alkaloids and latex, thereby stabilizing resistant plants [44]. This bacterial family is also found in other insects such as Hyphantria cunea (Lepidoptera: Erebidae) [69], Galleria mellonella (Lepidoptera: Pyralidae) [70], and Paederus fuscipes (Coleoptera: Staphylinidae) [71].

On the other hand, Lactobacillaceae was detected exclusively in the gut microbiota samples of the artificial diet-fed group but not in the FAW larvae fed on four different host plants: maize, sorghum, pepper, and tomato. Lactobacillaceae has also been detected in other insects, such as Grapholita molesta (Lepidoptera: Tortricidae) [19], and Drosophila melanogaster [72, 73]. A previous study found that Lactobacillus plantarum promotes the growth and development of germ-free fruit flies, an effect that is related to the Tor and insulin signaling pathways [74]. Schretter et al. reported that monocolonization of germ-free Drosophila with Lactobacillus brevis restores their overactivated behavior to normal levels, similar to those of conventional flies [75]. The xylose isomerase expressed by L. brevis plays a key role in this process. Further research is required to determine if L. plantarum can promote growth and oviposition in FAW and if L. brevis can influence the locomotor behavior of FAW.

Enterobacteriaceae have been consistently observed in the gut microbiota of FAW larvae as well as in other insect species. Huang et al. demonstrated its role in accelerating reproduction in Harmonia axyridis, particularly by reducing the preoviposition duration and incubation period [64]. These findings highlight the importance of microbial communities for insect reproductive biology. Research indicates that Enterobacteriaceae, a symbiotic microorganism prevalent in both human and insect guts, plays pivotal roles in vitamin biosynthesis, pheromone production, and degradation of plant compounds [76]. Additionally, it contributes to enzyme metabolism through activities such as superoxide dismutase or catalase enzyme activity [40]. Moreover, Enterobacteriaceae have been linked to sugar metabolism in hosts and may aid in various physiological processes, including digestion, protection, courtship, and reproduction [77].

Enterococcus emerges as the most abundant genus observed in the gut microbiota of FAW larvae, serving as a stable insect symbiotic bacterium throughout metamorphosis. Its protective effects against pathogens [78], capability to neutralize toxic molecules in plants [79], and facilitation of host adaptation [80] contribute to increasing tolerance to harmful foods [81]. Additionally, Enterococcus plays roles in vitamin and pheromone synthesis, degradation of plant compounds, nitrogen fixation and insecticide [61, 82, 83].

Significant differences in gut microbial community structures of fifth-instar FAW larvae were observed based on the host plant they fed on, as revealed via PCoA. Notably, there was some overlap in the PCoA plots between pepper and maize, suggesting that these plants may offer unique nutrients crucial for FAW larvae growth and development. Ugwu et al. reported that the observed variations in the gut of FAW larvae could be attributed to the diverse chemical components of the different diet groups [23]. These findings underscore the importance of understanding the relationship between host plant nutrition and insect microbiomes for effective FAW infestation management.

In the investigation of the gut microbiota of FAW larvae feeding on various host plants, researchers commonly utilize 16S amplicon sequencing technology for analysis. Additionally, functional annotation is predicted and analyzed using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) based on OTU representative sequences. Previous studies have indicated variations in the gut bacteria of FAW larvae fed on different host plants, with the most dominant putative function prediction categories in metabolic pathways [21, 24, 57, 58]. In this study, metagenomic sequencing was employed to accurately decipher the microbial community functions and obtain functional abundance and differences using the KEGG database. Results revealed that the gut microbiota of FAW larvae was predominantly associated with metabolism, followed by genetic information processing and cellular processes. At the KEGG second-level pathway, the FAW gut microbiota was primarily involved in carbohydrate metabolism, amino acid metabolism, and metabolism of cofactors and vitamins. This suggests a significant role of the microbiota in food digestion, degradation, and nutrient absorption. Additionally, the microbiota plays a crucial role in vitamin metabolism, providing essential vitamins for host growth and development.

At the KEGG third-level pathway, various cellular processes were observed, including the biosynthesis of terpenoids and steroids; ribosomal functions; PTS; metabolism of D-alanine; starch and sucrose metabolism; biosynthesis of valine, leucine, and isoleucine; vancomycin resistance; bacterial chemotaxis; biosynthesis of peptidoglycan; flagellar assembly; and the biosynthesis of amino acids. Tang et al. explored the variations in gut microbiota composition and functional annotation between S. litura and S. frugiperda, predicting that S. frugiperda had a significantly higher relative abundance of gut microbiota involved in detoxification compared to S. litura [84]. Additionally, pathways related to detoxification were found to be more abundant in S. frugiperda.

Furthermore, we identified several ARGs, indicating that the gut microbiota of FAW may serve as a reservoir for these genes. Our analysis revealed the top 20 ARGs, with vanRC being the most abundant in the gut microbiota of maize-fed FAW. These ARGs confer resistance to antibiotics such as glycopeptide, macrolide, fluoroquinolone, rifamycin, and rifampicin antibiotics, underscoring the potential for antibiotic resistance development within the FAW gut microbiota. This has significant implications for pest management strategies. Interestingly, Tang et al. detected different types of FAW resistance genes, with the relative abundances of AbaF, DfrA42, and MexD being most abundant, contrasting with our study results [84]. These findings highlight the prevalence and diversity of ARGs in the gut microbiota of FAW larvae and underscore the importance of host plant selection in shaping antibiotic resistance profiles. Further research in this area is crucial for a comprehensive understanding of the factors influencing antibiotic resistance in agricultural ecosystems.

In this study, we only observed one generation and one instar (fifth instar) after the host switch. Long-term observation of successive generations of FAW larvae fed on host plants would provide a more comprehensive understanding of the dynamic changes in the gut microbiome during the process of host transfer. Additionally, employing multiomics technologies and conducting bacterial removal and re-inoculation experiments are necessary to elucidate the specific functions of distinct strains of gut microbiota in FAW adaptation to different host plants. This would ultimately enhance our understanding of the relationship between FAW fitness and its host plants.

Conclusion

In conclusion, our study utilized metagenomic sequencing to explore the gut microbiota of FAW larvae fed on various host plants and an artificial diet. We observed significant variations in gut microbiota composition among different host plants, with Firmicutes being the dominant phylum, Enterococcaceae as the dominant family, and Enterococcus as the predominant genus. Notably, Enterococcus casseliflavus emerged as the most abundant species. Functional analysis indicated a predominance of metabolic pathways, particularly those related to carbohydrate and amino acid metabolism. We identified vanRC as the most abundant ARG, conferring resistance to glycopeptide antibiotics. These findings enhance our understanding of gut microbiota dynamics and functional profiles in FAW larvae across different host plants, offering valuable insights into pest management strategies.

Availability of data and materials

All metagenome sequence reads can be accessed in the Sequence Read Archive (SRA) of NCBI under the BioProject with the accession number PRJNA1123873, available at https://www.ncbi.nlm.nih.gov/sra/PRJNA1123873.

Abbreviations

FAW:

Fall Armyworm

ARG:

Antibiotic Resistance Gene

PCR:

Polymerase Chain Reaction

qPCR:

Quantitative Polymerase Chain Reaction

PE:

Paired-End

NCBI:

National Center of Biotechnology Information

PCoA:

Principal Coordinate Analysis

BRACKen:

Bayesian Re-estimation of Abundance after Classification with Kraken

LDA:

Linear Discriminant Analysis

LEfSe:

Linear Discriminant Analysis (LDA) Effect Size

UniRef90:

UniProt Reference Clusters 90

HUMAnN2:

HMP Unified Metabolic Analysis Network

KEGG:

Kyoto Encyclopedia of Genes and Genomes

KOs:

KEGG Orthologs

CARDS:

Comprehensive Antibiotic Resistance Database

OTUs:

Operational Taxonomic Units

ARO:

Antibiotic Resistance Ontology

PICRUSt:

Phylogenetic Investigation of Communities by Reconstruction of Unobserved States

PTS:

Phosphotransferase System

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Acknowledgements

This study was supported by the Guizhou Provincial Science and Technology Projects ([2020]1Y105), the Guizhou Province Science and Technology Innovation Talent Team Project ([2021]004), and the Training Program of Guizhou University ([2019]32 and [2019]45).

Funding

This study was supported by the Supported by Guizhou Provincial Science and Technology Projects ([2020]1Y105), the Guizhou Province Science and Technology Innovation Talent Team Project ([2021]004), and the Training Program of Guizhou University ([2019]32 and [2019]45).

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Authors and Affiliations

  1. Institute of Entomology, Guizhou University, Guiyang, China

    Li-hong Wu, Chao-xing Hu & Tong-xian Liu

  2. Guizhou Provincial Key Laboratory for Agricultural Pest Management of the Mountainous Region, Scientific Observing and Experimental Station of Crop Pests in Guiyang, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Guiyang, China

    Li-hong Wu, Chao-xing Hu & Tong-xian Liu

  3. Institute of Plant Health and Medicine, Guizhou University, Guiyang, 550025, China

    Li-hong Wu, Chao-xing Hu & Tong-xian Liu

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  1. Li-hong Wu
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LHW performed the experiments, analyzed the data, prepared figures and tables, authored drafts of the paper, and approved the final draft; CXH and TXL conceived the ideas, designed the experiments and selected the appropriate methodologies. All authors contributed to the manuscript and gave their final approval for the publication of the current manuscript.

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Correspondence to Chao-xing Hu or Tong-xian Liu.

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Wu, Lh., Hu, Cx. & Liu, Tx. Metagenomic profiling of gut microbiota in Fall Armyworm (Spodoptera frugiperda) larvae fed on different host plants. BMC Microbiol 24, 337 (2024). https://doi.org/10.1186/s12866-024-03481-4

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BMC Microbiology

ISSN: 1471-2180

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