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Dynamics and diversity of symbiotic bacteria in Apolygus lucorum at different developmental stages



Apolygus lucorum is a worldwide omnivorous pest damaging a range of crops and causing great economic losses. Symbiotic bacteria living in insects play a key role in the nutrition, physiology, and behavior of hosts. Here, we present an experiment using Illumina HiSeq sequencing targeting the V3–V4 regions of bacteria’s 16S rRNA throughout the entire life cycle of A. lucorum.


The first and second instar nymphs have the largest alpha diversity compared with other life stages of the insect. Bacterial phyla Proteobacteria (72.29%), Firmicutes (15.24%), Actinobacteria (7.76%) exhibit the largest relative abundance in all developmental stages. Erwinia (23.97%) and Lactococcus (10.62%) are the two genera with the highest relative abundance. The relative abundance of Erwinia in the nymph stage is significantly greater than the adult stage, and the relative abundance of Lactococcus in 6-day-old and 9-day-old adult females is higher compared with adult males.


These results reveal that microbial community composition and relative abundance shift dynamically at different life stages, implying that different bacterial phyla and genera may have specific roles in specific life stages such as metabolism, nutrition absorption, detoxification, and reproduction. This study reveals for the first time the community composition and ecological dynamics of symbiotic bacteria throughout the life stages of A. lucorum, and thus may provide insight to new strategies for pest control.


Insects are the most diverse and abundant animals on Earth in terms of species number and physical biomass (Basset et al. 2012). Almost all insects have microbial communities in their bodies. There are a large number of microbial symbionts in insects, including those obtained from the surrounding environment and food (Coolen et al. 2022; Luo et al. 2021). Bacteria that can establish a confirmed symbiotic relationship with the host are regarded as symbiotic bacteria (Kucuk 2020). Insects and symbiotic bacteria have a reciprocal relationship, and microbial symbioses play an important role in the health, survival, and behavior of the host (Dillon and Dillon 2004; Kikuchi et al. 2007; Moran et al. 2008; Santos-Garcia et al. 2017). Endosymbioses are not only related to the coevolution of plants and herbivores, but also play a profound role in the ecology and evolution of insects (Duron et al. 2008; Kikuchi et al. 2012; Pietri and Dangsheng 2018). Specifically, bacterial symbionts can provide nutrients for the host, facilitate host reproduction, protect the host from natural enemies, defend against pathogens, and promote host detoxification and metabolism to improve drug resistance (Broderick et al. 2006; Ceja-Navarro et al. 2015; Cheng et al. 2017; Genta et al. 2006; Gerardo and Parker 2014; Heyworth and Ferrari 2015; Rolff and Siva-Jothy 2003; Scarborough et al. 2005; Sharon et al. 2010).

Hemipteran insects have a needle-like stylet that can be used for sucking plant sap. Some of them are notorious agricultural pests responsible for serious economic losses, because they may not only suck the plant sap and kill crops directly, but also transmit plant viruses (Liu et al. 2018; Qin et al. 2018; Wang et al. 2015). Apolygus lucorum belongs to Miridae (Hemiptera) and is an important worldwide agricultural pest (Tan et al. 2018). A. lucorum has a wide range of plant hosts and can harm economic crops such as cotton, vegetables, and fruit trees. Due to the high density of wild population and their rapid growth rate, A. lucorum outbreaks erupt and spread geographically extremely easily (Lu et al. 2007). Before the 1990s, A. lucorum was historically a minor cotton pest in China (Zhen et al. 2016). However, since the large-scale adoption of transgenic Bacillus thuringiensis (Bt) cotton beginning in 1997, A. lucorum has gradually become a destructive and pervasive cotton pest of serious economic importance with higher frequent outbreaks (Lu et al. 2010). At present, chemical insecticides such as organophosphorus pesticides and pyrethroids are widely used in China to control A. lucorum (Zhen et al. 2016). Furthermore, insecticide resistance could easily arise leaving an urgent and difficult to resolve A. lucorum outbreak. Moreover, the omnivorous nature of A. lucorum increases the difficulty of pest control (Wu et al. 2010). Studies have shown that endosymbiotic bacteria facilitate insect hosts’ ability to easily form new feeding habits, expand food sources, and thus enhance the adaptability of insects to the environment (Douglas 2009; Lü et al. 2001). Similarly, there are a great deal of increasing evidence of a link between symbiotic bacteria in insects and evolution of insecticide resistance (Broderick et al. 2006; Engel and Moran 2013; Kikuchi et al. 2012; Xia et al. 2013). Therefore, it is profoundly necessary to understand the composition and ecological dynamics of symbiotic bacteria in A. lucorum for the improvement of pest control.

Although the diversity of insect microbes has been extensively studied, most researchers focused on gut microbes (Hulcr et al. 2012; Roh et al. 2008; Salem et al. 2013), and there are few longitudinal studies on the shifts in microbial communities in insect life cycles (Zhao et al. 2019). Therefore, this study focuses on A. lucorum, an important pest in cotton fields, and uses 16S rRNA high-throughput sequencing technology to systematically evaluate the microbial diversity of A. lucorum at multiple life stages to reveal changes in symbiotic bacterial communities during the development of A. lucorum, and to provide a solid theoretical basis for guiding pest control.

Materials and methods

Insect rearing and maintenance

A. lucorum adults were originally collected from the cotton field in Wuhan, Hubei Province, China in July 2014. They were maintained in climate chambers at (75 ± 5)% relative humidity, (26 ± 2) ℃ temperature, 16 h: 8 h light: dark cycle, and fed with beans (Phaseolus vulgaris) and 10% (mass fraction) sucrose solution (Tan et al. 2018).

Sample collection, DNA extraction, and 16S rRNA amplification sequencing

Nymphs of five developmental stages, and male and female adults at 1 day, 6 days and 9 days after eclosion of A. lucorum were randomly collected from colonies. Each life stage had six sample groups, and each group included 20 insects. To remove microbial contaminants on the surface of insects, each sample was soaked in 70% (volume fraction) ethanol for 5 min, followed by 10% bleach (mass fraction) for 30 s, and then rinsed with sterile ultrapure water. DNA was extracted from whole insects using MagPure Stool DNA KF Kit B (Magen, Shanghai, China) according to the manufacturer’s instructions. DNA was quantified using a Qubit Fluorometer with a Qubit dsDNA BR Assay Kit (Thermofisher, Massachusetts, USA), and the quality was assessed by performing an aliquot on 1% (mass fraction) agarose gel. The variable regions V3–V4 of the bacterial 16S rRNA gene were amplified with the degenerate polymerase chain reaction (PCR) primers 338F (5’-ACTCCTACGGGAGGCAGCA-3’) and 806R (5’- GGACTACHVGGGTWTCTAAT-3’). Both forward and reverse primers were tagged with Illumina adapter, pad, and linker sequences. PCR amplification was performed in a 50 μL reaction system containing 30 ng template, fusion PCR primers, and PCR master mix. PCR cycling conditions were as follows: 94 ℃ for 3 min, 30 cycles of 94 ℃ for 30 s, 56 ℃ for 45 s, 72 ℃ for 45 s, and a final extension at 72 ℃ for 10 min. PCR products were visualized with electrophoresis in a 1.8% (mass fraction) agarose gel, then the gel was purified with VAHTS DNA Clean Beads (Vazyme Biotech, Nanjing, China), and quantified using the NanoDrop 2000 (Thermo Fisher Scientific, Massachusetts, USA). Validated libraries were sequenced on an Illumina HiSeq platform (BGI, Shenzhen, China) according to standard Illumina procedures and generated 2 × 300 bp paired-end reads. The sequences obtained in this study were deposited in the GenBank Short-Read Archive (SRA) with accession number PRJNA713416. Sequenced samples were labelled as follows: LL1: 1st instar nymph; LL2: 2nd instar nymph; LL3: 3rd instar nymph; LL4: 4th instar nymph; LL5: 5th instar nymph; LM1D: adult male at 1 day after eclosion; LF1D: adult female at 1 day after eclosion; LM6D: adult male at 6 days after eclosion; LF6D: adult female at 6 days after eclosion; LM9D: adult male at 9 days after eclosion; LF9D: adult female at 9 days after eclosion.

Bioinformatic analysis

Illumina sequencing data was processed with UCHIME (v8.1) and QIIME (v1.9.1) softwares. First, chimeric and low quality sequences were filtered out and removed from downstream analyses. Sequences were spliced using FLASH software (Magoc and Salzberg 2011), paired reads obtained by double-terminal sequencing were assembled into sequences by overlap relationship, and tags of hypervariable regions were obtained. The splicing parameters were as follows: (i) minimum matching length of sequence was 15 bp; and (ii) allowable mismatch rate of overlapping region was 0.1. UPARSE software (Edgar 2013) was then used to cluster sequences at 97% similarity and output the representative sequences of Operational Taxonomic Units (OTU). Chimeric sequences generated by PCR amplification were removed from the representative OTU sequences with UCHIME (v8.1) (Edgar et al. 2011). All tags were compared with representative OTU sequences using usearch_global (Wang et al. 2007). After obtaining representative OTU sequences, taxonomy assignment was performed by comparing OTU representative sequences with the SILVA database using the RDP classifier (v2.2) with a confidence threshold of 0.8.

Differences in community composition were visualized by weighted unifrac and unweighted unifrac Principal Component Analysis (PCA) in R software (v3.1.1). In order to detect the species richness and sequencing depth of each sample, mothur (v.1.31.2) and R (v3.1.1) software were used to calculate coverage, dilution curves, and six alpha diversity indices of community richness: Chao1, ACE, Shannon, Simpson, and Good’s coverage. The Kruskal–Wallis test was used to analyze the difference of relative abundance of species in three groups or more, P values < 0.05 were considered statistically significant (*, 0.01 ≤ P < 0.05; **, 0.001 ≤ P < 0.01; ***, P < 0.001). In order to best visualize differences in species composition, QIIME (v1.9.1) and R (v3.1.1) softwares were used to cluster the samples by Unweighted Pair Group Method with Arithmetic Mean (UPGMA) to analyze beta diversity. PICRUSt was used to predict bacterial community function based on OTU taxonomy and relative abundance, using on Kegg Orthology (KO).


Sequencing quality analysis

Illumina HiSeq sequencing of 16S rRNA V3–V4 region amplicons from A. lucorum yielded a total of 5 336 031 raw reads. Following demultiplexing, quality filtering, and chimera removal, a total of 4 246 604 tags were used in downstream analysis and each of the 66 samples had an average of 64 342 high-quality sequences (Additional file 1: Table S1). High-quality reads were spliced to yield hypervariable region tags, totaling 4 198 796 tags. To each developmental stage of A. lucorum, 144 to 505 OTUs were assigned with a similarity cutoff of 97% (Additional file 1: Table S1). Community richness, based on Good’s coverage, was 99% in all samples at a 3% dissimilarity cutoff (Additional file 1: Table S1) and rarefaction curves showed saturation, indicating that our sequencing had captured most of the bacterial diversity associated with A. lucorum (Additional file 1: Fig. S1). The observed species index dilution curve also tended to flatten, which verified that the depth of sequencing was sufficient to detect all of the bacterial species in the sample (Fig. 1A).

Fig. 1
figure 1

Bacterial community dynamics among different developmental stages in Apolygus lucorum. A Observe species dilution curve. B Relative abundance of bacteria communities at the phylum level in different groups. C Relative abundance of bacteria communities at the genus level in different groups. D Heat map analysis of the top 15 microbial populations with relative abundance at different developmental stages, and the data represented by color in the figure are represented by log2(relative abundance). (Bacteria with relative abundance lower than 0.1% in all samples were all merged into others. The “Kruskal–Wallis test” was used to analyze the differences of phyla at all developmental stages. * 0.01 ≤ P < 0.05, ** 0.001 ≤ P < 0.01, and *** P < 0.001)

Bacterial community structure among different development stages in A. lucorum

Structural composition and classification analysis of microbial samples at different developmental stages of A. lucorum, revealed that Proteobacteria was the most abundant phylum. Among all samples, the top three phyla with the highest relative abundance are Proteobacteria (72.29%), Firmicutes (15.24%), and Actinobacteria (7.76%) (Fig. 1B) (average of all samples). Bacterial community composition ratios shifted significantly at different developmental stages. The relative abundances of Proteobacteria in the male adult life stages LM9D (88.70%), LM6D (87.31%), and LM1D (87.65%) were the highest. In addition, the relative abundance of Proteobacteria was the lowest in the female adult life stage LF9D (49.52%), and the relative abundance of Proteobacteria notably reduced from the LF1D (80.70%) period when the female adult was just emerging (Fig. 1B and Additional file 1: Table S2).

In the nymph stages, the relative abundance of Proteobacteria was the highest in the third instar nymph stage (77.27%), and the lowest in the fourth instar nymph stage (58.49%). Interestingly, the trend of Proteobacteria’s shifts in relative abundances was inverse to the trend of Firmicutes’ shifts in relative abundance in female adult stages. At different stages of female development, the relative abundances of Firmicutes increased from 9.04% to 43.14%. The relative abundances of Actinobacteria during the nymph stages were higher than those of the adult stages (Fig. 1B and Additional file 1: Table S2). Proteobacteria, Firmicutes, and Actinobacteria showed significant differences in relative abundances at different life stages (Additional file 1: Table S2).

Proteobacteria was the dominant phylum during the entire developmental period, and the Erwinia genus of Proteobacteria, was the most dominant genus (Fig. 1C, D). We combined stacked histograms with heat maps to demonstrate the trend of community changes at the genus level. Erwinia (23.97%), Lactococcus (10.62%) and Acinetobacter (8.57%) were the three most abundant genera overall. Moreover, the relative abundances of Erwinia were higher than those of other genera during the development of LL1 (22.14%), LL2 (33.34%), LL3 (44.65%), LL4 (38.87%), LF1D (22.55%), LM1D (45.96%), and LM9D (17.24%) (Fig. 1C and Additional file 1: Table S3). Overall, the relative abundances of Erwinia in the nymph stages were higher than adult stages. However, it is worth noting that the highest relative abundance of Erwinia appeared in newly emerged male adults. After 6 days of adult development, the relative abundances of Erwinia in each stage decreased (Fig. 1D and Additional file 1: Table S3). The relative abundances of Lactococcus in 6-day-old and 9-day-old females were higher than those in males of the same age. Lactococcus was also the dominant genus in the LF6D (25.74%) and LF9D (42.81%). In addition, Acinetobacter’s relative abundance in the fifth instar nymph stage was much higher than that in the newly emerged adult (Fig. 1C, D and Additional file 1: Table S3).

Comparative analysis of microbiota diversity indices at different developmental stages

We used four metrics to explore alpha diversity: Chao1, Simpson, ACE, and Shannon indices (Fig. 2A–D and Additional file 1: Table S1). There were considerable differences in the bacterial species diversity and richness of A. lucorum microbial communities at different developmental stages. The first and second instar nymphs had the highest bacterial community diversity and richness according to the Chao1, ACE and Shannon indices, and were considerably higher than all other developmental stages, showing extremely high complexity. The low Simpson index value of the first and second instar nymphs also reflected the high diversity of bacterial species in these two developmental stages. The community diversity was the lowest in the LM1D stage. Similarly, the community diversity also showed a low level in LF1D. The OTU totals of the first and second instar nymphs, 498 and 505 respectively, were also the highest compared with other developmental stages. The number of total taxa in the LM1D stage was the lowest (Additional file 1: Table S1).

Fig. 2
figure 2

Sequencing analysis of 16S rRNA gene amplicons of A. lucorum with diversity indices. A Chao1 index, B Simpson’ diversity, C ACE index, D Shannon’s diversity. “°” indicates an outlier

The OTU flower diagram in Fig. 3A visualizes the common and unique microbial OTU numbers of insect host A. lucorum at various developmental stages. There were 46 conserved OTUs at different developmental stages. Similarly, first instar nymphs and second instar nymphs had the most unique OTUs, 184 and 168 respectively, followed by third-instar nymphs with 142 OTUs. Bacterial species richness and diversity remained low from fourth instar nymph to adults. Similar results were found in the PCA analysis. The first thru third instar nymphs were clearly distinguished from fourth and fifth instar nymphs and adults (Fig. 3B). UPGMA analysis also verified the above results. The close clustering relationship of first thru third instar nymphs revealed their similarity, and the difference between males and females in different developmental stages of adults was relatively small (Fig. 3C). The results of OTU flower, PCA, and cluster analysis revealed that the bacterial community diversity of the fourth instar nymph and the fifth instar nymph was closer to that of the adult A. lucorum microbiota. Both female and male communities were similar in each developmental stage, and the composition of the first thru third instar nymphs community was especially species rich.

Fig. 3
figure 3

Bacterial community dynamics among different developmental stages in A. lucorum. A Flower of OTU to show common and unique OTUs for all samples. B Comparison of bacterial community structures in development stages using unweighted uniFrac metrics. C Unweighted pair-group method with arithmetic means (UPGMA) analysis of microbial community structure based on 16S rRNA gene amplicon sequencing data

Function prediction

Based on the predicted results of KO, the pathway abundance was showed at three levels. In level 1, “Environmental Information Processing” accounted for 16.99% at all developmental stage, followed by “Metabolism” (16.59%) and “Brite Hierarchies” (8.71%) (Fig. 4). In level 2, “Membrane transport” (16.99%) was the pathway with the highest relative abundance. In the metabolic pathways, the relative abundances of “Carbohydrate metabolism” (3.90%), “Amino acid metabolism” (3.65%), and “Nucleotide metabolism” (3.35%) were the three highest pathways. “Transporters” (7.09%) and “ABC transporters” (4.43%) were the two pathways with the highest relative abundance of level 3, followed by “Two-component system” (2.28%) and “Secretion system” (2.19%), both of which belong to “Environmental Information Processing”. “Purine metabolism” (1.98%), “Ribosome” (1.62%) and “Pyrimidine metabolism” (1.36%) had higher relative abundance in “Metabolism”.

Fig. 4
figure 4

Relative abundance of the top 30 KEGG Orthology (KO) in all developmental stages of A. lucorum


Although the diversity of microbes associated with insect hosts had been widely studied, most studies were limited to the intestinal tract of insects (Augustinos et al. 2019; González-Serrano et al. 2020; Muratore et al. 2020; Wei et al. 2017; Xia et al. 2018), and there were few longitudinal studies on microbial diversity throughout the life cycle of insects. Here, we present the first research that we know of on microbial composition and ecology during the life cycle of the insect A. lucorum. It is well known that the bacterial community in wild-type insects is very different from that raised in the laboratory for a long time (Rani et al. 2009), and different geographical areas will also affect microbial diversity (Zouache et al. 2011). Temperature is also an important factor affecting insect bacterial community (Kikuchi et al. 2016). Although the experimental samples were collected from the field, the bacterial community in A. lucorum was relatively stable after countless generations of breeding in the laboratory. In this study, we used the next-generation sequencing technology to comprehensively identify and analyze the microbial communities at different developmental stages of A. lucorum. We found that phyla Proteobacteria, Firmicutes, and Actinobacteria dominated at various developmental stages (Fig. 1B), which was similar to microbiota composition of other insect symbiotic communities (Chen et al. 2018; Gao et al. 2020; Mason and Raffa 2014; Zhao et al. 2019). Bacterial community structure and diversity are distinct at different developmental stages of insect host A. lucorum. Microorganisms that affect evolutionary and ecological processes such as developmental, physiological, and ecological interactions exist in many insects (Currie et al. 1999; Engel and Moran 2013; Hammer et al. 2017). Although the microbial composition of A. lucorum was significantly diverse in the first and second instar nymph stages, the bacterial diversity observed in the later developmental stages was significantly reduced (Fig. 1A). This phenomenon of reduced bacterial diversity has the same evidence in the nymph of Adelphocoris suturalis, and the direct influencing factor may be the transformation of a carnivorous diet (Luo et al. 2021). A. lucorum's habit of feeding on eggs or smaller members of the nymph during mid-development is very similar to that of A. suturalis, which may well explain the decrease in microbial diversity (Xue et al. 2021).

A. lucorum is an insect with incomplete metamorphosis. According to PCA and UPGMA analysis, the bacterial community diversity of fourth instar nymphs and fifth instar nymphs was closer to that of adults. During the transition period from nymph to adult, the diversity and structure of microorganisms did not change significantly compared with completely metamorphic insects (Zhao et al. 2019). In general, compared with Hyalesthes obsoletus (Iasur-Kruh et al. 2017) and Myzus persicae (Xu et al. 2020) of Hemiptera, the microflora of A. lucorum showed higher biodiversity. There are great differences in microbiomes of different insects, and the microbial diversity of the same insect species is also affected by many factors, such as in vitro environment (Zouache et al. 2011), artificial feed (Priya et al. 2012) and food sources (Broderick et al. 2004; Priya et al. 2012). Because A. lucorum is a highly omnivorous pest, it can feed not only on plants, but also on some small insects or eggs (Li et al. 2016; Lu et al. 2008; Yuan et al. 2013). Complex eating habits may lead to differences in bacterial community diversity and species richness.

Erwinia, which belongs to Gram-negative family Enterobacteriaceae, is both an important intestinal bacterium (Basset et al. 2000) and a symbiote of many arthropods (Estes et al. 2012; Iasur-Kruh et al. 2017). In this study, Erwinia populations were found to be continuous throughout the development cycle, indicating that Erwinia a persistent symbiont and maybe important in the growth, development, and survival of A. lucorum. Erwinia can metabolize most nitrogen, sulfur, and phosphorus sources (Friedl et al. 2008), indicating an important role in the digestion and metabolism of insects. Similarly, Candidatus Erwinia dacicola may also benefit the larval stage of the fruit fly (Estes et al. 2012). Olives, especially unripe olives, are defended with antimicrobial secondary metabolites (Levinson and Levinson 1984). Fruit fly larvae lacking symbiont C. Erwinia dacicola can not survive in unripe olives and few can pupate when olives are ripe (Estes et al. 2012). In this study, the average relative abundance of Erwinia in the nymph stage was higher than that in the adult stage, which was consistent with previous studies (Yong et al. 2017). We hypothesize that the nymphs’ digestion, metabolism, and drug resistance is weaker than adults, and that Erwinia can degrade complete plant tissues and produce antimicrobial agents to help A. lucorum larvae grow and develop, thus helping A. lucorum have a strong ability to cope with complex environments. Our functional prediction results also give another explanation: most bacterial functions are concentrated in “Environmental Information Processing” and “Metabolism” to meet the adaptability of insects to the environment. Further research might include genome sequencing of Erwinia symbionts to explore these metabolic roles. Interestingly, Erwinia is also the main pathogenic source of soft rot in fruits, vegetables, and ornamental plants (Grenier et al. 2006). It can secrete a variety of cell wall degrading enzymes, causing potato black leg disease, soft rot, Fusarium wilt, and other plant diseases (Whitehead et al. 2002). In the process of feeding, A. lucorum uses its needle tip to pierce plant tissues, inject saliva digestive enzymes, and ingest liquefied plant substances. Polygalacturonase is one of the enzymes that can digest plant tissues and cause damage to plant tissues (Liu et al. 2021). Further research is needed to elucidate if A. lucorum spreads the plant pathogen Erwinia.

At the same time, we also found that the average relative abundance of Lactococcus was higher in female adults than in male adults. Six-day-old female adults we selected were samples obtained based on the pre-oviposition data of A. lucorum (Zhen et al. 2018). Lactococcus is abundant in the insect intestinal tract (De Jonge et al. 2020), and can decompose sugars, produce organic acids, reduce the pH value of the environment, and resist some acid-sensitive pathogenic bacteria (Evans and Armstrong 2006). These bacteria are well-known for the ability to ferment complex carbohydrates and produce lactic acid, which can be oxidized to pyruvate and participate in the tricarboxylic acid cycle, and which can also be converted into glucose in the liver, thus playing an important role in the insect nutritional pathway, and the nutritional pathways of some insects are closely related to reproduction (Carpenter et al. 2012; Hansen et al. 2004; Koyama et al. 2013; Lu et al. 2016; Pérez-Hedo et al. 2013; Roy and Raikhel 2011; Sancak et al. 2008). Therefore, we speculate that Lactococcus may have an important connection to the reproduction of A. lucorum. However, Wolbachia and Rickettsia that are ubiquitous in several other arthropods and play an important role in insect reproduction (Duguma et al. 2013; Rasgon and Scott 2004), were  not detected during multiple developmental stages of A. lucorum in this study.

In this study, we provide an understanding of the symbiotic biodiversity and community composition dynamics in different life cycle stages of A. lucorum. This knowledge of the A. lucorum microbiome may lead to important practical applications in the development of microbially based strategies for the management of insect pests (Crotti et al. 2012). For example, future pest control might be based on targeting essential microbial symbionts to insect host survival and thus eliminating host insect pest. Similarly, this research might inform future pest control which may involve preventing microbe-facilitated insecticide resistance in host microbe.These results provide an important theoretical basis for the control of A. lucorum, including the theoretical relationship between drug resistance and reproduction of microorganisms in A. lucorum, and thus provide novel approaches for guiding new pest control strategies.


The bacterial community composition and diversity of A. lucorum changed at different developmental stages, and these bacteria have potentially important functions in different developmental stages. The high abundance of Erwinia reflects that it plays an important role in the physiology and biochemistry of A. lucorum, involving important functions such as metabolism, nutrition absorption and detoxification. Lactococcus, the dominant genus in LF6D and LF9D period, may be the key genus in the reproduction of female A. lucorum. More research is needed to verify the function of these bacteria. This research reveals for the first time the community composition and ecological dynamics of symbiotic bacteria throughout the life stages of A. lucorum, and thus may provide insight to new strategies for pest control.

Availability of data and materials

All the sequencing data of this experiment have been uploaded to NCBI, the accession number is PRJNA713416.


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Thanks for the support of experimental technology and equipment provided by the Institute of Cotton Research of Chinese Academy of Agricultural Sciences.


This research was supported by Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences.

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Authors and Affiliations



Conceptualization, Cui JJ and Gao XK; Methodology, Gao XK; Software, Xue H, and Niu L; Validation, Cui JJ; Formal analysis, Xue H; Investigation, Xue H and Zhu XZ; Resources, Luo JY; Data curation, Xue H, Ji JC, and Gao XK; Writing—original draft preparation, Xue H; Writing—review & editing, Cui JJ and Gao XK; Visualization, Wang L and Li DY; Supervision, Zhang KX; Project Administration, Zhang KX; Funding acquisition, Cui JJ. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Gao Xueke, Luo Junyu or Cui Jinjie.

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Supplementary Information

Additional file 1.

Table S1. 16S rRNA gene sequencing data. Table S2. Relative abundance of bacteria communities at the phylum level in different group. Table S3. Relative abundance of bacteria communities at the genus level in different group (Top 15). Fig. S1. Alpha diversity dilution curve.

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Xue, H., Zhu, X., Wang, L. et al. Dynamics and diversity of symbiotic bacteria in Apolygus lucorum at different developmental stages. J Cotton Res 6, 5 (2023).

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  • Symbiotic bacteria
  • Apolygus lucorum
  • Life cycle
  • Pest control
  • Community composition
  • Relative abundance
  • Community richness