Developmental Dynamics of Bacterial Microbiota in Aphis gossypii Revealed Using Full-Length 16S rRNA Sequencing

Abstract

The cotton aphid, Aphis gossypii, is a globally significant agricultural pest whose microbiota plays vital roles in its physiology and adaptation. However, the dynamics of bacterial communities across its developmental stages remain poorly understood. This study employed full-length 16S rRNA gene sequencing to characterize the microbiota structure, diversity, and functional potential in nine developmental stages of A. gossypii, including egg, nymph (1-, 3-, 5-, 7-day-old), and adult (1-, 3-, 5-, 7-day-old). Results revealed Proteobacteria (72.75–95.51%) as the dominant phylum across all stages, with Buchnera aphidicola (primary obligate symbiont) constituting over 23.83% of bacterial abundance and peaking in eggs (≈80%). Alpha diversity indices (Shannon, Simpson) indicated significantly higher microbial diversity in nymphs compared to adults, suggesting stage-specific ecological interactions. While beta diversity analysis showed no structural clustering by developmental stage, functional predictions highlighted enrichment in metabolic pathways (>73% of genes), though limitations in 16S-based functional inference were noted. Notably, facultative symbionts like Hamiltonella or Serratia were absent, contrasting with other aphid systems. Dynamic shifts in Buchnera titer and the prominence of Delftia tsuruhatensis and Enterobacter hormaechei implied potential roles in host adaptation. These findings highlight the persistent dominance of the obligate symbiont Buchnera aphidicola across all developmental stages, despite quantitative fluctuations in its abundance, alongside stage-specific shifts in facultative bacterial communities, offering insights into novel targets for microbiome-driven pest management strategies. Further multi-omics approaches are warranted to validate functional contributions of these microbial communities.

1. Introduction

Aphis gossypii Glover, 1877, commonly known as the cotton or melon aphid, is a small, soft-bodied insect belonging to the family Aphididae. It is a highly polyphagous pest, feeding on over 700 plant species worldwide, including members of the Asteraceae, Cucurbitaceae, Malvaceae, Rutaceae, Solanaceae, and Fabaceae families [1,2]. This aphid causes significant agricultural damage through direct feeding, leading to leaf curling, chlorosis, and stunted growth. Additionally, it excretes honeydew, which fosters sooty mold growth, reducing photosynthesis and crop quality [3]. More critically, A. gossypii is a vector for numerous plant viruses, including cucumber mosaic virus and cotton leaf curl virus, exacerbating its impact on crop yields. Control strategies include the use of insecticides such as carbamates and organophosphates. However, the species has developed resistance to several chemical classes, complicating management efforts [4,5].

Insects harbor diverse and specialized bacterial microbiota that play crucial roles in their physiology, ecology, and evolution. These microbial communities, particularly within the gut and bacteriocytes, contribute to digestion, immunity, development, and adaptation to environmental challenges [6]. For instance, bacterial microbiota assist in breaking down complex dietary components, such as lignocellulose in termites, facilitating nutrient absorption. Microbiota can metabolize toxic compounds, including pesticides, thereby contributing to the insect’s resistance and survival. Symbiotic bacteria stimulate the host’s immune system, enhancing defense against pathogens. Microbial communities also influence insect growth and reproductive success, with some bacteria providing essential nutrients during development [7,8,9].

Aphids maintain intricate symbiotic relationships with bacteria that are vital to their survival, development, and ecological adaptability [10,11]. The primary obligate endosymbiont, Buchnera aphidicola, resides within specialized cells called bacteriocytes and has co-evolved with aphids for over 160 million years. Buchnera synthesizes essential amino acids lacking in the aphids’ phloem sap diet, thereby supporting their growth and reproduction. In addition to Buchnera, aphids often harbor facultative symbionts such as Hamiltonella defensa, Regiella insecticola, and Serratia symbiotica. These secondary symbionts, while not essential for survival, confer various ecological advantages, including resistance to heat stress, pathogens, and parasitoids, as well as influencing host plant utilization and reproductive strategies [12,13]. The composition and diversity of these symbiotic communities can vary among aphid populations, contributing to host specialization and population differentiation. Understanding these symbiotic associations not only sheds light on aphid biology and evolution but also offers potential avenues for pest management strategies [14,15].

The composition and diversity of insect microbiota can shift significantly across the different developmental stages of host insects due to changes in diet, habitat, and physiological needs [8,16]. Understanding the microbiota associated with Aphis gossypii across different developmental stages is crucial because microbial communities can significantly influence insect growth, reproduction, immunity, and adaptation [17,18]. In many insects, this relationship is dynamic; shifts in microbiota composition throughout development can affect nutrient assimilation, resistance to pathogens, and overall fitness [19]. In Spodoptera frugiperda, the dynamics changes in bacterial communities resulted in differences in the metabolic functions of the gut microbiota during development [19], while in the invasive hornet Vespa velutina nigrithorax, the overall taxonomic composition alters throughout the life cycle. Alpha diversity analyses revealed that eggs possessed higher bacterial diversity compared to larvae, pupae, and adults [20].

While the microbiota changes in A. gossypii are still unclear, this study aims to characterize the bacterial communities of Aphis gossypii across developmental stages and to determine how these communities change during development. Prior investigations have predominantly relied on partial 16S rRNA gene sequencing. These short-read approaches often provide genus-level or ambiguous species-level taxonomic assignments due to limited sequence information, especially when distinguishing closely related bacteria within aphid-associated microbiota [8]. We hypothesize that microbiota composition varies significantly between stages, reflecting their potential roles in insect development. Full-length 16S rRNA gene sequencing enables the capture of the entire gene, incorporating all nine hypervariable regions. This comprehensive coverage enhances taxonomic resolution down to the species or even strain level, reducing ambiguities in microbial classification [21].

In this experiment, based on full-length 16S rRNA gene sequencing, we measured the bacterial microbiota in nine different developmental stages of A. gossypii, including the egg, 1-day-old nymph, 3-day-old nymph, 5-day-old nymph, 7-day-old nymph, 1-day-old adult, 3-day-old adult, 5-day-old adult, and 7-day-old adult. Additionally, the microbiota diversity, bacterial abundance, and microbiota function at each stage were fully explored. Understanding the gut microbiota of aphids is crucial for developing sustainable pest management strategies, as these microbes influence aphid development, reproduction, and adaptability. Insights into their microbiome can lead to innovative biological control methods that reduce reliance on chemical pesticides, benefiting both agriculture and the environment. This research enhances our ability to harness microbial interactions for pest regulation, with potential applications in improving crop protection and promoting ecological balance.

2. Materials and Methods

2.1. Collection of Aphis gossypii Samples at Different Developmental Stages

A population of Aphis gossypii used in this study was originally collected in 2018 from Gossypium hirsutum L. in Jinan, China (116.21° E, 36.02° N). Prior to experimentation, the aphids were reared on cotton leaves for over six months under controlled laboratory conditions: 25 ± 2 °C, 60 ± 5% relative humidity, and a photoperiod of 16 h light and 8 h dark. Aphids were synchronized by collecting eggs laid within a 24-h window. Post-hatching, nymphs were transferred to fresh cotton leaves and aged daily. Developmental stages were determined by egg hatching and emergence. Samples were collected from nine distinct developmental stages: the egg, 1-day-old nymph (24 h post-hatching), 3-day-old nymph (72 h post-hatching), 5-day-old nymph (120 h post-hatching), 7-day-old nymph (168 h post-hatching), 1-day-old adult (24 h post-molt), 3-day-old adult, 5-day-old adult, and 7-day-old adult. Eggs were carefully detached using sterilized fine forceps under a dissecting microscope to prevent damage. Post-collection, eggs were washed gently in sterile phosphate-buffered saline (PBS) to remove surface contaminants and stored at 4 °C in sterile containers until DNA extraction, conducted within 24 h to preserve integrity. Leaf condition was assessed visually prior to sampling for signs of disease or damage, and only healthy, uniform leaves were used. Aphid health status was verified by assessing physical appearance, developmental stage consistency, and absence of visible pathogens or deformities. For DNA extraction, for each stage, four replicates were obtained, each comprising 20 individuals. Samples were labeled with IDs reflecting the stage (e.g., ‘egg’), age (e.g., 5-day-old nymphs), and replicate number (e.g., 5-day-old nymph-1) in Table S1. Aphid DNA was extracted using the TaKaRa MiniBEST Universal Genomic DNA Extraction Kit Ver.5.0 (Takara, Biotechnology Co., Ltd., Dalian, China). All samples were stored at −20 °C until subjected to full-length 16S rRNA gene sequencing.

2.2. Full-Length 16S rRNA Gene Sequencing Across Developmental Stages

DNA extracted from aphid samples was evaluated for integrity and purity using a Nanodrop spectrophotometer Thermo Scientific SPECTRONIC 200 (Thermo Scientific, Waltham, MA, USA). To minimize cross-contamination, DNA extraction and PCR setup were conducted in separate, designated areas to further reduce contamination risk. All reagents, consumables, and pipettes were certified DNA-free, and all work surfaces and instruments were routinely decontaminated with DNA-away and 70% ethanol. Prior to OTU clustering, sequence reads were quality-filtered to remove low-quality, short, and chimeric reads. During downstream analysis, to investigate the variations in bacterial microbiota across different developmental stages of A. gossypii, full-length 16S rRNA gene sequencing was performed. DNA extraction, amplification, library construction, and sequencing were conducted by the Biomarker Technologies Corporation (Beijing, China). The universal primer set 27F (AGRGTTTGATYNTGGCTCAG) and 1492R (TASGGHTACCTTGTTASGACTT) was employed to amplify the full-length 16S rRNA gene from genomic DNA extracted from each sample, incorporating barcode and adapter sequences. PCR amplification followed the protocol outlined by our previous study [22]. Quantification of PCR products was performed using the Quant-iT™ dsDNA HS Reagent, after which the products were pooled. Sequencing was carried out on the PacBio SMRT RS II platform (Pacific Biosciences, Menlo Park, CA, USA), with off-target and low-quality sequences filtered using PacBio’s circular consensus sequencing technology [23].

2.3. Data Analysis

Phylogenetic analysis and diversity indices were computed, including alpha diversity metrics (Ace, Chao1, Shannon, and Simpson) and beta diversity assessments (Principal Coordinates Analysis [PCoA] and heatmaps based on the Bray–Curtis similarity). Multiple sequence alignment and phylogenetic tree construction were performed using QIIME command-line tools. The phylogenetic tree was then integrated with taxonomic abundance data and visualized using the R package ggtreeExtra version 1.4.2, which combines a circular phylogenetic tree with bar plots. Operational Taxonomic Units (OTUs) and taxonomic assignments were generated using USEARCH v12 and mothur v1.30.0, respectively. Beta diversity analyses were conducted using QIIME software version 2 on the BMK Cloud platform accessed on 11 November 2024 (http://www.biocloud.net). PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) was used to predict functions of bacteria based on full-length 16s rRNA genes in the KEGG database (https://www.kegg.jp/). To evaluate diversity indices and bacterial abundance in A. gossypii, the Shapiro–Wilk test (SPSS 21.0) was applied to assess the normality of alpha diversity indices and OTU count data. For normally distributed data (p < 0.05 considered evidence of departure from normality), one-way ANOVA followed by post-hoc LSD analysis was performed. For data not conforming to normal distribution, the Kruskal–Wallis test and Dunn’s test with Bonferroni correction for multiple comparisons were utilized. All statistical analyses were conducted using SPSS 21.0. Statistical significance was set at p < 0.05.

3. Results

3.1. Sequencing Data of Bacterial Microbiota in Aphis gossypii

Based on the results, in total, 31 bacterial phylum and 1616 bacterial species were detected in different developmental stages of Aphis gossypii. The effective Circular Consensus Sequences (CCS) ranged from 41,719 to 66,888, the average length of CCS ranged from 1430 bp to 1471 bp, the percentage of effective CCS reads in raw reads was all above 97.68%, and the number of OTU numbers ranged from 284 to 700 (Table S1). For the top abundant 20 bacterial species, most species were from the phylum Proteobacteria, others were from Firmicutes, Actinobacteriota, and Methylomirabilota, and all bacterial species from the same phylum clustered together (Figure S1). The related abundance of bacterial species is shown in Figure S1 and Table S2.

3.2. Bacterial Communities in Different Growth Stages of Aphis gossypii

Based on full-length 16s rRNA gene sequencing, in A. gossypii, Proteobacteria bacteria occupy the largest proportion (over 72.75%) of the total bacterial abundance across all growth stages of A. gossypii, while Actinobacteriota bacteria are the second most abundant (Figure 1). Regarding bacterial species, the primary symbiont, Buchnera aphidicola, is the most dominant bacterium, occupying more than 23.83% mean relative abundance of the bacterial abundance in different A. gossypii stages. Delftia tsuruhatensis and Enterobacter hormaechei are also abundant in A. gossypii (Figure 2). Based on Principal Coordinates Analysis (PCoA), all samples from different growth stages clustered together, which showed an insignificant influence of different A. gossypii growth stages on the structures of microbiota (PERMANOVA: R² = 0.045, p = 0.18) (Figure S2).

For A. gossypii, which was divided into the egg, nymph, and adult stages, Proteobacteria and Actinobacteriota were also the two most abundant bacterial phyla, while Proteobacteria occupied greater proportions in the egg stage (Figure S3). Buchnera aphidicola also occupied a greater proportion in the A. gossypii egg compared to the nymph and adult stages (Figure 3).

3.3. Bacterial Microbiota Varied in Different Growth Stages of Aphis gossypii

The bacterial diversity across various developmental stages of Aphis gossypii was assessed using full-length 16S rRNA gene sequencing. For Shannon and Simpson indexes, the analysis revealed that nymph stages exhibited the highest diversity, with a Shannon index of 3.19 ± 1.26 (Mean ± SEM) in the 5-day-old nymph and 3.05 ± 0.80, and a Simpson index of 0.68 ± 0.18 in the 7-day-old nymph, which were significantly higher than that of the 5-day-old adult (one-way ANOVA, p < 0.05). ACE indices of the 1-day-old, 3-day-old, 5-day-old nymph, and 1-day-old adult were significantly higher than that of the 5-day-old adult. The Chao1 index of the 1-day-old adult was significantly higher than that of the 5-day-old adult (one-way ANOVA, p < 0.05) (Figure 4). Additionally, when comparing the egg, nymph, and adult stages, the Shannon index of the nymph 2.59 ± 0.42 was significantly higher than that of the adult 1.39 ± 0.28, and the Simpson index of the nymph 0.53 ± 0.07 was significantly higher than that of the adult 0.31 ± 0.07 (one-way ANOVA, p < 0.05) (Figure S4).

Proportions of the Buchnera aphidicola in different A. gossypii developmental stages were compared based on 16s rRNA sequencing data using one–way ANOVA (Figure 5). Based on the results, the proportion of the Buchnera aphidicola in the 5-day-old adult 0.91 ± 0.04 was significantly higher than that of the 7-day-old nymph 0.24 ± 0.23 (one-way ANOVA, p < 0.05) (Figure 5A). When comparing the egg, nymph, and adult stages, no significant differences in Buchnera aphidicola in different stages were detected (Figure 5B).

3.4. Functions of Bacterial Microbiota Changed with the Development of Aphis gossypii

Based on the KEGG database, functional prediction analysis revealed that genes related to metabolism were most enriched in all growth stages of A. gossypii, with proportions exceeding 73% in all developmental stages. Additionally, there are also large proportions of bacterial genes related to environmental information and genetic information processing (Figure S5). In the KEGG level 2 classification, most bacterial genes were associated with global and overview maps, with a large proportion linked to carbohydrate metabolism, amino acid metabolism, membrane transport, and other processes (Figure S6). In the KEGG level 3 classification, most bacterial genes were mapped to metabolic pathways, while other genes were associated with the biosynthesis of secondary metabolites, antibiotics, and other related functions (Figure S7).

4. Discussion

In this experiment, bacterial phylum Proteobacteria was most abundant in all A. gossypii stages, the proportions ranged from 72.75% in the nymph 5d to 95.51% in the egg stage (Figure 2 and Figure S2), and most abundant bacterial species were in the Proteobacteria phylum, with B. aphidicola being the dominate bacterial species (Figure 2 and Figure S1). In other related studies, bacteria of Proteobacteria and Buchnera aphidicola were also the most abundant bacterial in host insects, such as in Melanaphis sacchari [16], Myzus persicae (Sulzer) [24], and Mollitrichosiphum aphids [25]. Similar results indicated that the present experiment is reliable.

In aphids, there is one primary endosymbiont, Buchnera aphidicola, and several facultative symbionts, such as Regiella, Hamiltonell, Wolbachia, Serratia, Arsenophonus, and so on [26,27]. In this experiment, only the primary symbiont Buchnera aphidicola was detected, similar to the research of microbiota in the sugarcane aphid Melanaphis sacchari, as only one symbiont, B. aphidicola, was detected in Melanaphis sacchari [16]. Buchnera aphidicola is known to have co-evolved with aphids for over 160 million years, forming an obligate mutualistic relationship essential for the survival and reproduction of both partners. Buchnera aphidicola compensates for this nutritional deficiency by synthesizing essential amino acids for the aphid host. Beyond nutrition, Buchnera aphidicola can influence the aphid’s physiological traits. For instance, certain strains have been associated with increased thermal tolerance in aphids, suggesting a role in adapting to environmental stresses [28,29,30]. In this study, Buchnera aphidicola is abundant in all growth stages of A. gossypii, especially in the egg stage, with a proportion of about 80% in all bacterial microbiota, indicating a vital role throughout the development of A. gossypii.

Our observation that Buchnera aphidicola remains the dominant member of the A. gossypii microbiota throughout development is consistent with its established role in essential nutrient provision [31,32]. However, as our study focused on relative abundance data, functional inferences should be drawn with caution. Apart from Buchnera, stage-specific shifts in rare taxa were detected, which may reflect developmental or ecological factors, as suggested in both A. gossypii [33] and other aphid species [34]. Alternative explanations for these shifts include possible changes in plant sap composition or stochastic colonization processes characteristic of aphid life cycles [35].

Except for Buchnera aphidicola, Delftia tsuruhatensis and Enterobacter hormaechei were also abundant in A. gossypii. Delftia tsuruhatensis is a Gram-negative bacterium first isolated from activated sludge in 2003. It has garnered attention for its diverse metabolic capabilities and emerging clinical significance [36,37,38]. Delftia tsuruhatensis TC1 symbiont could suppress malaria transmission in anopheline mosquitoes [39]. Enterobacter hormaechei is a Gram-negative bacterium belonging to the Enterobacter cloacae complex (ECC), known for its clinical significance and environmental ubiquity. First described in 1989, E. hormaechei has emerged as a notable opportunistic pathogen [40,41,42]. Low-level laboratory contamination has been reported for Delftia and Enterobacter [43], so their presence should be interpreted with caution.

In Figure 2, the relative abundance of Enterobacter varies significantly among different nymphal stages. Specifically, its abundance is higher in the early nymphal stages (instars 1–2), decreases in mid-stages (instar 3), and is lowest or nearly undetectable in the late nymphal and adult stages. This pattern is consistent with the findings of [43], who observed that Enterobacter was more prevalent in the early developmental stages of certain hemipteran insects, possibly due to its role in nutrition or immune modulation during early growth, potentially reflecting nutritional or immune requirements during development or environmental acquisition.

The relationship between symbiont titer and function is a critical aspect of insect physiology and ecology. Symbiont titers can influence host development, reproduction, and survival, while hosts have evolved mechanisms to regulate symbiont populations to maintain mutualistic benefits and prevent potential costs [44,45,46]. A high load of symbionts was considered to induce fitness costs in many studies [47,48,49]), especially in the pea aphid Acyrthosiphon pisum, where higher titers of the obligate symbiont Buchnera aphidicola are associated with longer development times and reduced fecundity. This suggests that while Buchnera provides essential nutrients, excessive symbiont loads can impose metabolic costs on the host [48]. In this study, Buchnera aphidicola, Delftia tsuruhatensis, and Enterobacter hormaechei titers significantly varied in different stages of A. gossypii (Figure 3 and Figure 4), which showed a trade-off associated with bacteria abundance regulation to meet changing physiological needs in different growth stages.

Microbiota diversity significantly impacts host insect physiology and ecology. A diverse microbial community can enhance digestion, nutrient absorption, immune responses, and resistance to pathogens [6,50]. Insect microbiota plays crucial roles in various aspects of insect biology, including digestion, nutrition, development, immunity, and environmental adaptation [8,18,22]. Aphids maintain intricate symbiotic relationships with bacterial microbiota that are essential to their survival, development, and ecological adaptability [29,31,34]. In this experiment, using the function prediction of 16s rRNA gene sequencing results, most bacterial genes of A. gossypii were related to metabolism and environmental information processing functions (Figure S2). However, based on other research, it is notable that the function prediction based on full-length 16s rRNA gene sequencing is not so reliable [51]. The functions of microbiota in A. gossypii should be further explored using more examinations.

However, regarding the study’s limitations, we acknowledge that while our use of full-length 16S rRNA sequencing provides high taxonomic resolution, this approach is not without potential biases. Amplicon-based methods are susceptible to primer specificity issues, PCR amplification bias, and sequencing errors, each of which can influence the observed composition or relative abundance of bacterial taxa. Although our workflow included rigorous quality filtering and negative controls to minimize contaminants, technical artifacts such as chimeras or differential amplification efficiency may still affect certain results, especially among low-abundance taxa. Additionally, our study lacks comprehensive environmental and contextual metadata—such as host plant microbiota or local abiotic factors—which may shape symbiont community structure. Functional predictions based solely on 16S data have inherent limitations in accuracy, as they cannot substitute for direct metagenomic or metatranscriptomic evidence of gene content or activity.

Our findings provide a foundation for developing microbiome-based strategies for aphid pest management, yet further steps are needed for their practical application. Specifically, targeting symbionts that are essential for aphid survival or those that influence insecticide resistance and plant virus transmission could be an effective approach. For instance, future research could experimentally manipulate key symbionts such as Buchnera or identify facultative symbionts to assess the impact on aphid biology and pest status. Additionally, efforts could focus on introducing or promoting antagonistic microbes in the aphid environment, either through biocontrol agents or by modifying host plant microbiota, to disrupt the beneficial symbiont community within aphids. By pursuing these lines of research, future integrated pest management programs could leverage the aphid microbiome for highly specific, eco-friendly, and sustainable aphid control strategies that complement or reduce reliance on chemical insecticides.