Microbiome–Metabolome Analyses Reveal Compound Risks from Multiple-Generation Cocoon Accumulation in Honeybee Combs

TL;DR

Old honeybee combs accumulate harmful microbes and pesticides over generations, threatening colony health and suggesting the need for regular comb replacement.

Contribution

This study combines microbiome and metabolomic analyses to reveal how multi-generational cocoon accumulation in honeybee combs increases health risks.

Findings

Older cocoons contain higher levels of harmful bacteria, fungi, and pesticide residues.

Microbial diversity shifts significantly with multiple generations of cocoon accumulation.

Pesticides and pathogens show strong positive correlations, compounding health risks.

Abstract

Honeybees reuse the same comb cells many times, leaving behind silk-like cocoon layers. Over generations, these layers build up inside old combs. The accumulation of cocoons within brood cells of old combs is a key factor causing a series of negative impacts on bee colonies. By studying both the microbes and chemicals present in cocoons from new and old combs, we discovered that older cocoons contain more harmful bacteria and fungi, as well as higher levels of pesticide residues. These pollutants and pathogens appear to work together, increasing health risks to bee brood. Our findings show that regularly replacing old combs can help protect honeybee colonies from hidden threats. This practice supports more sustainable beekeeping, which in turn benefits agricultural productivity and environmental health.

The accumulation of cocoons within brood cells of old combs is a key factor causing a series of negative impacts on bee colonies. Previous studies did not sufficiently address this dynamic nature as the core microenvironment for preimaginal bee development. During this accumulation, the enrichment of potentially harmful microorganisms and chemical substances may pose a latent threat to colony health. This study combined microbiome and metabolomics analyses to systematically investigate the potential colony health risks posed by multi-generational accumulation of cocoons in Apis mellifera combs. The results demonstrated that with the growing number of brood rearing generations, the microbial diversity within the cocoons underwent significant shifts. For the bacterial community within multiple-generation cocoons, the Simpson index exhibited a significant increase, whereas indices including Sobs, Ace, and Chao showed significant decreases (p < 0.05). In the fungal community, the Shannon and Pielou_e indices significantly increased, while the Simpson and Faith_pd indices significantly declined (p < 0.05). Potential pathogens such as Melissococcus and the mycotoxin-producing fungus Wallemia became significantly enriched, reaching alarming relative abundances of 42.70% and 13.52%, respectively, in the multiple-generation cocoons. Metabolomic analysis further revealed the enrichment of 685 differential metabolites, including persistent exogenous pesticides such as cyanazine and pymetrozine, etc. Correlation analysis uncovered a significant positive relationship (r > 0.8) between these pesticide residues and pathogen abundance, indicating interactions between pollutants and pathogens that may exacerbate risks. This study reveals the aggravation of microecological imbalance and chemical pollution load within the cocoons of old combs and therefore provides strong scientific support for risk assessment of comb age in colony health management and offers practical guidance for the sustainable development of beekeeping.

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1. Introduction

Honey bees are indispensable pollinators in terrestrial ecosystems. They play a central role in supporting agricultural productivity and preserving the diversity of natural ecosystems [1]. Furthermore, bee products (e.g., honey and royal jelly) have significant economic and nutritional value [2]. These products are widely used across the food and pharmaceutical industries [3]. However, global colony numbers have been declining [4]. In addition to external pressures such as parasites and pesticides, various biotic and abiotic stress factors can disrupt the gut microbiota, immune responses, and cognitive abilities of honey bees, thereby accelerating colony losses [5,6]. Notably, the nest, as a semi-enclosed microenvironment, gradually accumulates environmental pollutants through foraging activities and the routine application of therapeutic substances in apiculture [7,8]. This buildup creates potential risks for individuals at all developmental stages of the colony. Consequently, the quality of the internal nest environment is a key factor influencing colony health.

The comb, as the central structure of the nest, is essential for maintaining colony health [9,10]. With prolonged use, comb aging causes two major categories of negative effects, creating a compounded threat to colony performance. On one hand, the volume of cells used for rearing multiple generations of brood decreases and restricts the available developmental space for larvae and pupae [11,12]. This results in lower emergence weight and smaller worker morphology [13]. These changes reduce foraging efficiency and shorten worker lifespan, ultimately weakening colony strength and productivity [14,15]. On the other hand, the main components of beeswax (e.g., long-chain esters and hydrocarbons) have a high affinity for lipophilic environmental pollutants [16,17]. As a result, old combs act as a long-term sink for harmful substances. These pollutants remain in the wax and gradually migrate or release into other comb matrices, such as bee bread, honey, and the brood rearing environment [18,19]. This process creates a continuous source of chronic exposure and introduces potential sublethal or long-term toxic risks for all developmental stages of the colony.

The cocoon is an important yet often overlooked component of the comb. It is composed of silk secreted by the spinneret of bee larvae during the spinning larval stage, along with excreted material such as feces [20]. The cocoon adheres tightly to the inner cell wall, becomes integrated with beeswax, and continues to thicken as more brood rearing generations are completed [11,21]. The silk layer has strong permeability and hygroscopicity. These properties allow it to absorb moisture from the nest, especially under high temperature and humidity, which makes it a suitable carrier for accumulating microorganisms and pollutants [22,23,24]. Studies have reported significantly higher concentrations of heavy metals, including lead and cadmium, in multiple-generation cocoons. These accumulated metals can negatively affect brood immune development [25]. Pathogens such as Melissococcus plutonius (European foulbrood) and Paenibacillus larvae (American foulbrood) can also establish infectious sources on combs and spread through ingestion [26,27]. The gradual buildup of cocoon layers may therefore act as a refuge and transmission pathway for these pathogens.

The cocoon forms the core microenvironment surrounding the preimaginal developmental stages of honey bees [28]. Bees remain in constant contact with this structure during the egg, larval, and pupal stages. Their bodies and food directly interact with the cocoon, making its physicochemical condition highly relevant to colony health [25]. Consequently, bees developing in old combs containing thick cocoon layers may face increased health risks. While previous studies have focused on the physical alterations in comb structure due to cocoon accumulation and their effects on the morphological development of honey bees, and the presence of contaminants such as pesticides in old beeswax has been widely recognized, the intrinsic dynamics of the cocoon as a dynamic micro-niche and pollution source remain poorly characterized [9,11]. This study therefore compared the microbial (bacterial and fungal) and metabolic profiles of cocoons accumulated over one versus eight generations within the comb cells of Apis mellifera, to identify the harmful microorganisms and potential toxins enriched in them and to improve the understanding of the risks associated with cocoon accumulation. This study contributes fundamentally by revealing how multiple-generation cocoons function as a reservoir that dynamically interacts with microbial communities. These findings together provide a theoretical basis for colony health management and for assessing the hazards of old combs.

2. Materials and Methods

2.1. Cocoon Sample Collection

This study used three healthy A. mellifera colonies maintained at the experimental apiary of Yunnan Agricultural University, each standardized to a population of 8 frames of adult bees and 5 frames of brood. All colonies were housed in standard Langstroth ten-frame hives and had comparable colony strength. For each colony, a single empty frame (inner dimensions: 42.0 × 20.0 cm) reinforced only with stainless steel wires was prepared and labeled with a unique identification number. To promote beeswax secretion and comb construction, each colony received 1 L of 50% (w/v) sucrose solution every evening for five consecutive days. Once the new comb was built, the stored honey was removed via gentle centrifugal extraction to obtain a freshly constructed worker comb. This method was chosen to minimize physical damage to the delicate newly built cell walls and wax matrix, thereby preserving the integrity of the substrate for subsequent cocoon adhesion. Synchronized brood rearing was then initiated using a queen excluder cage. Each experimental comb and its corresponding queen were confined in the cage for 48 h to obtain a sufficient number of age-synchronized worker eggs for the experiment, during which the colonies continued to receive the 50% (w/v) sucrose solution supplementation. After cage removal, the comb was returned to the original colony for brood rearing. Approximately 19 days later, the comb was collected after the first generation of workers emerged. A 5 × 5 cm section of the comb was cut, and the cocoons inside the cells were extracted with sterile forceps, forming the single-generation cocoon samples. The synchronized brood rearing procedure was then repeated. After each worker emergence, the same comb and queen were again confined in the queen excluder cage for 48 h. After the cage was removed, the comb remained in the original colony for brood development. This procedure was carried out for eight consecutive brood cycles (from March to September 2024), resulting in combs that had reared eight generations of workers. Cocoon samples extracted from these combs were designated as multiple-generation cocoon samples. All cocoon samples were placed immediately into sterile centrifuge tubes and stored at −80 °C in an ultra-low temperature freezer until analysis.

2.2. Microbial Diversity Analysis

Total genomic DNA was extracted from the cocoon samples using the E.Z.N.A.^®^ soil DNA kit (Omega Bio-tek, Norcross, GA, USA). DNA integrity was checked by 1% agarose gel electrophoresis. Its concentration and purity were measured using a NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The extracted DNA was then used as the template for targeted amplification. For the bacterial community, primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) were used to amplify the V3-V4 hypervariable region of the 16S rRNA gene. For the fungal community, primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) were used to amplify the internal transcribed spacer (ITS) region. All PCR reactions were carried out in a 20 μL system containing 4 μL of 5× TransStart FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each forward and reverse primer (5 μM), 0.4 μL of TransStart FastPfu DNA polymerase, 2 μL ddH_2_O, and 10 μL of template DNA. The amplification conditions included an initial denaturation at 95 °C for 3 min, followed by 27 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s. A final extension was performed at 72 °C for 10 min. The PCR products were confirmed by 2% agarose gel electrophoresis and purified using a DNA gel extraction kit (Yuhua, Shanghai, China). The purified amplicons were quantified with a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) to ensure accurate measurement. Qualified PCR products from all samples were then pooled in equimolar concentrations and sent to Shanghai Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China) for high-throughput sequencing on the Illumina Nextseq2000 platform (Illumina, San Diego, CA, USA).

2.3. Metabolite Composition Analysis

Approximately 100 mg of each cocoon sample was weighed and transferred into a 2 mL centrifuge tube. Subsequently, 800 μL of a pre-cooled extraction solution (methanol: water = 4:1, v/v, containing internal standards such as L-2-chlorophenylalanine) was added. The mixture was homogenized for 6 min using a frozen tissue grinder (−10 °C, 50 Hz) (Wanbo, Shanghai, China). It then underwent low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). After the extract was left to stand at −20 °C for 30 min, it was centrifuged at 13,000× g for 15 min at 4 °C. The resulting supernatant was collected and used for subsequent analysis. Metabolite profiling was conducted using an Ultra-Performance Liquid Chromatography system coupled with an Orbitrap Exploris 240 mass spectrometer (UPLC-Orbitrap Exploris 240 MS, Thermo Fisher Scientific, Waltham, MA, USA). A quality control (QC) sample was prepared by mixing equal volumes of supernatant from all samples. This QC sample was injected after every 5 to 10 experimental samples to evaluate instrument stability. Chromatographic separation was performed on an HSS T3 column maintained at 40 °C with a flow rate of 0.40 mL/min. Mobile phase A consisted of water/acetonitrile (95:5, v/v) containing 0.1% formic acid, while mobile phase B comprised acetonitrile/isopropanol/water (47.5:47.5:5, v/v/v) containing 0.1% formic acid. Mass spectrometric detection used an electrospray ionization source. Data were collected in both positive and negative ionization modes across an m/z range of 70 to 1050. The ion source voltage was set to 3500 V in positive mode and −3000 V in negative mode, with an ion source temperature of 450 °C. Lastly, data processing and metabolite identification were completed.

2.4. Statistical Analysis

Based on the raw data from 16S rRNA and ITS gene sequencing, Alpha diversity was assessed by calculating the Sobs, Ace, Chao, Pielou_e, Faith_pd, Shannon, Simpson, and Coverage indices. Data are reported as Mean ± SD. Differences in Alpha diversity between the single and multiple-generation cocoon groups were tested using Student’s t-test. For Beta diversity, Bray-Curtis and UniFrac distance matrices were used. Principal Coordinates Analysis (PCoA) was employed to visualize differences among samples. Analysis of similarities (ANOSIM) was used to determine whether overall community structure differed significantly between groups, with exact P and R values provided. Linear Discriminant Analysis Effect Size (LEfSe) was performed to identify microbial taxa showing significant differential abundance. An LDA score greater than 2.0 and a p value below 0.05 were used as thresholds. Community stability was also assessed by calculating the average variation degree based on deviations from the mean relative abundance.

For the metabolomics dataset, an unsupervised Principal Component Analysis (PCA) was first conducted to examine natural clustering among samples. A supervised Orthogonal Partial Least Squares–Discriminant Analysis (OPLS-DA) model was then constructed, and cyclic cross-validation was used to assess model reliability and avoid overfitting. Differential metabolites were screened using the Variable Importance in Projection (VIP) score from the OPLS-DA model (VIP > 1.5) and the p value from Student’s t-test (p < 0.05). To investigate relationships between microbial taxa and metabolites, Spearman’s rank correlation analysis was performed. Correlation coefficients were calculated between significantly different genera and the differential metabolites, with p < 0.05 considered statistically significant.

3. Results

3.1. Changes in Microbial Diversity During Cocoon Accumulation

3.1.1. Changes in Bacterial Diversity

Analysis of the bacterial community in A. mellifera cocoons showed that its diversity and structure changed markedly with increasing cocoon accumulation generations. Compared with single-generation cocoons, multiple-generation cocoons exhibited a clear reduction in bacterial species richness. This decline was reflected in significantly lower Sobs, Ace, and Chao indices (Figure 1A–C). The decrease in the Pielou_e evenness index also indicated an uneven community dominated by a few over-proliferating species (Figure 1D). In addition, the significant reduction in the Faith_pd phylogenetic diversity index suggested that species loss occurred across a broad range of evolutionary lineages, thereby narrowing the phylogenetic breadth of the community (Figure 1E). The combined decrease in richness, evenness, and phylogenetic diversity led to an overall decline in community diversity, as demonstrated by the lower Shannon index and higher Simpson dominance index (Figure 1F,G). The coverage indices for all samples remained above 0.99, with no difference between groups. This confirmed that sequencing depth was sufficient and did not influence the results (Figure 1H).

The bacterial community in cocoons also exhibited a distinct succession pattern (Figure 2A). In single-generation cocoons, persistent core species served as pioneer taxa and dominated the relative abundance (98.33%). However, these species represented only 18.96% of the absolute abundance. In contrast, transient and intermediate species accounted for only 1.67% of relative abundance but formed 81.04% of absolute abundance, indicating an early community stage characterized by low biomass and high structural imbalance. When cocoons accumulated for eight generations, the community structure shifted substantially. The relative abundance of persistent core species decreased to 57.7%, but their absolute abundance increased to 39.79%, indicating an expansion of their population size. Intermediate species became a major group comparable to persistent core species. Their relative abundance increased sharply to 42.21%, and their absolute abundance reached 39.79%.

PCoA further confirmed a significant separation between the bacterial communities of single and multiple-generation cocoons (Figure 2B). The first two principal coordinates explained 82.53% of the total variance, and samples within each group clustered tightly. At the genus level, core genera included Gilliamella, Melissococcus, Bombella, Frischella, and Tyzzerella (Figure 2C). As cocoon accumulation increased, the relative abundances of several genera changed significantly. The abundances of Melissococcus, Bombella, and Lactobacillus increased in multiple-generation cocoons, while Gilliamella, Frischella, and Tyzzerella showed significant decreases (Figure 2D). Most of these genera are typical symbionts associated with the honey bee gut or nest environment. However, the strong enrichment of the genus Melissococcus in multiple-generation cocoons is noteworthy. Its representative species, M. plutonius, is the causative agent of European foulbrood (Figure 2E). These findings indicate that multiple-generation cocoon accumulation not only reshapes the overall microbial community but also creates a microenvironment favorable for the proliferation of potential pathogens.

3.1.2. Changes in Fungal Diversity

Alpha diversity analysis of the fungal community revealed patterns that differed from those of the bacterial community. The Sobs, Ace, and Chao indices, which describe species richness, showed no significant differences between single and multiple-generation cocoons. This indicated that the total number of fungal species remained relatively stable throughout cocoon accumulation (Figure 3A–C). However, the internal structure of the community changed noticeably. Multiple-generation cocoons showed a significant increase in the Shannon diversity index and the Pielou_e evenness index, along with a significant decrease in the Simpson dominance index. Together, these shifts demonstrated that species were more evenly distributed and that the dominance of a few species observed in early stages had diminished (Figure 3D–F). Although species numbers did not change, the significant decrease in the Faith_pd phylogenetic diversity index suggested that the species lost during accumulation were likely from more ancient evolutionary lineages. This resulted in a narrower phylogenetic diversity within the fungal community (Figure 3G). The high Coverage indices (>0.99) for all samples confirmed that sequencing depth was adequate and that the results were reliable (Figure 3H).

The fungal community also showed clear successional patterns (Figure 4A). In single-generation cocoons, persistent core species dominated the relative abundance (88.31%) but accounted for only 20.19% of the absolute abundance. In contrast, transient and intermediate species formed 79.81% of the absolute abundance, indicating a community with low stability and uneven biomass distribution. In multiple-generation cocoons, persistent core species still held a high relative abundance (84.12%). Their absolute abundance, however, increased sharply to 42.20%. The absolute abundance of intermediate species also rose to 39.88%. These two groups together formed the stable core of the mature fungal community.

PCoA showed a significant separation in fungal community structure between single and multiple-generation cocoons. The first two principal coordinates explained 71.98% of the total variance, confirming that cocoon accumulation strongly shaped fungal community composition (Figure 4B). At the genus level, dominant fungal taxa included Metschnikowia, Cladosporium, unclassified Teratosphaeriaceae, Knufia, and Wallemia (Figure 4C). As cocoon accumulation increased, the abundances of Cladosporium, Knufia, and Wallemia increased significantly, while the abundance of Metschnikowia decreased significantly (Figure 4D,E). Notably, the enrichment of the genus Wallemia in multiple-generation cocoons was particularly striking. Members of this genus can produce mycotoxins such as walleminone, which may pose health risks to organisms (Figure 4F). This result aligns with the findings from the bacterial diversity analysis and suggests that multiple-generation cocoon accumulation reshapes the microecological environment in a way that may promote the growth of microorganisms associated with potential risks to colony health.

3.2. Dynamics in Metabolite Composition During Cocoon Accumulation

3.2.1. Analysis of Cocoon Compositional Metabolites

To decipher the dynamic changes in cocoon chemistry, the metabolite profiles of single and multiple-generation cocoons were compared using untargeted metabolomics. The results showed that the chemical composition of cocoons changed significantly with increasing accumulation generations. Across all samples, single and multiple-generation cocoons shared 5488 metabolites (Figure 5A). These shared compounds represented the core chemical matrix of the cocoon and mainly included carboxylic acids and derivatives (16.57%), fatty acyls (12.95%), organooxygen compounds (11.25%), prenol lipids (7.83%), and benzene and substituted derivatives (5.14%) (Figure 5B).

More notable differences appeared in the metabolites that were unique to each group. Single-generation cocoons contained 187 unique metabolites, and these were mainly carboxylic acids and derivatives (16.44%) and organooxygen compounds (13.70%). They were also enriched in prenol lipids (8.22%), steroids and steroid derivatives (6.85%), flavonoids (6.85%), and fatty acyls (6.85%) (Figure 5C). In contrast, multiple-generation cocoons contained a much larger set of 332 unique metabolites. Carboxylic acids and derivatives remained the dominant class (18.75%). Importantly, the proportion of flavonoids (7.81%) was markedly higher than that in single-generation cocoons. Other major classes included organooxygen compounds (8.33%), prenol lipids (6.77%), fatty Acyls (6.25%), and benzene and substituted derivatives (5.21%) (Figure 5D). Collectively, these findings showed that cocoon metabolite composition underwent clear dynamic changes during accumulation. The core chemical matrix remained relatively stable, but the number, type, and proportions of unique metabolites shifted substantially as cocoon layers accumulated over generations.

3.2.2. Changes in Metabolite Composition During Cocoon Accumulation

Multivariate statistical analysis of the metabolomics data revealed clear and significant differences in metabolite composition between single and multiple-generation cocoons. Heatmap analysis showed good reproducibility and tight clustering of samples within each group (Figure 5E). A distinct separation pattern was also visible between groups, providing initial evidence that cocoons from different generations contained different metabolite profiles. This observation was further supported by PCA. The first two principal components (PC1 and PC2) explained 78.90% of the total variance, capturing the main structure of the dataset (Figure 5F). In the PCA score plot, samples from single and multiple-generation cocoons occupied clearly separated regions, confirming that cocoon metabolite composition changed substantially with increasing brood-rearing generations. This separation was further investigated using an OPLS-DA model, yielding a Q^2^ value of 0.988 and an R^2^Y value of 0.997, indicating a robust model with a low risk of overfitting. A volcano plot was used to identify metabolites with significant changes. A total of 685 metabolites differed significantly between the two groups, with 513 being upregulated and 172 downregulated in multiple-generation cocoons (Figure 5G). Multiple categories of agricultural and environmental chemicals were identified among the upregulated metabolites in multiple-generation cocoons (Figure 5H). The detected compounds comprised five herbicides, four plant growth regulators, three fungicides, and two insecticides. Their presence provides clear evidence of colony exposure to environmental pollutants, and their elevated levels in multi-generation cocoons imply potential negative consequences for bee development.

3.3. Microbe-Metabolite Interactions Associated with Cocoon Accumulation

Association analysis between endogenous metabolites and the in-situ microbial community within cocoons revealed a critical pattern. A significant co-occurrence relationship was observed between residual pesticides from the nest environment and a major larval pathogen. Specifically, the abundance of Melissococcus, the primary pathogen responsible for European foulbrood, showed a significant positive correlation with the concentrations of several agricultural and environmental chemicals detected in the cocoons (Figure 6A). These compounds included the fungicides (cycloheximide, kresoxim-methyl, and triadimenol), herbicides (lenacil, imazethapyr, and bialaphos), plant growth regulators (forchlorfenuron, gibberellin a3, paclobutrazol, and dichlorprop), and insecticide (nitenpyram).

Furthermore, the fungal genus Wallemia, which is known to produce mycotoxins, also exhibited significant positive correlations with multiple pesticides (Figure 6B). This indicates that within the progressively accumulating cocoon microenvironment, exogenous pollutants and microbial community shifts interact synergistically, particularly promoting the colonization and enrichment of potential pathogens. This combined pollutant–pathogen interaction model is likely a major mechanism through which old combs generate compound risks to colony health. While functioning as a reservoir for pollutants, the cocoon simultaneously forms a refuge for pathogens and an amplifier of pathogenic potential.

4. Discussion

Cocoon accumulation is a gradual enrichment process within brood cells that progresses over time across multiple bee generations [25]. It also expands in space through the continuous deposition of cocoon material [11]. This accumulation leads to notable changes in the physical and biochemical properties of the cocoon. The growing mass of silk enhances permeability and hygroscopicity [21,23,24]. Combined with a brood comb temperature close to 35 °C, this warm and humid condition becomes highly suitable for microbial colonization [29,30]. Furthermore, the concomitant accumulation of propolis and larval debris provides a nutrient-rich biological matrix that may promote the proliferation of specific microorganisms while potentially suppressing others, thereby significantly shaping the distinct microbial community composition observed in multiple-generation cocoons [3,20,28]. As cocoons accumulate from one to eight generations, the richness, evenness, and phylogenetic diversity of the internal bacterial communities shift substantially. The research findings indicate that during the early stage of cocoon formation, only a few highly adaptable and efficiently transmitted persistent core species successfully colonize the cocoon. As accumulation progresses, the internal cocoon environment becomes more complex. During this transition, persistent core species continue to increase in absolute abundance. In contrast, the fungal community showed greater structural stability throughout generational succession. Persistent core fungi consistently maintained dominance in relative abundance. Their substantial increase in proportional abundance, along with the coordinated rise in intermediate species, reflects a transition from an early fungal community characterized by high relative abundance but low absolute abundance to a more mature system where relative and absolute abundances converge.

In ecosystems, microbial communities with high abundance can suppress the excessive growth of other bacteria [31]. They achieve this through niche occupation, resource competition, and the secretion of antimicrobial substances [32]. In this study, the pathogen Melissococcus was significantly enriched in multiple-generation cocoons, while Gilliamella and Frischella declined in abundance. The bacterial community in single-generation cocoons closely matched that in adult worker guts [33]. This finding supports the idea that the nest environment is the primary source of gut microbial acquisition [34]. Harmful bacteria enriched in multiple-generation cocoons may also use this route for transmission. Melissococcus, a globally distributed pathogen harmful to honey bee colonies, has previously been detected on brood combs and identified as a major route of infection for bee larvae [26,35,36]. The marked enrichment of this bacterium in multiple-generation cocoons is more plausibly attributed to the cocoon’s specific role in promoting the long-term survival of the pathogen, thereby contributing to its pronounced accumulation in this niche. Therefore, multiple-generation cocoons act not only as a reservoir for pathogens but also as an incubator that promotes their active growth. The simplified microbial structure may reduce competition and create conditions that favor pathogen expansion. A similar trend occurred in the fungal community. The number of fungal species remained stable, but phylogenetic diversity decreased. The strong enrichment of the mycotoxin-producing fungus Wallemia suggests a possible shift toward pathogenic behavior [37]. Together, these results indicate that cocoon accumulation creates a microenvironment that increasingly favors potential pathogens.

The cocoon is rich in diverse chemical constituents, primarily including carboxylic acids and derivatives, fatty acyls, organooxygen compounds, prenol lipids, and benzene and substituted derivatives. Metabolomic evidence further supports the role of the cocoon as a pollutant concentrator. More than 20 pesticide compounds were up-regulated significantly in multiple-generation cocoons, including fungicides, herbicides, and insecticides. These chemicals reflect the complex environmental exposures experienced by colonies during foraging [4,38]. They also highlight the strong adsorption and enrichment capacity of the cocoon material [25]. Two specific chemical categories require attention. The first includes compounds with known toxicity to bees, such as the neonicotinoid nitenpyram. In a 14-day chronic toxicity exposure study in honeybees, nitenpyram reduced survival and food consumption, significantly altered the relative abundance of several key gut microbiota, and induced gut dysbiosis, thereby affecting metabolic homeostasis and immunity [39]. Even at low doses, these substances can cause neurotoxicity, deformities, and immune suppression. Likewise, exposure to the triazinone insecticide pymetrozine inhibited related immune-detoxification enzyme activities and significantly decreased survival in bees [40]. The second category involves flavonoids, which increased significantly in multiple-generation cocoons. Flavonoids are common in nectar and pollen and have antimicrobial and antioxidant functions [41]. Their enrichment may reflect residual food materials. It may also indicate an attempt by the colony to use plant secondary metabolites to limit pathogen growth inside cocoons. Overall, the dynamic changes in the cocoon metabolome reflect both external pollutant pressure and internal biochemical responses. These patterns may represent chemical signatures of the self-protection strategies of the colony. However, further in-depth investigations are required to elucidate the specific effects of these compounds on honeybee colony health and adaptive mechanisms.

Correlation analysis revealed a significant positive relationship between pesticide residues and pathogen abundance. This co-occurrence pattern indicates a possible interaction mechanism between pollutants and pathogens. Honey bee larvae may remain exposed to low-dose mixtures of pesticides that slowly diffuse from cocoon layers during development. This continuous exposure may weaken larval immune systems and increase vulnerability to infections [42,43]. An immunocompromised host creates favorable conditions for pathogen invasion and colonization [44]. Thus, sublethal pesticide exposure combined with pathogen presence inside multiple-generation cocoons may generate additive negative effects [4,45]. This mechanism may also contribute to colony decline in situations where no single dominant cause is identifiable.

In summary, this study reframes the role of multiple-generation cocoons from a simple cause of reduced brood cell volume to an amplifier of pollutant–pathogen interactions [11]. These findings highlight a clear management implication. Regular replacement of old combs should be regarded as an essential apicultural practice [18]. This step is important for maintaining brood rearing space and, more importantly, for removing long-term sources of pathogen buildup and chronic pollutant exposure. The number of brood-rearing generations completed on a comb should therefore be included in colony health risk assessments. This study also provides a theoretical basis for creating evidence-based comb renewal schedules. Such guidelines can support sustainable beekeeping and help reduce colony losses. However, several limitations remain. Contaminants identified through metabolomics relied on database matching, and future targeted mass spectrometry will be required for precise verification. Geographical variation, seasonal conditions during the study period (such as source difference of nectar and pollen), and agricultural practices may influence the chemical and microbial composition of cocoons. Validation across broader spatial and temporal scales is therefore necessary.

5. Conclusions

By integrating microbiome and metabolomics analyses, this study revealed clear successional patterns in the microecosystem and chemical composition during multiple-generation cocoon accumulation in A. mellifera combs. As brood rearing generations increased, bacterial community diversity within cocoons declined significantly, and potential pathogens such as Melissococcus and the mycotoxin-producing fungus Wallemia became markedly enriched. Metabolomic analysis confirmed that multiple-generation cocoons contained various exogenous pesticides, including insecticides and fungicides. A strong positive correlation between pesticide residues and pathogen abundance indicated a synergistic pollutant–pathogen risk mechanism. Thus, cocoon accumulation in old combs represents a combined process of microbial imbalance and increasing chemical load, both of which intensify colony risks. While limited by a small sample size, spatial, and seasonal sampling, our findings underscore the need for further research into how chemical loads may biophysically alter cocoon integrity. These findings reposition the cocoon from a passive structural residue to an active ecological reservoir that intensifies microecological imbalance and chemical load. This study provides a critical foundation for risk assessment of comb age and opens new avenues for investigating host–microbe–pollutant interactions in social insects, offering a theoretical foundation and practical guidance for improving colony health management and establishing standardized comb renewal strategies.