Application of next-generation sequencing (NGS) for disease diagnosis in the beehive

Introduction

Traditional methods for monitoring hive health, particularly brood diseases, rely primarily on manual inspections during which beekeepers visually evaluate brood patterns for irregularities such as perforated capped cells indicative of foulbrood or chalky mummies associated with chalkbrood disease. A major concern in this context is the rise of multidrug-resistant bacteria, often linked to antibiotic residues in animal-derived food products (Arsène et al., 2022; Xu et al., 2022). Consequently, many countries prohibit antibiotic use in beehives for managing European foulbrood (EFB) or American foulbrood (AFB).

Complementary techniques include alcohol washes or powdered sugar shakes to assess Varroa mite infestations, modified hanging drop microscopy, Holst milk test, black light test, matchstick test for American foulbrood (AFB), and lateral flow devices where available. Although these methods are accessible and cost-effective, they also have important limitations. Furthermore, these approaches usually target single threats and may overlook synergistic interactions among multiple pathogens that contribute to colony decline. Honey bees represent an emerging area within exotic pet medicine, where veterinarians are increasingly involved in health management and differential diagnosis of diseases in managed colonies. As an NGS-based approach, 16S rRNA gene amplicon sequencing has emerged as a useful complementary tool (Elbehiry et al., 2025; Jia et al., 2021; Stefan et al., 2022; Zhao et al., 2023). Recent work has highlighted its utility in detecting shifts in microbial composition associated with brood diseases and Varroosis. The turnaround time from sample receipt to reporting is often 2–3 days, which can be faster than culture and susceptibility testing, especially for fastidious bacteria such as Melissococcus plutonius.

Monitoring hive health with NGS on honey bee larvae

16S rRNA gene sequencing can be used to profile bacterial communities in honey bee larvae and detect brood-associated shifts in microbial composition. Honey bee larvae are primary targets of brood pathogens such as Paenibacillus larvae and Melissococcus plutonius. Early detection of M. plutonius before overt clinical signs emerge is therefore particularly valuable, because access to antibiotics for EFB treatment requires veterinary oversight.

All cases presented in this report were submitted after the beekeeper had made a presumptive field diagnosis of EFB and requested antibiotic treatment. However, three of the four cases demonstrated detection of Melissococcus plutonius, indicating that EFB was present in most cases. These findings emphasize the value of molecular confirmation while highlighting that interpretation must be integrated with colony history, clinical findings, queen status, Varroa mite burden, mite-management history, colony strength, and other husbandry factors.

This manuscript focuses specifically on bacterial detection using 16S rRNA gene amplicon sequencing for clinical support in brood disease investigations.

Monitoring hive health with NGS on honey bee larvae

16S rRNA gene amplicon sequencing can be used to profile bacterial communities in honey bee larvae and to detect brood-associated shifts in microbial composition. Honey bee larvae are highly relevant sentinels for hive health because they are particularly susceptible to diseases such as AFB (caused by the bacterium Paenibacillus larvae) and EFB (caused by the bacterium Melissococcus plutonius). Larvae nourished by nurse bees with potentially contaminated royal jelly or brood food may accumulate bacterial pathogens, and M. plutonius is a fastidious organism that proliferates primarily in the midgut of developing larvae before larval death. After death, it persists at low levels while secondary invaders such as Paenibacillus spp. and Enterococcus spp. may become more prominent. Thus, early detection of M. plutonius, before clinical signs emerge, is critical. 16S rRNA gene amplicon sequencing of larval samples can therefore reveal shifts in bacterial community structure.

EFB outbreaks manifest with non-specific signs that weaken colonies substantially. Without precise diagnosis, beekeepers may resort to indiscriminate antibiotic use, potentially exacerbating issues (Allen NO, et al.; Li et al., 2017).

16S rRNA sequencing provides molecular confirmation of bacterial composition and can assist in distinguishing incidental from primary microbes. However, because most 16S rRNA data represent relative abundances and do not establish pathogenicity on their own, these interpretations must be made cautiously and require careful correlation with clinical signs and colony history.

The microbiome of bee larvae

Metagenomic analysis has demonstrated disease-associated shifts in the larval microbiome in multiple studies (Anderson et al., 2023; Daisley and Reid, 2021; Lewkowski and Erler, 2019). Published work consistently shows that EFB is associated with increased relative abundance of M. plutonius together with dynamic secondary invaders, including Enterococcus spp. and Paenibacillus spp., with community composition varying by disease stage and sample type. Thus, 16S rRNA sequencing is best interpreted as a tool for characterizing bacterial community structure and supporting clinical diagnosis when integrated with colony history, sample quality, and physical examination findings.

Clinical examples from the field

All samples were collected using the standardized commercial sampling kit provided by the diagnostic laboratory performing the analysis. Larvae were first visually assessed for clinical abnormalities, including dehydration, abnormal coloration, and atypical positioning within brood cells. Sampling focused on larvae estimated to be 3–6 days of age based on developmental appearance, as this stage is most relevant for evaluation of brood-associated bacterial disease. During collection, 2–3 larvae were intentionally disrupted to ensure adequate release of internal gastrointestinal contents and associated microbial DNA. Samples were immediately transferred into the proprietary transport buffer supplied with the kit and shipped to the laboratory on the same day.

Unlike traditional culture-based diagnostics, which require preservation of viable organisms throughout collection, transport, and laboratory processing, the transport buffer used in this system rapidly inactivates and stabilizes the microbial community at the time of sampling. This minimizes artifacts associated with delayed transport or suboptimal storage conditions and preserves the original microbial composition of the sample for downstream molecular analysis. As a result, this methodology offers a significant logistical and diagnostic advantage over conventional culture and sensitivity testing, particularly for fastidious organisms such as M. plutonius. In addition, the targeted next-generation sequencing platform used in this study enables simultaneous detection of bacterial and fungal organisms from the same sample, eliminating the need for separate submissions and specialized mycology diagnostics that would typically be required using traditional laboratory approaches.

Because these samples were collected from a variety of hives over an extended period, other key information—such as queen status, mite counts (or mite management history), cluster size, and overall colony size—was not recorded at the time of microbiological sampling.

Case 1

A strong colony without overt clinical issues was monitored for M. plutonius and other bacteria. In a sample collected from the brood nest in February and subjected to 16S rRNA sequencing, no M. plutonius was detected (Figure 1). Three months later, a follow-up sample revealed low levels (Figure 2). Because no clinical signs were present, antibiotic treatment was withheld. By August, post-nectar flow in Georgia, the hive displayed a characteristic ‘shotgun’ brood pattern (patchy brood with many empty cells resembling scattered shot holes). Resampling showed a marked increase in M. plutonius abundance (Figure 3). A probiotic treatment was then administered, and a one-month follow-up sample indicated a reduction in M. plutonius detection (Figure 4), coinciding with hive improvement. Notably, EFB symptoms are strongly seasonal and often resolve later in the season regardless of intervention (Fowler et al., 2025); therefore, controlled studies would be required to attribute the observed improvement specifically to probiotic treatment.

Here, 16S rRNA sequencing enabled longitudinal monitoring of bacterial abundance.

Case 2

A routine inspection revealed an abnormal brood pattern resembling EFB (Figure 5). 16S rRNA sequencing did not detect bacterial pathogens consistent with EFB (Figure 6), ruling out the initial suspicion. Subsequent separate viral screening identified sacbrood virus, and closer examination confirmed typical sacbrood larval features (Figure 7). This finding emphasizes the value of 16S rRNA sequencing in redirecting diagnosis away from antibiotic-based interventions when the clinical picture is suggestive but not specific for bacterial etiology. Regrettably, the colony later succumbed to the infection. Mite management data were not available; parasitic mite syndrome should be considered in differential diagnosis.

Case 3

A hive with an abnormal brood pattern was assessed for suspected EFB (Figure 8). 16S rRNA sequencing detected Melissococcus plutonius together with substantial Enterococcus faecalis abundance (Figure 9). The findings are most appropriately interpreted as EFB with associated secondary bacterial overgrowth. Enterococcus faecalis is considered a secondary invader and the results do not demonstrate that it was the primary cause of disease.

This case documents significant detection of E. faecalis in association with EFB-like signs and concurrent M. plutonius detection. No causal role for E. faecalis can be inferred from these data alone. Figure 8 also demonstrates brood abnormalities that may be consistent with queen failure or laying-worker activity, emphasizing the importance of considering alternative explanations for colony decline. Unfortunately, the hive perished weeks after sampling.

Case 4

Similar to Case 3, a hive with an abnormal brood pattern was evaluated for EFB suspicion. 16S rRNA metagenomics identified Paenibacillus dendritiformis together with M. plutonius in a co-infection.” (Figure 10). P. dendritiformis is a common environmental microbe previously reported in the U.S. (e.g., in clay) but, to the author’s knowledge, this is the first documented instance in honey bee colonies/larvae; prior literature was limited to Switzerland (Ory et al., 2025; Dainat et al., 2021). Probiotic powder treatment was followed by a marked reduction in both detections in follow-up 16S tests. Ory et al. (2025) noted uncertainty about this bacterium’s pathogenicity (Ory et al., 2025). This case illustrates colony survival without the use of antibiotics.

Comparison of traditional culture-based diagnostics vs. NGS sequencing for honey bee brood pathogens

Traditional culture-and-sensitivity approaches for honey bee brood pathogens have long been limited by the biological characteristics of the causative agents. M. plutonius, the primary etiologic agent of EFB, is a fastidious organism that requires highly specific anaerobic conditions, precise sodium/potassium ratios, and proliferates almost exclusively within the midgut of living larvae; after larval death it rapidly declines to very low levels while being overgrown by less fastidious secondary invaders such as Paenibacillus and Enterococcus species that tolerate oxygen and readily grow under standard laboratory conditions (Lewkowski and Erler, 2019, Takamatsu, 2023). Consequently, culture methods selectively recover only those organisms capable of surviving collection, transport, and artificial media, frequently leading to overgrowth by opportunistic secondary bacteria and under-recovery—or complete failure to isolate—the true primary pathogen.

In contrast, the 16S rRNA sequencing approach used in these cases relies on tubes containing a proprietary preservative that stabilizes the microbial community at the time of collection. This preserves the in-hive composition of the sample and minimizes the selective biases introduced by culture-based methods. By eliminating the need for organism viability during transport and laboratory growth, NGS circumvents the well-documented difficulties of culturing fastidious honey bee pathogens and offers a more accurate, culture-independent representation of the brood microbiome— supporting veterinarians to confirm a visual diagnosis of EFB and to decide on antibiotic therapy.

Limitations of 16S rRNA sequencings for diagnostic applications in honey bee brood diseases

Although 16S rRNA gene sequencing, as implemented in the commercial service used in this study, provides valuable insights into bacterial communities in beehive samples, several limitations must be considered for diagnostic applications. The method primarily reports relative abundance (taxonomic composition as percentages) rather than absolute pathogen load; although semi-quantitative estimates may be provided, these should be interpreted cautiously due to potential biases related to PCR amplification, DNA extraction efficiency, and total microbial biomass. In addition, bacterial composition can vary substantially between individual larvae and may rapidly change following larval death, making proper sample handling and cold-chain maintenance essential. The platform employed here uses a multiplexed targeted NGS approach, combining 16S rRNA gene sequencing for bacteria and ITS sequencing for fungi. As with any DNA-based method, NGS cannot distinguish viable from non-viable organisms and does not establish pathogenicity; therefore, results must be interpreted in conjunction with clinical signs, colony history, and conventional diagnostic methods. Practical constraints, including high costs, extended turnaround times compared to rapid assays, and the need for bioinformatics expertise, currently limit routine diagnostic use. Consequently, NGS based on 16S rRNA sequencing is best regarded as a complementary diagnostic adjunct, to be interpreted together with clinical history, colony inspection, and validated conventional methods. Future efforts should focus on standardization, validation against gold-standard techniques, and the development of clinically actionable interpretive frameworks to maximize its value in honey bee health management.

Conclusion

16S rRNA gene amplicon sequencing is best viewed as a complementary diagnostic tool for honey bee brood diseases rather than a replacement for conventional diagnostics. Its principal value lies in characterization of bacterial communities, confirmation of suspected bacterial brood diseases, and exclusion of bacterial etiologies in cases that may mimic EFB. Interpretation must be integrated with colony-level information including queen status, colony strength, Varroa mite counts, mite-management history, seasonality, and clinical examination findings. Importantly, detection of secondary bacteria such as Enterococcus spp. should not be interpreted as evidence of causation without additional supporting data. Future work should focus on standardization, validation against established diagnostic methods, and development of clinically actionable interpretation frameworks.

Custom bioinformatics pipelines increase accessibility—though costs and skills remain barriers. Future work should focus on standardization, validation against gold-standard methods, and controlled studies of interventions.

16S rRNA sequencing can serve as a useful adjunctive tool for veterinarians diagnosing honey bee brood diseases and supporting evidence-based management to improve honey bee health.

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The author(s) declared that generative AI was used in the creation of this manuscript. The author(s) declared that generative AI was used in the creation of this manuscript. AI was used to edit for readability as English is not my first language.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frbee.2026.1862765/full#supplementary-material.

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Footnotes

Edited by: Franco Mutinelli, Experimental Zooprophylactic Institute of the Venezie (IZSVe), Italy

Reviewed by: Fulvio Bordin, Experimental Zooprophylactic Institute of the Venezie (IZSVe), Italy

Peter Daniel Fowler, Michigan State University, MI, United States