Chainarong Sinpoo

Honey bees provide essential pollination services in natural and agricultural ecosystems, yet little is known about honey bee-associated pathogens in Cambodia and Laos. Here, we screened 53 colonies from 10 sites, including Apis cerana (n = 28), A. mellifera (n = 18), A. dorsata (n = 4), and A. florea (n = 3), for common honey bee viruses and the microsporidian Nosema ceranae using PCR and RT-PCR assays. Six viruses—acute bee paralysis virus, black queen cell virus, deformed wing virus type A, Israeli acute paralysis virus, Kashmir bee virus, and Lake Sinai virus—and N. ceranae were detected in A. cerana and A. mellifera, with overall low detection frequencies across sites. No targets were detected in A. dorsata or A. florea; however, the limited sample sizes for these species restrict interpretation of absence. Partial gene fragment sequences showed similarity to previously reported Asian isolates, indicating consistency with known regional viral diversity. These findings provide baseline detection data for honey bee-associated pathogens in Cambodia and Laos and support the need for expanded sampling across host species and seasons to better characterize pathogen occurrence in the region.

Mead quality is shaped by both fermentation and subsequent bottle aging, but stage-resolved descriptions that follow one production batch from honey must through fermented mead to aged mead remain scarce, particularly for region-specific single-honey products. Here, mead produced from Tithonia diversifolia honey from Southern Shan State, Myanmar, was characterised at three processing stages: pre-fermentation must (Pre), post-fermentation mead (Post), and mead bottle-aged for 1 year under ambient conditions at 24–28 °C (Aged). Physicochemical parameters were measured in three biologically independent production replicates and analysed statistically. Volatile and non-volatile profiling was performed on a single composite sample per stage, prepared by combining the three replicates, and is therefore descriptive: it characterises one production batch and does not support statistical comparison between stages. Fermentation reduced glucose and fructose from 71.9 ± 0.2 and 86.6 ± 0.8 mg/mL to below the limit of quantification, and both remained below it after aging. Mead pH rose slightly during fermentation (3.80 ± 0.07 to 3.88 ± 0.05) and fell during storage (3.67 ± 0.05), while colour shifted towards the lighter end of the Pfund scale rather than browning. Semi-quantitative electronic-nose analysis detected 60 putative volatile candidates across the sequence. The pre-fermentation profile was the least similar to the others (Pre versus Post, r = 0.16; Pre versus Aged, r = 0.04, against r = 0.38 between Post and Aged), and the post-fermentation profile was dominated by alcohol- and sulfur-associated responses. Twenty-nine candidates were detected only after fermentation, and 17 were shared between the fermented and aged mead, while the aged sample additionally contained candidates, including pentanol, 2-methylpropanoic acid, propyl acetate, 2-methylbutanal, thiophene, and terpinen-4-ol. Untargeted UHPLC-QTOF/MS resolved 10 non-volatile features that differed in detection across the three stages; these were putatively annotated by accurate mass, isotopic fidelity, and MS/MS library matching, with peptides and peptide derivatives forming the largest group. Because no authentic standards were analysed, these annotations remain tentative. Because the profiling data derive from one batch of one monofloral honey fermented with one yeast strain and were not confirmed by orthogonal or sensory methods, the compounds reported here define a candidate feature list for targeted validation in T. diversifolia mead rather than established markers of mead maturation.

Paraquat is a widely used herbicide known to adversely affect honeybee physiology; however, its impact on gut-associated biological processes remains poorly understood. Therefore, intricate effects of paraquat on honeybees highlight the need for a comprehensive understanding of its specific biological consequences. This study aimed to evaluate the gut microbiome composition and metabolomic profiles of honeybees following oral exposure to sublethal (LD₂₅) and toxic (LD₅₀) doses of paraquat, assessed 48 h post-exposure to represent acute exposure. Survival rate and sucrose consumption were monitored to assess toxicity. Honeybee gut samples were analyzed using 16S rRNA gene amplicon sequencing and metabolomic approaches. Both paraquat treatments resulted in increased mortality (p < 0.0001) and reduced sucrose consumption (p = 0.00503). In contrast, gut bacterial community composition remained largely unchanged (p > 0.05). Metabolomic analysis revealed dose-dependent alterations, particularly in metabolites associated with carbohydrate and energy metabolism, including oxaloacetic acid and pyruvic acid. This study provides the first integration of 16S rRNA gene amplicon sequencing and metabolomic analyses to evaluate the effects of paraquat exposure in honeybees. The findings indicate that paraquat-induced stress primarily exhibits as host metabolic reprogramming rather than changes in microbiome composition. These results provide insights into the mechanisms underlying honeybee responses to herbicide stress and may contribute to the development of strategies for honeybee protection.

Honey adulteration has long been a nuisance in local and international trade. Sugar syrup addition and false labeling of botanical origin have created a challenge in identifying fraudulent honey supplies and products. Stable carbon isotope ratio analysis (SCIRA) has been widely employed in honey authentication. While it is effective in identifying the addition of C4 plant-derived sugars, it does not provide information related to honey’s botanical source. This research investigated the combination of SCIRA and melissopalynology to provide a more robust assessment of honey integrity and showed that PCA analysis of δ¹³C together with sugar profiles could further improve the decision involving addition of sugar syrups. A total of 34 beekeeper honey samples were analyzed from 7 provinces of Thailand with a focus on longan honey. Twenty-four samples passed the δ¹³C criteria, exhibiting δ¹³C of bulk honey ranging from −28.53 ± 0.19‰ to −22.89 ± 0.08‰ and δ¹³C of extracted protein ranging from −29.30 ± 0.07‰ to −22.76 ± 0.03‰. Pollen profiling further eliminated honey of questionable and multifloral origins, yielding only eight samples that passed both criteria of being monofloral and not being adulterated with C4-derived sugars. These included six samples of longan honey and two honey samples of other botanical origins, yielding an overall passing rate of 23.5%. Our study showed that by combining SCIRA and melissopalynology, a robust determination of honey integrity could be achieved.

Traditional methods for assessing honey storage in beehives predominantly rely on manual visual inspection, which often leads to inconsistencies and inefficiencies. This study presents an automated deep learning approach utilizing the YOLOv11 model to detect, classify, and quantify honey cells within Apis mellifera frames across monthly sampling periods. The model’s performance varied depending on image resolution and dataset partitioning. Using the free version of YOLOv11 with high-resolution images (960 × 960 resolution) and a dataset split of 90:5:5 for training, validating, and testing, the model achieved a mean average precision at IoU threshold of 0.5 (mAP@0.5) of 83.4% for uncapped honey cells and 80.5% for capped honey cells. A strong correlation (r = 0.94) was observed between the 90:5:5 and 80:10:10 dataset splits, indicating that increasing the volume of training data enhances classification accuracy. In parallel, the study investigated the relationship between the physical properties of honey and image-based honey storage detection. Of the four tested properties, electrical conductivity (R² = 0.19) and color (R² = 0.21) showed weak predictive power for honey storage area estimation, with even weaker associations found for pH and moisture content. The honey storage areas via 90:5:5 and 80:10:10 datasets moderately correlated (r = 0.44–0.46) with increasing electrical conductivity and color. Especially, electrical conductivity exhibited statistically significant correlations with dataset performance across different dataset splits (p < 0.05), suggesting some potential influence of chemical composition on model accuracy. Our findings demonstrate the viability of image-based honey classification as a reliable technique for monitoring beehive productivity. Additionally, the research on image-based honey detection can be a non-invasive solution for improved honey production, beehive productivity, and optimized beekeeping practices.

Insects often rely on symbiotic bacteria and fungi for various physiological processes, developmental stages, and defenses against parasites and diseases. Despite their significance, the associations between bacterial and fungal symbionts in Apis mellifera are not well studied, particularly in the Philippines. In this study, we collected A. mellifera from two different sites in the Municipality of Bacnotan, La Union, Philippines. A gut microbiome analysis was conducted using next-generation sequencing with the Illumina MiSeq platform. Bacterial and fungal community compositions were assessed using 16S rRNA and ITS gene sequences, respectively. Our findings confirm that adult worker bees of A. mellifera from the two locations possess distinct but comparably proportioned bacterial and fungal microbiomes. Key bacterial symbionts, including Lactobacillus, Bombilactobacillus, Bifidobacterium, Gilliamella, Snodgrassella, and Frischella, were identified. The fungal community was dominated by the yeasts Zygosaccharomyces and Priceomyces. Using the ENZYME nomenclature database and PICRUSt2 software version 2.5.2, a predicted functional enzyme analysis revealed the presence of β-glucosidase, catalase, glucose-6-phosphate dehydrogenase, glutathione transferase, and superoxide dismutase, which are involved in host defense, carbohydrate metabolism, and energy support. Additionally, we identified notable bacterial enzymes, including acetyl-CoA carboxylase and AMPs nucleosidase. Interestingly, the key bee symbionts were observed to have a negative correlation with other microbiota. These results provide a detailed characterization of the gut microbiota associated with A. mellifera in the Philippines and lay a foundation for further metagenomic studies of microbiomes in native or indigenous bee species in the region.

The four honeybee species native to Cambodia—Apis dorsata, Apis cerana, Apis florea, and Apis andreniformis—play a vital role in ecosystem health and agricultural productivity through their pollination activities. Beekeeping in Cambodia has primarily developed around the introduced species Apis mellifera. However, it remains underdeveloped compared to neighboring countries, with wild honey collection continuing to play a significant role. Although native bees are not at immediate risk of extinction, their crucial ecological and socio-economic roles, coupled with the threats to their populations, necessitate the urgent implementation of conservation policies. The beekeeping sector in Cambodia faces numerous challenges that hinder its development. These include a lack of documentation and official data, limited access to productive apiaries, inappropriate pesticide practices, insufficient knowledge about bee health, diseases, and parasites, inadequate organization and representation of beekeepers, the high cost of Cambodian honey, and a lack of consumer awareness and trust in local bee products. This paper provides an overview of the status and distribution of honeybee species, along with the production, value chain, and trade of bee products in Cambodia. It examines the challenges of conserving native honeybees and developing the beekeeping sector while proposing strategies to strengthen bee conservation and support the growth of beekeeping in the country. Key recommendations focus on integrating bee conservation strategies with broader conservation goals, such as reducing deforestation, promoting sustainable agriculture, and regulating the consumption of wild bee brood. Key strategies for the development of beekeeping are documenting and conserving bee floral resources and habitats, reducing the use of bee-toxic pesticides, developing research and training capacities in beekeeping, regulating bee imports, and enhancing quality and authenticity testing facilities. Additionally, building capacity among honey value chain stakeholders in areas such as quality control, authenticity, processing, packaging, and marketing is essential.

Nosema ceranae infects midgut epithelial cells of the Apis species and has jumped from its original host A. cerana to A. mellifera worldwide, raising questions about the response of the new host. We compared the responses of these two species to N. ceranae isolates from A. cerana, A. mellifera from Thailand and A. mellifera from France. Proteomics and transcriptomics results were combined to better understand the impact on the immunity of the two species. This is the first combination of omics analyses to evaluate the impact of N. ceranae spores from different origins and provides new insights into the differential immune responses in honeybees inoculated with N. ceranae from original A. cerana. No difference in the antimicrobial peptides (AMPs) was observed in A. mellifera, whereas these peptides were altered in A. cerana compared to controls. Inoculation of A. mellifera or A. cerana with N. ceranae upregulated AMP genes and cellular-mediated immune genes but did not significantly alter apoptosis-related gene expression. A. cerana showed a stronger immune response than A. mellifera after inoculation with different N. ceranae isolates. N. ceranae from A. cerana had a strong negative impact on the health of A. mellifera and A. cerana compared to other Nosema isolates.