Gut Microbiome Diversity in European Honeybees (Apis mellifera L.) from La Union, Northern Luzon, Philippines

Abstract

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.

Keywords: Illumina MiSeq; Philippines; bacterial microbiota; fungal microbiota; honeybee functional microbiota; insects; metagenomics; next-generation sequencing.

Figure 1

World map showing the global distribution of published articles on the gut microbiome of Apis mellifera from 2011 to 2024. Data were extracted from Scopus, and the map was generated using R version 4.3.3 with the packages ggplot2, rnaturalearth, rnaturalearthdata, sf, and dplyr. The number of articles is indicated by color intensity: high (dark orange), low (light orange), and no data (gray).

Figure 2

World map showing the global distribution of published articles on pathogens and/or diseases in Apis mellifera from 2011 to 2024. Data were extracted from Scopus, and the map was generated using R version 4.3.3 with the packages ggplot2, rnaturalearth, rnaturalearthdata, sf, and dplyr. The number of articles is indicated by color intensity: high (dark green), low (light green), and no data (gray).

Figure 3

Apiary location in Northern Luzon, located at the Municipality of La Union: Barangay Santa Cruz and Barangay Sapilang.

Figure 4

Taxonomic class and phyla associated in Apis mellifera (A,B). The gut bacteriome (C) and gut mycobiome (D) of Apis mellifera from the two locations from the Municipality of La Union, Philippines: Barangay Santa Cruz (SC1–SC4) and Barangay Sapilang (SP1–SP4). Percentage of relative abundance was shown. Features were clustered and organized (colored taxa bar plots) based on genus. Abundance with <0.1% is clustered into “Others”.

Figure 5

Alpha diversity plots. Boxplot corresponding to Shannon diversity and Simpson diversity index. Taxonomic, bacterial community, and fungal community have no significant evidence (p < 0.05, Mann–Whitney U Test). Mean data correspond to the red polygon point, and the standard error is represented as the asterisk, and black dots represent individual data points.

Figure 6

Heatmap visualization for significantly differential functional metabolites enzymes identified in the gut microbiome of Apis mellifera. Colors indicate the detection level of functional metabolite enzymes, ranging from highest (light pink, red) to lowest (dark purple): pink-red indicates a relatively higher level, dark purple indicates a relatively lower level, and light green indicates that the relative level of metabolites falls between high and low.

Figure 7

The network analysis of bacterial and fungal communities reveals several correlations. For the bacterial community (A), there is a negative correlation between Commensalibacter and Apibacter, Gilliamella and Lactococcus, and Gilliamella and Lactiplantibacillus. A positive correlation was observed between Snodgrassella and Escherichia–Shigella and Bombilactobacillus and Apibacter. For the fungal community (B), Zygosaccharomyces shows a negative correlation with Coprinopsis and Pseudozyma. The degree of correlation was evaluated using Spearman’s correlation (p < 0.05) and represented by the color of the edges, ranging from positive (red) to negative (green), and by the size of the nodes, indicating the strength of the correlation from strong (yellow) to weak (blue).