Genome-Resolved Comparative Mining of Biosynthetic Gene Clusters in Global Stingless Bee Honey Metagenomes

1. Overview

Honeys produced by stingless bees of the tribe Meliponini are widely recognized for its medicinal properties^1,2. These bees are cultivated by Indigenous communities in Latin America and hold deep biocultural significance^3,4. In 2025, we participated in identifying “Meliponilactobacillus,” a bacterial genus closely related to Acetilactobacillus that is consistently present in the honey microbiome of Meliponini worldwide⁵. Our goal is to understand the ecological role of this genus within the microbial community and its interactions with its stingless bee hosts.

We propose to investigate whether Meliponilactobacillus exhibits conserved specialized metabolic genomic features encoding antibiotic-related metabolites, the bioactivity of these metabolites, and their interactions with the bee brain. Inoculation analyses of microorganisms in bees help to elucidate the functionality of symbiotic relationships between these microbes and their hosts. Furthermore, using bees as a model provides a simpler system for studying the microbiota–gut–brain axis. Unlike vertebrates, whose complex gut microbiota makes it difficult to distinguish bacterial metabolites from host-derived metabolites, insects harbor simpler microbial communities, making them valuable models for investigating the evolutionary mechanisms through which microbes influence brain metabolism^6,7.

We have direct access to meliponaries in Quintana Roo (Mexico) and Costa Rica, where honey is sustainably harvested. The meliponaries have formally endorsed our research goals. Laboratory work will be conducted at its Molecular Biology Laboratory, while computational analyses will be performed at the Computational Biology Laboratory of the Centro de Ciencias Matemáticas, which hosts two high-capacity servers (750 GB RAM each).

Once we receive the funding, since we do have isolates and samples, we estimate 4 months will be required to generate the metagenomics and metabolic data and antibiogram assays, and an additional 2 months for analysis. We anticipate completing the project within one year. The approximate timeline is as follows: 1-3 months of metagenomic sequencing and metabolic extraction, 1 month for antibiograms, 1 month for bioinformatic analysis, 1 month for writing.

All sequencing data will be deposited in the NCBI Sequence Read Archive (SRA), and all analysis scripts and processed results will be made publicly available through appropriate repositories (GitHub/Zenodo).

2. Introduction

Our previous analyses of 17 mature pot-honey samples from two mexican native stingless bee species, Scaptotrigona mexicana and Melipona beecheii, consistently detected a dominant Lactobacillus signal and yielded multiple MAGs representing an Acetilactobacillus-like lineage⁵. These MAGs shared ≤80% ANI with public Acetilactobacillus genomes, indicating previously uncharacterized diversity and supporting a novel taxon we provisionally designate Meliponilactobacillus. We also obtained six isolates from the same material, most of which clustered closely with our MAGs. Given their prevalence and abundance, these bacteria may contribute to shaping the honey microbiome and to its bioactive properties, including reported antioxidant and antimicrobial activities.

Based on these findings, we now aim to investigate whether the Meliponilactobacillus species complex is abundant in meliponini honey worldwide and its functional role in stingless bees. Previous studies in Apis and Bombus have shown that specific microorganisms can modulate metabolite abundance and gene expression in the brain, but equivalent research is still lacking for stingless bees. This absence is largely due to the previous unavailability of axenic models, which are essential for testing microbial colonization under controlled conditions. Preliminary experiments conducted at the Laboratory of Microbial Ecology and Genetics (Universidad de Costa Rica) and at CINAT have demonstrated the feasibility of rearing axenic stingless bees. As a next step, we aim to initiate controlled inoculation experiments with selected microorganisms, such as Meliponilactobacillus, followed by brain metabolomics analyses, to explore the possible mechanism underlying this symbiosis.

Carrying out inoculation with Melliponilactobacillus is of particular interest, since members of Lactobacillus have been shown to influence brain metabolite abundance and gene expression associated with behaviour in the other two social groups of corbiculate bees, honeybees and bumblebees^8,9. We also aim to investigate whether the honey microbiome produces bioactive compounds, for example, by assessing whether the concentrations of these bacteria inhibit other microorganisms when grown together.

Several MAGs and isolates from related microorganisms were recently recovered. Among them, there are vinegar “Cupei” isolates in Chinat¹⁰. These studies have established the acid tolerance of Acetilactobacillus^11,12 and the ecological succession of the microorganisms in the microbial community, from when the vinegar is fresh until it is completely fermented^10,12. We propose comparing the metabolites produced by our isolates with those of related genera. The genetic variance between them should be reflected in the metabolic variance, and when combined with the change in brain metabolites, we believe this is enough for publication. This grant would help not one but three young female authors, 2 students, and one postdoctoral researcher to finish this project in which the three of us are interested.

In synthesis, we want to test the following hypothesis:

H1 Meliponilactobacillus is globally distributed in the honey of stingless bees (Meliponini) and harbors biosynthetic gene clusters encoding specialized metabolites.

H2 Brain metabolite abundance changes when inoculating the bees with Meliponilactobacillus.

Impact statement

Honey is the bees’ food during winter and part of the larvae’s diet; this study will lay the groundwork for understanding whether this microorganism contributes to the stability and resilience of honey as a preserved food source. The results of the experiments testing these hypotheses will enable us to enhance our understanding of the role of Meliponilactobacillus in relation to other microorganisms and to the bees themselves. These results could initiate a research line to investigate if some mechanism to preserve the honey is metabolite-mediated. Furthermore, by linking microbial dynamics to brain metabolite changes, we will explore potential gut–brain interactions in bees, opening new avenues for stingless bees health research. Together, these findings could have significant implications for pollinator conservation, honey production, and our broader understanding of host–microbe interactions.

3. Methods

Participants/Sample

Reanalyzing data. We have collected public Apis mellifera honey microbiome data (n~100) that, together with our Melipona honey microbiomes (n=17) and the genomes of isolates (n=3). Metagenomes will be analyzed to search for Melliponilactobacillus MAGs; all of them and related genera will be reanalyzed, and, within them, biosynthetic Gene clusters will be identified. Metagenomics. Costa Rican honey and bee samples have been collected, and DNA has been extracted; samples from other Latin American countries are expected.

Mass spectrometry: To monitor the dynamics of expression of antibiotic-related genes, we will collect biological replicates under two conditions: the presence of antagonists and pH variation. We will use four strains: Acetilactobacillus and our three isolates (n = 3), which will be cultivated and from which extracts will be obtained for mass spectrometry analysis. To identify the metabolite associated with the BGC, we will compare these extracts with those from Acetilactobacillus, which lacks the BGC. If time allows, we will perform targeted mutagenesis to silence the BGC. Transcriptomics. If feasible, we will perform transcriptomic analyses under antagonist presence and pH variation conditions for the four strains mentioned above.

Inclusion criteria:
For bioinformatic analyses, we will include all publicly available metagenomes to broadly assess the distribution of the BGC. For the mass spectrometry and transcriptomic experiments, we will use the Meliponilactobacillus isolates currently available in our collection, which belong to two different species according to ANI. As a control, we will include Acetilactobacillus, which can be obtained commercially and does not contain the BGC but is genetically very similar.

Exclusion criteria:
We will work with isolates rather than direct metagenomic samples because they allow more controlled and interpretable conclusions. We will not include Nicoliella, a closely related genus, because it is not suitable as a control since it also contains the BGC.

Sample size justification:

Four samples are sufficient for the characterization of the BGC because we are including a control. Three strains are positive (they contain the BGC), and one negative control, Acetilactobacillus, does not contain the BGC.

For the inoculation experiments, mature pupae will be extracted from meliponaries located at CINAT (Heredia, Costa Rica). Rearing of bees: At least 48 mature pupae will be collected and reared in sterile boxes. Pre-inoculation: Emerged bees are verified to be microbiota-depleted by qPCR before inoculation. Inoculation: Bees are exposed to Melliponilactobacillus. Post-inoculation: metabolomics analysis from at least 12 bees exposed to the inoculum and 12 bees from the control treatment.

Inclusion criteria: For the inoculation experiment, all the bees that successfully develop and come from boxes where the microbiota depletion test by qPCR shows no detectable bacterial DNA. A total of 48 pupae will be used in the experiment, equally divided between the control (24 pupae) and inoculated (24 pupae) groups. Potential mortality is taken into account to ensure that at least 12 adult bees per group will be available for metabolomic analyses. Although the sample is small, it is statistically sufficient to detect biologically relevant differences between groups⁹, while also optimizing the use of resources and ensuring feasibility within the scope of the project.

Materials and Procedures

Metagenomic reads will be quality-filtered and trimmed using fastp to remove adapters and low-quality bases. High-quality reads will be assembled into contigs using MEGAHIT, and resulting assemblies will be binned into metagenome-assembled genomes (MAGs) with MetaBAT2. MAG quality (completeness and contamination) will be evaluated using CheckM, and taxonomic classification will be performed with GTDB-Tk. Gene prediction and annotation will be carried out using Prokka, and biosynthetic gene clusters (BGCs) will be identified using antiSMASH, enabling the detection and comparison of candidate BGCs across Meliponilactobacillus genomes and related taxa.

Fermentations and metabolite extraction for mass spectrometry
Selected strains will be cultivated under controlled fermentation conditions to promote metabolite production. Cultures will be grown in appropriate media and incubated under conditions that reflect the natural acidic environment of honey. After reaching the desired growth phase, culture supernatants and cell pellets will be collected and subjected to metabolite extraction using organic solvents. Extracts will then be concentrated and analyzed by UHPLC–MS/MS to detect and compare metabolite profiles between BGC-positive Meliponilactobacillus strains and the BGC-negative control (Acetilactobacillus). Comparative metabolomic analyses will be used to identify candidate metabolites associated with the BGC.

Antimicrobial assays (antibiograms)
The antimicrobial activity of the strains and their extracts will be evaluated using agar diffusion and minimum inhibitory concentration (MIC) assays against selected bacterial and fungal microorganisms. These experiments will allow us to determine whether the BGC-associated metabolites confer inhibitory activity and to compare the antimicrobial profiles of BGC-positive strains with the BGC-negative control.

Genomic DNA Extraction and Purification: If more samples arrive we will follow the DNA extraction workflow previously optimized in our group and collaborators⁵. Sample input: DNA will be extracted and concentrated from 50 g of honey into 1.5 mL tubes. Pre-treatment: A pre-treatment step will be applied following protocols in Soares 2011 ¹³, and Waiblinger 2012¹⁴ to optimize DNA recovery from honey matrices. Extraction: DNA will be isolated using the Qiagen DNeasy Blood & Tissue Kit after determining the optimal dilution for each sample. Mechanical lysis will be performed with a TissueLyser II (Qiagen) using 3 mm tungsten carbide beads. Column-based purification: Silica spin columns will be used following manufacturer’s guidelines (buffer ATL, Proteinase K, ethanol) to obtain DNA of sufficient yield and purity. Quality control: DNA concentration will be quantified (≥20 ng/µL, total ≥500 ng), and purity assessed with a QIAxpert spectrophotometer, targeting A260/280 ratios>1.7. Microorganism sampling: Complementary microbial isolation will be conducted following Syed 2022¹⁵ methodology

Bee rearing: Anexic and gnotobiotic bees will be produced following the methodology previously used in honeybees and bumblebees^16,17. Briefly, mature pupae will be collected from meliponaries at CINAT, placed in sterile boxes, and incubated to complete their development. Pupae kept under sterile conditions will emerge as microbiota-free adult workers. At last, six boxes with eight pupae will be reared, resulting in a total of 48 bees. To confirm that bees are microbiota-free, the gut from one bee per cage will be extracted, and bacterial loads will be determined by quantitative PCR (qPCR) using universal primers targeting the 16S rRNA gene. If a box shows amplification in the aPCR assay of its pupae, it will be excluded from the experiment. Inoculation: Bees that have emerged with no development defects will be selected and transferred to new sterile cup cages placed on top of 100 mm petri dishes, where they will be exposed to the inoculum. The inoculum (Melliponilactobacillus) will be mixed with sterile sugar water (1:1), and added as 100 ul droplets. Control cages will contain bees that will be fed only with sugar water. From the total number of successfully developed bees, half will be inoculated, and the other half will serve as a control. The exposure to the inoculum will be for 7 days. Metabolomics: Brains will be dissected for at least 12 bees from each group for metabolomics analysis. Samples will be processed using a modified Bligh and Dyer protocol, and a GC-MS analysis of metabolites will be performed ¹⁸.

Planned Analyses

We will combine comparative genomics, metabolomics, microbiological assays, and gnotobiotic bee experiments to characterize the function and ecological role of the Meliponilactobacillus BGC.

Comparative genomic analyses.

We will determine the distribution, conservation, and structural variation of the BGC across Meliponilactobacillus isolates, MAGs, and closely related honey-associated lactobacilli. This will allow us to identify core genes, accessory genes, and candidate tailoring enzymes associated with metabolite biosynthesis.

Mass spectrometry-based metabolomics. We will identify metabolites associated with the presence of the BGC. Representative BGC-positive and BGC-negative strains, as well as closely related control taxa such as Acetilactobacillus or Nicoliella, will be cultured under standardized and differential growth conditions. Metabolite extracts will be analyzed by UHPLC-MS/MS, followed by feature detection, molecular networking, and comparative metabolomic profiling to identify candidate specialized metabolites specifically enriched in BGC-positive strains.

Bioactivity

We will evaluate these candidate metabolites and producing strains against other microorganisms relevant to the honey bee microbiome and hive environment. These experiments will include agar diffusion assays, co-culture inhibition assays, minimum inhibitory concentration (MIC) measurements, and growth curve analyses against bacterial and fungal targets. This will test whether the BGC contributes to microbial competition, niche exclusion, or protection against potential pathogens or spoilage-associated microorganisms.

Inoculations assays

Finally, we will assess the ecological relevance of the BGC in gnotobiotic stingless bees. Germ-free or microbiota-reduced bees will be inoculated with BGC-positive versus BGC-negative strains, or with defined synthetic communities differing in BGC content. We will then evaluate colonization success, persistence, host survival, microbial load, and changes in community structure, together with metabolite detection in the bee or honey-associated environment. These experiments will test whether the BGC provides a fitness advantage in host colonization, microbiome assembly, or host-microbe interactions.

Overall, this integrative approach will allow us to link genotype, metabolite production, antimicrobial activity, and host-associated function, providing a mechanistic understanding of the ecological role of this conserved BGC in honey-associated Meliponilactobacillus.

Ethics and Data Management

Ethical approval will be obtained from the relevant institutional bodies when required. Confidentiality will be ensured by anonymizing sensitive information. All generated data will be shared in accordance with FAIR principles and deposited in publicly accessible repositories, while sensitive metadata will be archived under controlled-access conditions.

4. Pilot Data (Optional)

We already have 17 honey samples, their metagenomes, and 3 isolates analyzed, which will serve as a point of comparison. At the Centro de Innovación para el Desarrollo Apícola Sustentable en Quintana Roo (CIDASQROO) of the Universidad Intercultural Maya de Quintana Roo. There are samples of Melipona beecheii and fermented honey available, in extreme cases. We also have DNA extracted from the microbiomes of Costa Rican honey. We implemented the protocol for producing axenic and gnotobiotic bees, previously used in Apis and bumblebees, and confirmed its effectiveness in generating microbiota-free stingless bees suitable for subsequent inoculations.

5. Budget

Sampling (covered by us)
Microbiological kits (DNA Extraction and biolog) 1000
Shotgun metabarcoding sequencing: 6 samples 3500 DOL nanopore
Buying Acetilactobacillus strains from comparison and importation expenses: 1000 DOL
Laboratory items such as petri dishes, tubes, medium, etc for the antibiogram against other microorganisms 2000 DOL
Metabolomics (24 samples 2500 DOL)

6. References

1. Cabezas-Mera, F., Cedeño-Pinargote, A. C., Tejera, E., Álvarez-Suarez, J. M. & Machado, A. Antimicrobial activity of stingless bee honey (Tribe: Meliponini) on clinical and foodborne pathogens: A systematic review and meta-analysis. Food Front. 5, 964–993 (2024).

2. Ramón-Sierra, J., Magaña-Ortiz, D., Ortiz, E. & Ruiz Ruiz, J. Medicinal uses of Melipona Bee Honey in Mayan Communities of Yucatán. in Stingless Bee’s Honey from Yucatan: Culture, Traditional Uses and Nutraceutical Potential (eds Ortiz Vázquez, E. de la L., Ruiz Ruiz, J. C., Magaña Ortiz, D. I. & Ramon Sierra, J. M.) 11–26 (Nova Science publishers, 2016).

3. Ek, J. & Magaña, M. Características socioeconómicas de la meliponicultura en localidades rurales del estado de Yucatán. Textual 205–232 (2023) doi:10.5154/r.textual.2022.81.11.

4. Lazarino, L. da C. et al. Meliponicultura: potencialidades e limitações para a conservação de abelhas nativas e redução da pobreza. Divers. J. 6, 2217–2236 (2021).

5. Xolalpa-Aroche, A. et al. Uncharacterized Acetilactobacillus-like genera represent the most abundant microorganisms in honeys produced by native stingless bee. 2025.06.25.661387 Preprint at https://doi.org/10.1101/2025.06.25.661387 (2025).

6. Ashique, S. et al. Gut-brain axis: A cutting-edge approach to target neurological disorders and potential synbiotic application. Heliyon 10, e34092 (2024).

7. Engel, P. & Moran, N. A. The gut microbiota of insects – diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013).

8. Liberti, J. et al. The gut microbiota affects the social network of honeybees. Nat. Ecol. Evol. 6, 1471–1479 (2022).

9. Li, L. et al. Gut microbiome drives individual memory variation in bumblebees. Nat. Commun. 12, 6588 (2021).

10. Wu, J. et al. Metagenomics and Metagenome-Assembled Genomes: Analysis of Cupei from Sichuan Baoning Vinegar, One of the Four Traditional Renowned Vinegars in China. Foods 14, 398 (2025).

11. Li, Q. et al. Insight into the acid tolerance mechanism of Acetilactobacillus jinshanensis subsp. aerogenes Z-1. Front. Microbiol. 14, (2023).