Location: Carl Hayden Bee Research Center
Project Number: 2022-30500-002-000-D
Project Type: In-House Appropriated
Start Date: Mar 11, 2025
End Date: Mar 10, 2030
Objective:
Objective: Benefit commercial beekeeping through understanding of the honey bee microbiome, colony communication, physiology, nutrition and behavior in health and disease. This effort will define the social aspect of known disease states, reveal novel states of disease or opportunism and their extended social influence, and provide targeted hypotheses for disease testing, diagnosis, and treatment. Using a combination of laboratory and field approaches, we will further our understanding of the functional capacities of the microorganisms typical of the hive environment, the alimentary tracts of queens, workers and developing larvae. We will apply this information to the management of disease and colony loss associated with commercial beekeeping. Finally, we will collaborate with industry to produce an artificial intelligence driven diagnosis of brood disease using a combination of high-resolution photographs and associated microbiome and viral signatures. This diagnostic tool will benefit the apiary inspectors and beekeeping community by providing a quick and reliable method for the diagnosis of brood disease, curtailing the misuse of antibiotics.
Objective 1: Conduct research to quantify the potential benefits of various queen gut microbiomes to colony health in a laboratory setting and compare with a commercial beekeeping operation.
Sub-obj. 1.A: Determine the tissue specific microbiome structure of the queen gut by age and sub-species.
Sub-obj. 1.B: Investigate host-microbial function of the aging queen gut.
Objective 2: Determine the effects of queen and worker microbiota on queen quality, queen productivity, and worker-queen interactions, including semiochemical signaling.
Sub-obj. 2.A: Determine the effects of social resource space and opportunistic bacteria on worker-queen interactions, queen health, and signaling.
Sub-obj. 2.B: Determine whether queens selectively avoid trophallactic feeding exchanges with workers infected by known pathogens or opportunists.
Objective 3: Conduct research to assess the effects of microbiota on worker-brood interactions in health and disease.
Sub-obj. 3.A: Determine host brood resilience to pathogens relative to microbiota character and social variables.
Sub-obj. 3.B: Determine host brood resilience to various EFB strains with exposure to propolis, honey and royal jelly in vitro.
Objective 4: Use artificial intelligence to develop a rapid and accurate brood disease diagnosis tool.
Sub-obj. 4.A: Expand our AI-training set to include a greater variety of larval disease symptomology from around the USA.
Sub-obj. 4.B: Produce an AI-driven brood disease diagnosis tool.
Approach:
To explore relationships of health and disease in greater depth, we merge the techniques of metagenomics, chemical ecology and artificial intelligence, with a focus on microbiomes and disease states that dominate the aerobic to microaerophilic antimicrobial niches associated with colony function and social nutrient processing. This approach will determine how different microbial species or groups of native microbiota, including opportunistic and disease causing species respond to, or cause stress, and influence key social interactions, largely unknown.
We will select particular queens to examine in molecular detail. Two highly informative factors we consider are known age, and carbonyl accumulation; a proxy for biological age often considered "molecular mileage". Microbiome co-factors include the absolute abundance of C. melissae, the queen gut bacterium associated with youth, fecundity and colony size. Concurrent with this economic colony-level assessment, we will examine how selective interactions contribute to the queen’s gut microbiota and queen quality, examining both physiological indicators of queen reproductive and nutritional physiology, pheromone signatures and odors.
We will assess queen reproductive quality, physiology, and productivity to determine impacts of worker-queen and worker-larval interactions. Representative queens, workers, and brood will be sampled and analyzed for microbiota, semiochemicals, and physiological state and queen productivity assessed as before. We will compare across treatments and time points by repeated measures analysis. Queen and worker-queen interaction metrics will be checked to determine if nest worker microbiota composition impacts queen care or productivity. We will compare statistically the number of successful, unsuccessful, and total queen feedings across treatments by Pearson’s chi square test.
To improve brood disease diagnoses, we use a combination of machine learning, high throughput analyses, and molecular diagnostics. Using SCINet resources and open source software we extract features from high-resolution digital images of larval disease phenotypes that have been paired with corresponding microbiome data. Our preliminary machine learning algorithms have been successful in predicting the microbiome result when supplied with novel image symptomology. We propose that an AI application can be trained to identify and predict disease agents with great certainty when working from a representative and comprehensive set of pathogen-defined imagery.
