Using exposomic profile signatures as predictors of honey bee hive health

Chloe Wang1, Malia Wenny1, Robert L. Broadrup1, Christopher Mayack2, Anthony Macherone3,4

1Haverford College, Haverford, PA USA, 2Swarthmore College, Swarthmore, PA USA, 3Agilent Technologies, Santa Clara, CA USA, 4Johns Hopkins School of Medicine, Baltimore, MD USA

BibliographyBlacquière, T. et al. Ecotoxicology. 2012, 21:973.Celli, G. and Maccagnani, B. Bulletin of Insectology. 2003, 56(1):137-139.Couvillon, M.J. et al. Current Biology 2014, 24(11):1212-1215.Doublet, V. et al. Environ. Microbiol. 2014, 1.Henry, M. et al. Science. 2012, 336:348.Leita, L. et al. Environ. Monit. Assess. 1996, 43:1-9.Mullin, C.A. et al. PLoS ONE. 2010, 5(3):1.

METHODS

ABSTRACT BACKGROUND

• Sampled worker bees from 29 Philadelphia area hives

• 3 g bees from each hive extracted in 44 % water/55 % acetonitrile/1 % glacial acetic acid

• Blended in a Magic Bullet®

RESULTS

Figure 1. Overlay of total ion chromatograms for all sample injections (30 samples x 2 technical replicates). Similarity in the peaks for each sample shows reproducibility.

Figure 3. Principal component analysis (PCA) of a subset of hives for which hive health is known. Collapsed and healthy hives each clustered in 3D PCA space, while unhealthy hives clustered as outliers.

Figure 2. Unsupervised hierarchical clustering for a subset of hives for which hive health is known. Clustering shows a unique node for each hive status with corresponding higher (red) and lower (blue) relative abundance of chemical compounds.

FUTURE STUDIES• LC-MS and derivatized GC-MS analysis is planned in the near future

• Combine with data on the presence of varroamites, viruses, Nosema, and Foulbrood to assess correlation with chemical compounds

• End goal is to use a silicone band for a cost-effective way to monitor hive health on a regular basis using volatile compound signatures

Oldroyd BP. PLoS Biol 2007, 5:1195-1199.Rundlöf, M. et al. Nature. 2015, 521, 77.Sandrock, C. et al. PLoS ONE. 2014. 9(8):1.Watanabe ME. BioScience. 2008, 58(5):384-388.Wilfert L, et al. Science. 2016, 351(6273):594-597.

AcknowledgmentsThe authors wish to thank Haverford College, Swarthmore College, and Agilent Technologies. For Research Use Only. Not for use in diagnostic procedures.

• Solid phase extraction: sorbents (MgSO4, NaOAc, PSA, C18, and GCB) used to remove water, acids, long-chain alkanes, and pigments

Extraction protocol adapted from Mullin et al.

• Analyzed supernatants via GC-Q TOF

• Bioinformatics used to investigate correlation between health status based on PCR assays and detected compounds

• Bee populations are declining globally due to internal hive health problems such as parasites, and environmental factors such as habitat degradation and pesticide exposure

• Stressors leading to the ultimate collapse of honey bee colonies are likely multifactorial and are likely to act in synergistic ways

• Beekeepers are in need of a way to regularly monitor hive health comprehensively to identify causative and interacting agents of bee decline

• Combining disease screening using PCR assays and assessment of the total chemical environmental exposures (exposomics) using GC-Q TOF will facilitate the identification of novel internal and external stressor(s) and their potential synergistic interactions

Many disease and chemical stressors have been identified in the most recent worldwide decline of honey bees (Apis mellifera) and other pollinators. However, none of these factors have led to a “smoking gun” biomarker or array of biomarkers that predict the current and future health status of a hive, and we have yet to identify how exactly these causative factors lead to reduced hive health and ultimate collapse. To address this need, we implemented the exposomics paradigm using gas chromatography-high resolution time of flight mass spectrometry to characterize chemical profiles of honey bees collected from 29 hives from 7 unique geographical locations and compared this with their health status as indicated by disease load using multiplex semi-quantitative PCR analysis. Principal component and unsupervised hierarchal analysis revealed distinct clusters of healthy and unhealthy collapsed hives, with diseased hives as outliers of these two clusters. Our preliminary findings suggest that we can characterize the health status of a hive based on a chemical signature profile and identify multiple factors that might act synergistically and cause honey bee health decline. A longitudinal study is now underway to confirm which chemical changes will predict colony collapse before it occurs.