Characterizing the Honey Bee Interactome using Mass Spectrometry‐Based Proteomics

Western honey bees ( Apis mellifera ), as a superorganism, have an essential ecological and economic role as pollinators. However, various understudied pathogens infecting honey bees pose a significant threat to their health. One such pathogen is Nosema ceranae , a microsporidian parasite which rapidly proliferates in the honey bee ventriculus (midgut) cells, weakening their immune systems by competing for nutrients. Upon completing their lifecycle in the midgut cells, Nosema spores rupture host cells and spread into the rest of the gut and subsequently to other bees in the colony. The resulting symptoms of the diseased colony have been attributed to colony death. We hypothesize that host‐pathogen interactions underpin Nosema infection in honey bees, and therefore understanding pathological protein interactions will suggest tools for combating Nosema . To this end, we used an optimized version of the mass spectrometry‐based co‐fractionation experiment described in Kristensen et al . (2012) This method uses size exclusion chromatography (SEC) to separate protein complexes into fractionated samples. After measuring protein amounts in each fraction through mass spectrometry, a machine learning pipeline detects proteins with similar separation profiles as protein interactions (Stacey et al ., 2017). We can then assess the effects of Nosema infection. Using an in vivo approach, we first constructed a honey bee interactome from uninfected midgut cells. This is the first honey bee interactome to date. Validation experiments using co‐immunoprecipitation coupled with mass spectrometry (IP‐MS) demonstrate that this interactome is high quality and will be a valuable tool to future researchers. Using this interactome as a baseline, we can then compare interactomes derived from infected honey bee cells to identify the protein‐based mechanisms of Nosema infection. Preliminary results from this highlight interactome changes in various processes such as peroxiredoxin activity and carbohydrate binding. This work presents the first honey bee interactome map. In addition, we are gaining novel insight into how protein interactions change upon infection. References 1. Kristensen, A. R., Gsponer, J., & Foster, L. J. (2012). A high‐throughput approach for measuring temporal changes in the interactome. Nature Methods , 9(9), 907‐909. doi:10.1038/nmeth.21312. Stacey, R. G., Skinnider, M. A., Scott, N. E., & Foster, L. J. (2017). A rapid and accurate approach for prediction of interactomes from co‐elution data (PrInCE). BMC Bioinformatics , 18(1), 457.

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