Data from: Seasonal dynamics and co-occurrence patterns of honey bee pathogens revealed by high-throughput RT-qPCR analysis
D'Alvise, Paul et al. (2019), Data from: Seasonal dynamics and co-occurrence patterns of honey bee pathogens revealed by high-throughput RT-qPCR analysis, Dryad, Dataset, https://doi.org/10.5061/dryad.q7s60q9
The health of the honey bee Apis mellifera is challenged by introduced parasites that interact with its inherent pathogens and cause elevated rates of colony losses. To elucidate co-occurrence, population dynamics and synergistic interactions of honey bee pathogens, we established an array of diagnostic assays for a high-throughput qPCR platform. Assuming that interaction of pathogens requires co-occurrence within the same individual, single worker bees were analyzed instead of collective samples. Eleven viruses, four parasites and three pathogenic bacteria were quantified in more than one thousand single bees sampled from sixteen disease-free apiaries in Southwest Germany. The most abundant viruses were Black Queen Cell Virus (84%), Lake Sinai Virus 1 (42%), and Deformed Wing Virus B (35%). Forager bees from asymptomatic colonies were infected with two different viruses in average, and simultaneous infection with four to six viruses was common (14%). Also the intestinal parasites Nosema ceranae (96%) and Crithidia mellificae/Lotmaria passim (52%) occurred very frequently. These results indicate that low-level infections in honey bees are more common than previously assumed. All viruses showed seasonal variation, while N. ceranae did not. The foulbrood bacteria Paenibacillus larvae and Melissococcus plutonius were regionally distributed. Spearman’s correlations and multiple regression analysis indicated possible synergistic interactions between the common pathogens, particularly for Black Queen Cell Virus. Beyond its suitability for further studies on honey bees, this targeted approach may be, due to its precision, capacity and flexibility, a viable alternative to more expensive, sequencing-based approaches in non-model systems.