Publication details

The role of antioxidants in the prediction of honey bee, Apis mellifera L., longevity

Authors

KONUPKOVÁ Anežka HURYCHOVÁ Jana

Year of publication 2019
Type Conference abstract
MU Faculty or unit

Faculty of Science

Citation
Description Honey bee, Apis mellifera L., is a well-known organism with a large ecological and economical importance. Recently, an increase in frequency of colony losses that could be caused by many factors including oxidative stress was observed. Oxidative stress arises during disbalance among the amount of reactive oxygen or nitrogen species and antioxidants and it leads to the cell degradation which can result in senescence of organisms. Therefore, there are many antioxidant mechanisms protecting honey bees against oxidative stress that could also affect their longevity. Antioxidants could be very diverse, and they are divided into three groups: enzymatic, non-enzymatic and hormonal antioxidants. There are two different populations of honey bee workers in our temperate climate – short-living summer populations and long-living winter generation. We used oxygen radical absorbance capacity (ORAC) assay for measurement of the total antioxidant capacity of haemolymph. ORAC assay is a fluorescent method used to measure the amount of antioxidants in various samples, particularly in food. We followed an antioxidant capacity of Apis mellifera haemolymph for two years and noticed a higher level of antioxidants in summer populations. This could be caused by the fact that honey bees gain a lot of antioxidants from food and during the summer season they can feed on plant products. However, the same haemolymph samples we also measured by electrophoresis to determine the level of vitellogenin, the yolk protein, which has except others also antioxidant properties. Vitellogenin was found to be increased in winter bee generation. These data suggest that honey bee populations are protected by different antioxidants through the year and their determination could help us with longevity prediction. This project was funded by NAZV grant number QK1910286.
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