La Porta Gianandrea, Magara Gabriele, Goretti Enzo, Caldaroni Barbara, Dörr Ambrosius Josef Martin, Selvaggi Roberta, Pallottini Matteo, Gardi Tiziano, Cenci-Goga Beniamino T, Cappelletti David, Elia Antonia Concetta
Department of Chemistry, Biology and Biotechnology, University of Perugia, 06126 Perugia, Italy.
Department of Agricultural, Food and Environmental Sciences, University of Perugia, 06126 Perugia, Italy.
Toxics. 2023 Aug 1;11(8):661. doi: 10.3390/toxics11080661.
Insect pollinators provide an important ecosystem service that supports global biodiversity and environmental health. The study investigates the effects of the environmental matrix on six oxidative stress biomarkers in the honey bee . Thirty-five apiaries located in urban, forested, and agricultural areas in Central Italy were sampled during the summer season. Enzyme activities in forager bees were analyzed using an artificial neural network, allowing the identification and representation of the apiary patterns in a Self-Organizing Map. The SOM nodes were correlated with the environmental parameters and tissue levels of eight heavy metals. The results indicated that the apiaries were not clustered according to their spatial distribution. Superoxide dismutase expressed a positive correlation with Cr and Mn concentrations; catalase with Zn, Mn, Fe, and daily maximum air temperature; glutathione S-transferase with Cr, Fe, and daily maximal air temperature; and glutathione reductase showed a negative correlation to Ni and Fe exposure. This study highlights the importance of exploring how environmental stressors affect these insects and the role of oxidative stress biomarkers. Artificial neural networks proved to be a powerful approach to untangle the complex relationships between the environment and oxidative stress biomarkers in honey bees. The application of SOM modeling offers a valuable means of assessing the potential effects of environmental pressures on honey bee populations.
昆虫传粉者提供了一项重要的生态系统服务，支持着全球生物多样性和环境健康。该研究调查了环境基质对蜜蜂体内六种氧化应激生物标志物的影响。在夏季，对位于意大利中部城市、森林和农业地区的35个养蜂场进行了采样。使用人工神经网络分析采集蜂的酶活性，从而在自组织映射图中识别并呈现养蜂场模式。自组织映射图节点与八种重金属的环境参数和组织水平相关。结果表明，养蜂场并未根据其空间分布聚类。超氧化物歧化酶与铬和锰浓度呈正相关；过氧化氢酶与锌、锰、铁及每日最高气温呈正相关；谷胱甘肽S-转移酶与铬、铁及每日最高气温呈正相关；谷胱甘肽还原酶与镍和铁暴露呈负相关。本研究强调了探索环境压力源如何影响这些昆虫以及氧化应激生物标志物作用的重要性。事实证明，人工神经网络是理清环境与蜜蜂氧化应激生物标志物之间复杂关系的有力方法。自组织映射图建模的应用为评估环境压力对蜜蜂种群的潜在影响提供了一种有价值的手段。
