Teixeira Cláudia, Rodrigues Sara, Amorim João, Diogo Bárbara S, Pinto Ivo, Carvalho António Paulo, Antunes Sara C, Teles Luís Oliva
Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal.
CIIMAR/CIMAR LA, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Matosinhos, Portugal.
Ecotoxicology. 2025 Jul;34(5):746-759. doi: 10.1007/s10646-025-02873-0. Epub 2025 Mar 19.
Environmental exposure to caffeine (CAF) poses potential risks to aquatic ecosystems, affecting non-target species. This study investigated the chronic effects of environmentally relevant CAF concentrations, ranging from 0.16-50 µg/L, on zebrafish behaviour. A Kohonen-type artificial neural network classified zebrafish behaviour into nine behavioural classes based on a set of movement descriptors (mean meander, mean velocity, instantaneous velocity, distance to centre point, mean angular velocity and instantaneous acceleration), while a comprehensive analysis integrated behavioural classes previously defined and biochemical markers of oxidative stress, lipid peroxidation, reserve energy content, energetic pathways, and neurotoxicity. The discriminant analysis demonstrated that behaviour descriptors and biomarkers individually explained 38% and 67% of data variation, respectively, while the combination resulted in 19 models with 100% correct diagnosis. One of the models (Model A) seemed to suit the best dose-response relationship, incorporating key biomarkers including superoxide dismutase, catalase, glutathione peroxidase activities, and behavioural characteristics such as movement distance and velocity. This suggested methodology offers a different approach to evaluating CAF's ecological impact, highlighting behavioural analysis as a valuable complement to traditional ecotoxicological assessments. This study provides a novel framework for understanding organism-level responses to environmental stressors (e.g., several anthropogenic compounds), utilising Mahalanobis distance as an integrative response index. This approach shows promise for broader application in assessing the impact of various aquatic contaminants on aquatic organisms (from bacteria to fish), potentially extending to pharmaceuticals, pesticides, and industrial pollutants.
环境中接触咖啡因（CAF）会对水生生态系统构成潜在风险，影响非目标物种。本研究调查了环境相关浓度范围为0.16 - 50μg/L的CAF对斑马鱼行为的慢性影响。一个Kohonen型人工神经网络根据一组运动描述符（平均曲折度、平均速度、瞬时速度、到中心点的距离、平均角速度和瞬时加速度）将斑马鱼行为分为九种行为类别，同时进行了一项综合分析，整合了先前定义的行为类别以及氧化应激、脂质过氧化、储备能量含量、能量途径和神经毒性的生化标志物。判别分析表明，行为描述符和生物标志物分别单独解释了38%和67%的数据变化，而两者结合产生了19个诊断正确率为100%的模型。其中一个模型（模型A）似乎最符合剂量反应关系，纳入了包括超氧化物歧化酶、过氧化氢酶、谷胱甘肽过氧化物酶活性等关键生物标志物以及运动距离和速度等行为特征。这种建议的方法提供了一种评估CAF生态影响的不同途径，突出了行为分析作为传统生态毒理学评估的有价值补充。本研究提供了一个新的框架，用于理解生物体对环境应激源（如几种人为化合物）的反应，利用马氏距离作为综合反应指标。这种方法有望在评估各种水生污染物对水生生物（从细菌到鱼类）的影响方面得到更广泛的应用，可能扩展到药物、农药和工业污染物。
