A systems biology approach to unravel mechanisms of pesticide resistance

农药耐药性是一个世界性的问题，阐明其潜在机制对粮食安全和人类健康至关重要。理解杀虫剂抗性过程的基础机制具有挑战性，因为它们随着进化时间的推移而发展，受到食物网动态的影响，并受到涉及直接生物体和间接微生物区系反应的复杂机制的调节。需要一种跨代的系统生物学方法来解开这种复杂性。在这里，我们使用特殊的节肢动物水蚤来研究导致杀虫剂抗药性的纵向动态。水蚤具有其他节肢动物的许多特征，如短的生命周期和外部产卵的胚胎。这些胚胎可以通过克隆繁殖在实验室中无限繁殖，允许从单一基因培育出相同基因的个体(克隆)群体。此外，水蚤因其长达数十年甚至数百年的休眠而独一无二，这使得该模型成为研究导致杀虫剂抗药性的基础进化机制的理想模型。我们将应用系统生物学的方法来研究因农业土地利用而从原始环境转变为受干扰环境的水蚤复活标本。我们将使用质谱仪对环境中的农药水平进行量化，并使用环境DNA对食物网的组成进行量化。我们将把这些参数与水蚤基因组范围的转录、代谢和肠道微生物区系动态的变化联系起来，建立食物网动态、基因组范围的转录和微生物区系变化之间的因果关系。

Pesticide resistance is a worldwide concern and elucidating the underlying mechanisms is critical for food security and human health. Understanding the mechanisms underpinning the process of pesticide resistance is challenging because they develop over evolutionary time, are influenced by foodweb dynamics and regulated by a complex machinery that involves direct organisms and indirect microbiota response. A transgenerational systems biology approach is need to unravel this complexity.Here, we use the exceptional model arthropod, the waterflea Daphnia, to study longitudinal dynamics that led to pesticide resistance. Daphnia enjoys many of the characteristics of other arthropods, such as a short life cycle and externally laid embryos. These embryos can be propagated indefinitely in the laboratory via clonal reproduction, allowing the rearing of populations of isogenic individuals (clones) from a single genotype. Additionally, Daphnia is unique for its extended dormancy that spans decades or even centuries, making this model ideal to study the underpinnign evolutionary mechanisms that enable pesticide resistance. We will apply a systems biology approach to resurrected specimens of Daphnia transisitioning from pristine to perturbed enviroments by agricultural land-use. We will quantify the level of pesticides in the environment using mass spectrometry and the composition of the food web using environmental DNA. We will link these parameters to changes in Daphnia genome-wide transcriptional, metabolomic and gut microbiota dynamics, establishing causal associations among foodweb dynamics, genome-wide transcriptional and microbiota changes.