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Secondary Toxicity Mechanisms of Insecticides and Their Metabolism in Pollinators

Secondary Toxicity Mechanisms of Insecticides and Their Metabolism in Pollinators
杀虫剂的次生毒性机制及其在传粉媒介中的代谢
批准号:
2236030
负责人:
金额:
$0.0万
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --

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中文摘要
翻译
昆虫是传粉者面临的主要压力源之一,其数量在全球范围内呈下降趋势。很少有研究已经进行,以概况其次要影响,即不介导的主要目标相互作用的反应。酿酒酵母,面包酵母,提供了一个理想的模型,为表征二次杀虫剂的影响,因为它的基因组缺乏他们的主要分子目标,而在同一时间共享基本的细胞过程与其他真核生物。将进行基因表达分析,以揭示酵母中的杀虫剂介导的反应,十二种杀虫剂,代表五类。这些结果将结合现有的,类似的表达研究大黄蜂,B.terrestris,以确定同源的二级反应。全面了解杀虫剂的毒性机制对于评估其对传粉者的风险至关重要。 该项目的第二部分将旨在绘制负责杀虫剂代谢的酶在B.terrestris。使用基于酿酒酵母的表达系统,将评价细胞色素p450酶(CYP1A1)驱动的杀虫剂代谢,并确定单个CYP1A1底物谱。反过来,CYP代谢的重要性将成为系统发育比较研究的基础,以评估杀虫剂代谢的能力,在远亲传粉物种。这是预测性解毒特征分析的第一步。
英文摘要
Insecticides are one of the main stressors faced by pollinators, whose numbers are in global decline. Few studies have been undertaken to profile their secondary effects, that is responses not mediated through primary target interactions. S.cerevisiae, Baker's yeast, offers an ideal model for characterising secondary insecticide effects as its genome lacks their main molecular targets, whilst at the same time sharing fundamental cellular processes with other eukaryotes. Gene expression analysis will be conducted to reveal insecticide mediated responses in yeast for twelve insecticides, representing five classes. These results will be combined with existing, analogous expression studies in the bumblebee, B.terrestris, to identify homologous secondary responses. Gaining a holistic understanding of insecticide toxicity mechanisms is crucial for assessing their risk to pollinators. The second part of this project will aim to map the enzymes responsible for insecticide metabolism in B.terrestris. Using S.cerevisiae based expression systems, cytochrome p450 enzyme (CYP) driven insecticide metabolism will be evaluated, with individual CYP substrate profiles determined. In turn, CYPs important to metabolism will become the basis for a phylogenetic comparison study, to assess the capability of insecticide metabolism in distantly related pollinator species. This presents the first step to predictive detoxification profiling.
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