The increasing levels of pesticide resistance in agricultural pests and disease vectors represents a threat to both food security and global health. As insecticide resistance intensity strengthens and spreads, the likelihood of a pest encountering a sub-lethal dose of pesticide dramatically increases. Here, we apply dynamic Bayesian networks to a transcriptome time-course generated using sub-lethal pyrethroid exposure on a highly resistant Anopheles coluzzii population. The model accounts for circadian rhythm and ageing effects allowing high confidence identification of transcription factors with key roles in pesticide response. The associations generated by this model show high concordance with lab-based validation and identifies 44 transcription factors regulating insecticide-responsive transcripts. We identify six key regulators, with each displaying differing enrichment terms, demonstrating the complexity of pesticide response. The considerable overlap of resistance mechanisms in agricultural pests and disease vectors strongly suggests that these findings are relevant in a wide variety of pest species.

中文翻译：

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捕捉对亚致死性杀虫剂暴露的转录因子相互作用组

农业病虫害和病媒中抗药性水平的提高对粮食安全和全球健康构成威胁。随着抗药性强度的增强和扩散，有害生物遇到亚致死剂量农药的可能性大大增加。在这里，我们将动态贝叶斯网络应用于转录组时间过程，该过程使用高度抗药的按蚊拟南芥种群中的亚致死拟除虫菊酯暴露产生。该模型考虑了昼夜节律和衰老效应，可以高度可靠地鉴定在农药响应中具有关键作用的转录因子。该模型产生的关联与基于实验室的验证高度一致，并鉴定了44种调节杀虫剂响应转录本的转录因子。我们确定了六个主要监管者，每种都显示不同的富集术语，说明了农药响应的复杂性。农业病虫害和病媒中的抗药性机制有相当大的重叠，这强烈表明，这些发现与各种各样的害虫种类有关。