The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana.
The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana.
复制标题
拟南芥对高温和干旱联合胁迫反应的顺式调控编码。
DOI：
10.1093/nargab/lqaa049
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发表时间：
2020-09
影响因子：
4.6
通讯作者：
Shiu SH
中科院分区：
文献类型：
--
作者：
Azodi CB;Lloyd JP;Shiu SH
植物通过动态调节基因表达来响应环境。了解这些反应是如何调节的一个强有力的方法是将有关顺式调节元件（克雷斯）的信息整合到称为顺式调节代码的模型中。对组合应激的转录响应通常不是对单独应激的响应的总和。然而，顺式调控代码的基础上联合应激反应尚未建立。在这里，我们模拟了拟南芥的转录反应，单一和组合的热和干旱胁迫。我们根据基因的反应模式（独立、拮抗和协同）对基因进行分组，并训练机器学习模型，使用推定的克雷斯（pCREs）作为特征（中位数F-测量值= 0.64）来预测它们的反应。然后，我们开发了一种深度学习方法，将额外的组学信息（序列保守性，染色质可及性和组蛋白修饰）整合到我们的模型中，将性能提高了6.2%。虽然pCREs预测独立和拮抗反应的重要性往往类似于与热和/或干旱胁迫相关的转录因子的结合基序，重要的协同pCREs类似于不知道与应激相关的转录因子的结合基序。这些研究结果表明，在硅片的方法可以提高我们的理解的复杂代码调节反应的组合压力，并帮助我们确定未来表征的主要目标。
Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic and synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility and histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.