Evaluating epigenetic and transcriptomic adaptations to climate change in Brasicca napus (oilseed rape)

本项目的目的是为英国特有品种的甘蓝型油菜热胁迫对产量的影响提供系统水平的分子和代谢方面的了解。这样一种系统生物学方法将通过监测dNaM和全球转录组图谱以及对热胁迫下油菜的代谢分析来实现。本研究的主要目的是：1.研究高温胁迫对甘蓝型油菜花前花后授粉育性和种子品质的影响。识别对热胁迫敏感的甘蓝型油菜基因组中dNaM的定点修饰和潜在的基因组印记3。使用RNA-Seq进行转录组图谱，以确定热应激导致的关键差异表达基因。利用目标2和3的研究结果，通过建立基因调控网络，整合表观遗传特征和相应的基因表达，以评估表观遗传对授粉育性和种子质量措施的调节作用。开发一个机器学习模型，该模型将预测未来高温胁迫对授粉、育性和种子质量的影响。使用决策树模型，将评估甲基化位点和基因表达之间的相关性。接下来，站点将被作为输入提供给决策树，以便按照重要性的顺序对这些站点进行排序，并最终估计代谢特征和表型特征(即授粉肥力和种子质量)作为输出变量。

The aim of this project is to provide a systems-level molecular and metabolic understanding of the impact of heat stress on yield of B. napus for UK-specific varieties. Such a systems biology approach will be achieved through monitoring DNAm and global transcriptome profiling in tandem with metabolic analysis of oilseed rape as a result of heat stress. The main objectives of the project are:1. Investigating the effect of heat stress on two UK-grown lines on pollination fertility and seed quality at pre- and post-anthesis periods of B. napus.2. Identifying site-specific modification of DNAm and potential genomic imprinting in the B. napus genome susceptible to heat stress3. Performing a transcriptome profiling using RNA-Seq, to identify key differentially expressed genes as a result of heat stress.4. Integrating the epigenetic profiling and the corresponding gene expression through the development of a gene regulatory network using findings from obj.2&3, in order to assess the epigenetic mediation effect on pollination fertility and seed quality measures.5. Developing a machine learning model which will predict the future impact of heat stress on pollination fertility and seed quality. Using the decision tree models, correlation between methylation sites and gene expression will be assessed. Next, sites will be fed to the decision tree as input in order to rank these in order of importance, and ultimately estimate metabolic profiles and phenotypic traits (i.e. pollination fertility and seed quality) as output variables.