Evolutionary network analysis based on the transcriptome atlas of Marchantia polymorpha (EvoNet)

基因调控网络通过相互作用的转录因子控制植物中所有基因的表达，从而最终控制植物的表型。该网络是高度复杂的，经常包含冗余的元素，并且由于基因组复杂性扩展，通常从苔藓植物向种子植物扩展复杂性。地钱为我们提供了一个了解早期陆生植物进化和陆生植物转录因子进化历史的独特窗口。地钱缺乏全基因组复制，因此保留了一个相当简单的调控电路，总共只有374个转录因子。这种简单性将促进对代谢途径、细胞分化以及对内部和外部信号的响应的转录控制的研究。我们将构建一个转录组图谱的苔类植物，包括发展和压力和信号诱导的时间进程，其目的是捕捉大多数的转录变化，可以发生。有监督的机器学习方法利用该转录图谱来预测转录因子-靶基因相互作用并构建基因调控网络。我们打算（一）为地钱社区和我们自己提供一个全面的，开放的，容易获得的基因表达资源和（ii）利用资源来建立和分析地钱中的基因调控网络。我们将测试子功能化和新功能化在多大程度上增加了种子植物与地钱相比调控网络的复杂性。功能的实验验证将测试计算生成的假设。

Gene regulatory networks control the expression of all genes in plants via interacting transcription factors and therefore ultimately control the phenotypes plants display. The network is highly complex, frequently contains redundant elements, and generally expands in complexity from alga over mosses towards seed plants since genomic complexity expands. The liverwort Marchantia polymorpha presents us with a unique window into early land plant evolution and the evolutionary history of transcription factors in land plants. Marchantia lacks whole genome duplications and therefore retains a fairly simple regulatory circuitry with only 374 transcription factors in total. This simplicity will facilitate the study of transcriptional control for metabolic pathways, for cellular differentiation, and for responses to internal and external signals. We will construct a transcriptome atlas of the liverwort including developmental and stress and signal induced time courses which aims at capturing the majority of transcriptional changes that can occur. Supervised machine learning approaches make use of this transcriptional atlas to predict transcription factor-target gene interactions and construct a gene regulatory network. The presumably much simpler network of a liverwort can then be compared them with those in seed plant networks and the mechanisms for expansion studied.We intend to (i) provide a comprehensive, open, easily accessible gene expression resource for the Marchantia community and ourselves and (ii) leverage the resource to build and analyze gene regulatory networks in the liverwort. We will test to which degree sub- and neo-functionalization have contributed to increased complexity of regulatory networks in seed plants compared to liverworts. Experimental validation of function will test the hypotheses which were computationally generated.