禾谷镰刀菌动态基因调控网络的构建及致病性模块的挖掘

Wheat head blight is a devastating and difficult-to-control wheat disease worldwide primarily caused by filamentous fungus Fusarium graminearum. Years of international research has been dedicated to understanding F. graminearum pathogenic mechanisms, leading to characterization of many fungal pathogenicity genes. However, studies on gene regulatory networks connecting these fungal genes fall quite behind, which has impeded us from understanding the systems biology of the pathogenic mechanisms and beyond. Recently, the applicant has performed an inference of static global gene regulatory networks in F. graminearum, by harnessing a large collection of transcriptomic data and a machine-learning based computer algorithm. This static network contains key regulators that are organized into a modularized structure. To build on such work, this proposal aims to reconstruct the dynamic gene regulatory networks of the fungus, emphasizing characterizing the network modules involved in fungal pathogenicity, sexual reproduction and mycotoxin production. Specifically, the pathogenicity module networks will be validated using biological experiments involving high-throughput transcriptomic sequencing of regulator-knockout fungal mutants, which will produce highly precise regulatory networks containing true regulation. Finally, the validated pathogenicity networks will be subject to a further round of validation in transcription factor and DNA binding using ChIP-seq experiments, differentiating direct from indirect regulation and therefore improving the network resolution. By using a reverse-engineering rather than a single-gene driven approach, this project will deliver a dynamic gene regulatory network at genome scale in a both time- and cost-effective manner, allowing us to expedite understanding the structure and rewiring of pathogenicity gene circuits. This piece of key yet missing knowledge is required to accomplish the precision disease management against wheat heat blight.

由禾谷镰刀菌引起的小麦赤霉病是世界范围内小麦的毁灭性病害之一 ，具有流行广、危害大、防治难等特点。近年来国内外学者对病原菌致病机制进行了大量研究，鉴定了一批致病基因。然而，对于病原菌基因调控网络的研究尚缺乏，阻碍了我们从系统生物学角度对致病机制的理解。申请人前期结合转录组大数据和计算机算法，构建了病原菌的静态基因调控网络，发现了关键调控子并证实其模块化网络的特征。本项目拟在此基础上构建病原菌的动态基因调控网络，全面挖掘网络中调节致病性的模块及有性生殖、毒素产生等模块；通过调控子突变体构建和高通量测序技术，对致病性模块进行生物学验证，筛选阳性调控关系以提高网络的精确度；最后通过转录因子结合基因位点的实验验证，增强底层网络的解析度。不同于单基因研究模式，本项目逆向构建病原菌的动态调控网络，以求在基因组尺度上全方位、立体地理解病原菌的基因调控及动态，为阐明其侵染机制，精准防治赤霉病提供有力支持。

禾谷镰刀菌是一种导致毁灭性的农作物疾病的丝状真菌，并产生有害的霉菌毒素。研究复杂的禾本科镰刀菌转录调控网络对于有效的病害防治至关重要。重构禾谷镰刀菌动态基因调控网络属于复杂的NP-hard计算问题，因此传统的基于归约论或共表达的方法不可能解决该问题。禾谷镰刀菌的基因组学，转录组学数据和表型学数据之类的多组学数据对重建调控网络至关重要，但到目前为止它们很大程度上没有被充分利用。我们首次综合利用这些组学数据资源，使用一种称为“模块网络”的基于贝叶斯网络的算法来推断禾谷镰刀菌的全局基因调控网络，预测了包含49个基因模块的动态基因调控网络，这些模块显示出明显的条件特异性调控。通过基于先前生物学知识（包括功能注释和转录因子结合位点丰富化）的全面验证，我们的网络预测显示出较高的准确性，并与现有知识保持一致。使用Tri6和Tri10基因破坏引起的网络扰动以及使用Tri6 Chip-seq数据部分验证了一个调节模块。然后，我们开发了一种新颖的计算方法来计算模块与表型之间的关联，并确定了调节不同表型的主要模块组。结果，我们确定了改调控网络的子网络，鉴定了与表型相关的模块和关键调控因子。 这些子网络分别调控禾谷镰刀菌的毒力，有性繁殖和霉菌毒素的产生。最后，我们在禾谷镰刀菌的核心和谱系特异性基因组区域中发现了调控网络模块的区室化，反映了真菌物种中调控网络的进化特征。丝状真菌转录调控网络的系统重建为理解复杂的基因调控网络提供了新颖的见解，这些网络是揭示禾本科镰刀菌病理生物学关键过程的基础，并为改进疾病控制策略的发展提供了希望。