时间：2023年1月4日（周三） 14:30-16:00

会议号：腾讯会议:535 245 565

主办：华中农业大学、湖北洪山实验室

承办：植物科学技术学院、作物遗传改良全国重点实验室

报告人1：苏超  14:30-15:15

题目：根瘤菌侵染宿主细胞的动力学研究

Cellular dynamics during rhizobial infections

摘要：在根瘤菌与豆科植物建立共生关系的过程中，根毛会经过一系列形变捕获到根瘤菌形成侵染室，随后在根毛中诱导一个由细胞壁与细胞膜包裹的管状通道被称为侵染线。侵染线形成后需要穿越皮层细胞运输根瘤菌到达根瘤原基，进而释放根瘤菌到宿主细胞形成共生体进行固氮。这一系列复杂的生物学变化的背后是由一系列分子模块所调控的。近来，我们揭示了侵染线形成过程中细胞膜内陷的细胞学机理，以及侵染线实现胞内穿越的遗传调控机制。上述研究不仅提高了人们对侵染线发生与生长的理解，而且为将上述复杂的侵染过程整合到一个系统模型中提供了可能。

Rhizobial infections on legumes roots are characterized by a series of molecular switches that are required to morphologically steer the formation and the progression of the infection thread (IT). The sequence of events starts with a re-orientation of root hair growth followed by its final arrest in a curled state, the maintenance of an infection chamber, the onset of IT growth and its transcellular passage. We have identified several novel genetic components that now allow us to integrate these sequential processes into a systematic model. This includes the molecular mechanism for the primary membrane invagination from the infection chamber and the IT intracellular passage. Our studies improved the understanding of infection thread progression and root nodule symbiosis.

报告人2：赵虎 15:15-16:00

题目：作物的后GWAS时代：应对自然变异到功能的挑战

The post-GWAS era in crops: Tackling the challenge of natural variants to function

摘要：全基因组关联分析已被广泛用于作物重要性状调控基因的鉴定，然而从大量显著关联位点中找到候选基因以及功能位点依然充满挑战。利用多组学数据、机器学习与深度学习算法，我们开展了以下研究：1）利用染色质可及性数据与深度学习算法构建水稻遗传变异的功能效应图谱；2）构建PlantDeepSEA在线服务预测变异效应与鉴定调控元件；3）结合油菜多组学数据与机器学习算法解析油菜籽粒含油量等性状的遗传基础和调控网络。

Genome-wide association analysis has been widely used for the identification of genes regulating important traits in crops. However, finding candidate genes and functional variants from a large number of significantly associated sites is still challenging. Using multi-omics data, machine learning and deep learning algorithms, we have made the following attempts: 1) Construct a functional impact map of genetic variants in rice using chromatin accessibility data and deep learning algorithms. 2) Build PlantDeepSEA online service to predict variant impacts and identify regulatory elements; 3) Combine multi-omics data and machine learning algorithms in Brassica napus to resolve the genetic basis and regulatory network of traits such as oil content.