在被Phytophthora sojae感染后,大豆中的基因调控网络推断
Brett Hale1,2,3, Sandaruwan Ratnayake2,3, Ashley Flory2
1Molecular Biosciences Graduate Program, Arkansas State University, State University, AR, United States of America.
PloS one
|July 7, 2023
概括
这项研究使用深度学习来分析大豆.
科学领域:
- 植物病理学 植物病理学
- 分子生物学分子生物学
- 基因组学就是基因组学.
背景情况:
- Phytophthora sojae会导致大豆的根和茎腐烂,导致大量的产量损失.
- 目前对Phytophthora根和茎腐烂 (PRR) 的管理策略受到不断变化的病原体病变型的挑战.
- 需要新的方法来增强大豆对P. sojae.的耐药性.
研究的目的:
- 用高通量测序和深度学习阐明大豆防御P. sojae的分子机制.
- 确定涉及大豆-P. sojae相互作用的差异表达基因 (DEG) 和关键转录因子 (TF).
- 构建一个基因调节网络,使大豆对P. sojae.免疫.
主要方法:
- 生成的转录组用于识别大豆与P. sojae相互作用中的DEG.
- 利用DNA亲和力净化和测序 (DAP-seq) 来识别TF结合位点.
- 训练深度神经网络 (DNN) 使用大豆和阿拉比多普西斯数据来预测TF结合点.
- 开发了一种基因调节网络,基于预测的TF-目标基因相互作用.
主要成果:
- 确定了DEGs和选择的WRKY和RAV家族转录因子,参与大豆防御.
- 通过使用DNN成功预测了大豆基因组中的TF结合位.
- 利用Arabidopsis的跨物种数据来增强大豆中TF结合部位的预测.
- 构建了一个基因调节网络,说明TF介导的免疫反应.
结论:
- 这项研究为大豆和P. sojae.之间的分子植物病原体相互作用提供了新的见解.
- 开发的基因调节网络突出了大豆免疫力中关键的TF-目标基因相互作用.
- 这些发现可以帮助开发具有对P. sojae.持久耐药性的大豆品种.
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