Cell:植物根系如何允许有益微生物定植的
  • 拟南芥的根需要细胞损伤来产生强烈的局部免疫反应;
  • 邻近细胞损伤的感知比损伤相关的分子模式(DAMPs)更复杂,引起模式识别受体(PRR)上调表达,可能是由细胞解体而引起的离子和渗透压释放或机械应力触发;
  • 细胞损伤导致对微生物相关分子模式(MAMPs)的反应能力的普遍的上调;
  • 根有限的、损伤门控的MAMPs响应有助于根被无害或有益的细菌种类定殖;
  • DAMPs释放可能是损伤感知的必要元素,但仅靠其本身不足以重建实际的细胞损害效果。
主编推荐语
刘永鑫-中科院-宏基因组
微生物相关分子模式(microbe-associated molecular patterns, MAMPs)的识别对于植物的免疫反应至关重要。如何将这种复杂的感知系统有效地部署在不断暴露于微生物的根中仍然是一个谜。本文通过分析拟南芥中单细胞分辨率下的MAMP受体表达和应答,观察到分化的外细胞层显示出模式识别受体(pattern-recognition receptors, PRRs)的低表达并且缺乏MAMP应答。然而,这些细胞可以通过邻近细胞的损伤被门控以变得有反应。并且得知局部损伤也导致对原本非免疫原性的有益细菌的免疫反应。尽管土壤中存在微生物模式,本研究还发现,细胞损伤可以启动局部免疫反应,这将有助于从理论上提出如何利用MAMP。这些发现为非致病性微生物如何通过简单地避免损伤和随之而来的免疫反应的强烈增强,而成功地在根中定植提供了一个全新层次的解释。
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Cell [IF:38.637]

Co-incidence of Damage and Microbial Patterns Controls Localized Immune Responses in Roots

损伤和微生物模式的共同作用控制着根部的局部免疫反应

10.1016/j.cell.2020.01.013

02-06, Article

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Recognition of microbe-associated molecular patterns (MAMPs) is crucial for the plant’s immune response. How this sophisticated perception system can be usefully deployed in roots, continuously exposed to microbes, remains a mystery. By analyzing MAMP receptor expression and response at cellular resolution in Arabidopsis, we observed that differentiated outer cell layers show low expression of pattern-recognition receptors (PRRs) and lack MAMP responsiveness. Yet, these cells can be gated to become responsive by neighbor cell damage. Laser ablation of small cell clusters strongly upregulates PRR expression in their vicinity, and elevated receptor expression is sufficient to induce responsiveness in non-responsive cells. Finally, localized damage also leads to immune responses to otherwise non-immunogenic, beneficial bacteria. Damage-gating is overridden by receptor overexpression, which antagonizes colonization. Our findings that cellular damage can “switch on” local immune responses helps to conceptualize how MAMP perception can be used despite the presence of microbial patterns in the soil.

First Authors:
Feng Zhou

Correspondence Authors:
Feng Zhou,Niko Geldner

All Authors:
Feng Zhou,Aurélia Emonet,Valérie Dénervaud Tendon,Peter Marhavy,Dousheng Wu,Thomas Lahaye,Niko Geldner

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