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本文引用的文献

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Rapid identification of an Arabidopsis NLR gene as a candidate conferring susceptibility to Sclerotinia sclerotiorum using time-resolved automated phenotyping.利用时间分辨自动化表型分析快速鉴定拟南芥 NLR 基因作为对核盘菌感病的候选基因。
Plant J. 2020 Jul;103(2):903-917. doi: 10.1111/tpj.14747. Epub 2020 Apr 21.
2
Reconstitution and structure of a plant NLR resistosome conferring immunity.植物 NLR 抗病体的重建与结构赋予免疫性。
Science. 2019 Apr 5;364(6435). doi: 10.1126/science.aav5870.
3
Integrative network-centric approach reveals signaling pathways associated with plant resistance and susceptibility to Pseudomonas syringae.综合网络中心方法揭示了与拟南芥对丁香假单胞菌抗性和易感性相关的信号通路。
PLoS Biol. 2018 Dec 12;16(12):e2005956. doi: 10.1371/journal.pbio.2005956. eCollection 2018 Dec.
4
The BioGRID interaction database: 2019 update.生物相互作用数据库(BioGRID):2019 年更新版。
Nucleic Acids Res. 2019 Jan 8;47(D1):D529-D541. doi: 10.1093/nar/gky1079.
5
Proteasome-associated HECT-type ubiquitin ligase activity is required for plant immunity.蛋白酶体相关的 HECT 型泛素连接酶活性对于植物免疫是必需的。
PLoS Pathog. 2018 Nov 20;14(11):e1007447. doi: 10.1371/journal.ppat.1007447. eCollection 2018 Nov.
6
Transcriptome analysis reveals the molecular mechanisms of the defense response to gray leaf spot disease in maize.转录组分析揭示了玉米对灰斑病防御反应的分子机制。
BMC Genomics. 2018 Oct 11;19(1):742. doi: 10.1186/s12864-018-5072-4.
7
Fungal resistance mediated by maize wall-associated kinase ZmWAK-RLK1 correlates with reduced benzoxazinoid content.玉米细胞壁相关激酶 ZmWAK-RLK1 介导的真菌抗性与降低的苯并恶嗪类含量相关。
New Phytol. 2019 Jan;221(2):976-987. doi: 10.1111/nph.15419. Epub 2018 Sep 4.
8
Transcriptome dynamics associated with resistance and susceptibility against fusarium head blight in four wheat genotypes.转录组动态与四种小麦基因型对赤霉病抗性和易感性的关系。
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9
Receptor networks underpin plant immunity.受体网络是植物免疫的基础。
Science. 2018 Jun 22;360(6395):1300-1301. doi: 10.1126/science.aat2623.
10
Network biology discovers pathogen contact points in host protein-protein interactomes.网络生物学发现宿主蛋白-蛋白互作组中的病原体接触点。
Nat Commun. 2018 Jun 13;9(1):2312. doi: 10.1038/s41467-018-04632-8.

植物定量疾病抗性的稳健性是由分散的免疫网络提供的。

Robustness of plant quantitative disease resistance is provided by a decentralized immune network.

机构信息

Laboratoire des Interactions Plantes-Microorganismes, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, CNRS, Université de Toulouse, 31326 Castanet-Tolosan, France.

KWS SAAT SE & Co, 37574 Einbeck, Germany.

出版信息

Proc Natl Acad Sci U S A. 2020 Jul 28;117(30):18099-18109. doi: 10.1073/pnas.2000078117. Epub 2020 Jul 15.

DOI:10.1073/pnas.2000078117
PMID:32669441
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7395444/
Abstract

Quantitative disease resistance (QDR) represents the predominant form of resistance in natural populations and crops. Surprisingly, very limited information exists on the biomolecular network of the signaling machineries underlying this form of plant immunity. This lack of information may result from its complex and quantitative nature. Here, we used an integrative approach including genomics, network reconstruction, and mutational analysis to identify and validate molecular networks that control QDR in in response to the bacterial pathogen To tackle this challenge, we first performed a transcriptomic analysis focused on the early stages of infection and using transgenic lines deregulated for the expression of , a gene underlying a QTL conferring quantitative and broad-spectrum resistance to -dependent gene expression was shown to involve multiple cellular activities (signaling, transport, and metabolism processes), mainly distinct from effector-triggered immunity (ETI) and pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) responses already characterized in Protein-protein interaction network reconstitution then revealed a highly interconnected and distributed RKS1-dependent network, organized in five gene modules. Finally, knockout mutants for 41 genes belonging to the different functional modules of the network revealed that 76% of the genes and all gene modules participate partially in RKS1-mediated resistance. However, these functional modules exhibit differential robustness to genetic mutations, indicating that, within the decentralized structure of the QDR network, some modules are more resilient than others. In conclusion, our work sheds light on the complexity of QDR and provides comprehensive understanding of a QDR immune network.

摘要

定量疾病抗性 (QDR) 是自然种群和作物中主要的抗性形式。令人惊讶的是,关于这种植物免疫形式的信号机制的生物分子网络的信息非常有限。这种信息的缺乏可能是由于其复杂和定量的性质。在这里,我们使用了一种整合的方法,包括基因组学、网络重建和突变分析,来识别和验证控制 QDR 的分子网络,以响应细菌病原体 。为了应对这一挑战,我们首先进行了一项转录组分析,重点关注感染的早期阶段,并使用转基因系对 进行表达调控,该基因是一个 QTL 的基础,赋予对 的定量和广谱抗性。依赖基因表达被证明涉及多种细胞活动(信号转导、运输和代谢过程),主要与已经在 中表征的效应子触发免疫 (ETI) 和病原体相关分子模式 (PAMP) 触发免疫 (PTI) 反应不同。然后,蛋白质-蛋白质相互作用网络重建揭示了一个高度互联和分布式的 RKS1 依赖性网络,组织在五个基因模块中。最后,属于网络不同功能模块的 41 个基因的 knockout 突变体表明,76%的基因和所有基因模块都部分参与了 RKS1 介导的抗性。然而,这些功能模块对遗传突变表现出不同的稳健性,表明在 QDR 网络的分散结构中,一些模块比其他模块更具弹性。总之,我们的工作揭示了 QDR 的复杂性,并提供了对 QDR 免疫网络的全面理解。