Functional Variation of Plant-Pathogen Interactions: New Concept and Methods for Virulence Data Analyses
Functional Variation of Plant-Pathogen Interactions: New Concept and Methods for Virulence Data Analyses
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DOI：
10.1094/phyto-02-19-0041-le
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发表时间：
2019-08-01
期刊：
影响因子：
3.2
通讯作者：
Manisterski, J.
中科院分区：
文献类型：
--
作者：
Kosman, E.;Chen, X.;Manisterski, J.
经典的毒力分析是基于在一组不同的寄主基因型中发现与抗性基因组成有关的分离物的毒力表型。有了这样的观点，毒力表型通常以遗传方式被视为两种可能的等位基因之一，在一个双基因座上，要么是毒力，要么是无毒。因此，群体遗传学指标和方法已成为分析多个基因座毒力数据的主流工具。然而，解析二元毒力表型的基础是宿主-病原菌相互作用的感染类型(IT)数据，这些数据表达了每个特定分离物在特定情况下(特定宿主、环境条件、栽培实践等)的功能特征。它是由观察到的症状和体征(例如，病斑类型、病斑大小、菌丝覆盖叶片或叶段、产孢量等)决定的，并以每种植物-病原菌系统普遍接受的IT评分进行评估。因此，获得了分离株的多个IT图谱，并且可以对病原体的操作单元内和之间的功能变异进行分析。这种方法可以更好地利用原始数据中可用的信息，并揭示病原体变异的一个功能(例如，环境)成分，而不是遗传成分。发展了利用IT数据测量植物-病原菌相互作用的功能变异的新方法。这些方法需要一个适当的评价表和专家对每个植物-病原菌系统的IT得分之间的差异进行估计(给出了一个例子)。对不同等级的几个数据集的分析表明，IT表型与二元毒力表型获得的结果存在差异。测量基于信息技术的功能性变异的能力为研究植物病原体引起的流行病提供了一种有效的工具。
Classical virulence analysis is based on discovering virulence phenotypes of isolates with regard to a composition of resistance genes in a differential set of host genotypes. With such a vision, virulence phenotypes are usually treated in a genetic manner as one of two possible alleles, either virulence or avirulence in a binary locus. Therefore, population genetics metrics and methods have become prevailing tools for analyzing virulence data at multiple loci. However, a basis for resolving binary virulence phenotypes is infection type (IT) data of host-pathogen interaction that express functional traits of each specific isolate in a given situation (particular host, environmental conditions, cultivation practice, and so on). IT is determined by symptoms and signs observed (e.g., lesion type, lesion size, coverage of leaf or leaf segments by mycelium, spore production and so on), and assessed by IT scores at a generally accepted scale for each plant-pathogen system. Thus, multiple IT profiles of isolates are obtained and can be subjected to analysis of functional variation within and among operational units of a pathogen. Such an approach may allow better utilization of the information available in the raw data, and reveal a functional (e.g., environmental) component of pathogen variation in addition to the genetic one. New methods for measuring functional variation of plant-pathogen interaction with IT data were developed. The methods need an appropriate assessment scale and expert estimations of dissimilarity between IT scores for each plant-pathogen system (an example is presented). Analyses of a few data sets at different hierarchical levels demonstrated discrepancies in results obtained with IT phenotypes versus binary virulence phenotypes. The ability to measure functional IT-based variation offers promise as an effective tool in the study of epidemics caused by plant pathogens.