1Institute for Cereal Crops Improvement, Tel Aviv University, Tel Aviv 69978, Israel.
2United States Department of Agriculture-Agricultural Research Service Wheat Health, Genetics, and Quality Research Unit, and Washington State University, Pullman, WA 99164-6430, U.S.A.
Phytopathology. 2019 Aug;109(8):1324-1330. doi: 10.1094/PHYTO-02-19-0041-LE. Epub 2019 Jun 25.
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.
经典的毒力分析基于发现分离株的毒力表型，这些表型涉及在一组不同的宿主基因型中抗性基因的组成。基于这种观点，毒力表型通常以遗传方式作为两个可能的等位基因之一来处理，在二元基因座中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在多个基因座分析毒力数据的流行工具。然而，解决二元毒力表型的基础是宿主-病原体相互作用的感染类型（IT）数据，该数据表达了每个特定分离株在特定情况下（特定宿主、环境条件、栽培实践等）的功能特征。IT 是由观察到的症状和体征决定的（例如，病变类型、病变大小、菌丝覆盖叶片或叶片段的程度、孢子产生等），并通过每个植物-病原体系统的通用量表中的 IT 评分来评估。因此，获得了分离物的多个 IT 图谱，并可以对病原体的操作单位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功能（例如环境）成分，除了遗传成分。开发了使用 IT 数据测量植物-病原体相互作用的功能变异的新方法。该方法需要一个适当的评估量表和对每个植物-病原体系统的 IT 评分之间差异的专家估计（提供了一个示例）。在不同层次结构水平上分析了几个数据集，结果表明 IT 表型与二元毒力表型之间的结果存在差异。基于 IT 测量功能变异的能力有望成为研究植物病原体引起的流行病的有效工具。
