Functional Variation of Plant–Pathogen Interactions: New Concept and Methods for Virulence Data Analyses

2019 - Vol. 109 - n° 8 - pag. 1324-1330
Authors: E. Kosman , X. Chen , A. Dreiseitl , B. McCallum , A. Lebeda , P. Ben-Yehuda , E. Gultyaeva , and J. Manisterski

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.