Functional variation of plant-pathogen interactions: new concept and methods for virulence data analyses

Authors: KOSMAN, E. - Tel Aviv University; Chen, Xianming; DREISEITL, A. - Agritec Plant Research Ltd; MCCALLUM, B. - Agriculture And Agri-Food Canada; LEBEDA, A. - Palacky University; BEN-YEHUDA, P. - Tel Aviv University; GULTYAEVA, E. - All-Russian Institute For Plant Protection; MANISTERSKI, J. - Tel Aviv University

Submitted to: Phytopathology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/2/2019
Publication Date: 4/8/2019
Citation: Kosman, E., Chen, X., Dreiseitl, A., McCallum, B., Lebeda, A., Ben-Yehuda, P., Gultyaeva, E., Manisterski, J. 2019. Functional variation of plant-pathogen interactions: new concept and methods for virulence data analyses. Phytopathology. 109(8):1324-1330. https://doi.org/10.1094/PHYTO-02-19-0041-LE.
DOI: https://doi.org/10.1094/PHYTO-02-19-0041-LE

Interpretive Summary: Classical virulence analysis is considered as discovering genetic patterns of isolates and their relatedness due to dependence of virulence phenotypes on a composition of resistance genes in a differential set of host plants. With such a vision, virulence data are usually treated in a genetic manner as two alleles of virulence and avirulence in a binary locus so that population genetics metrics and methods have become prevailing tools for virulence data analyses. 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. IT is determined by symptoms and signs observed and assessed by 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 approach may allow better utilization of information available in raw data, and reveal a functional component of pathogen variation in addition to the genetic one. New methods for measuring functional variation of plant-pathogen interactions based on IT data were developed. Analyses of a few data sets at different hierarchical levels demonstrated discrepancies in results obtained with IT versus virulence phenotypes. The ability to measure functional IT-based variation could be an effective tool in study of epidemics caused by plant pathogens.

Technical Abstract: Classical virulence analysis is considered as a kind of discovering genetic patterns of isolates and their relatedness due to dependence of virulence phenotypes on a composition of resistance genes in a differential set of host plants. With such a vision, virulence data are usually treated in a genetic manner as two alleles of virulence and avirulence in a binary locus so that population genetics metrics and methods have become prevailing tools for virulence data analyses. 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, etc.). IT is determined by symptoms and signs observed (e.g. lesion type, lesion size, coverage of leaf or leaf segments by mycelium, spore production etc.), 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 approach may allow better utilization of information available in 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 based on IT data were developed. They need a proper assessment scale and expert estimations of dissimilarity between IT scores for each plant-pathogen system (an example is shown). Analyses of a few data sets at different hierarchical levels demonstrated discrepancies in results obtained with IT versus virulence phenotypes. The ability to measure functional IT-based variation could be an effective tool in study of epidemics caused by plant pathogens.