Combining Selective Pressures to Enhance the Durability of Disease Resistance Genes

Abstract

The efficacy of disease resistance genes in plants decreases over time because of the selection of virulent pathogen genotypes. A key goal of crop protection programs is to increase the durability of the resistance conferred by these genes. The spatial and temporal deployment of plant disease resistance genes is considered to be a major factor determining their durability. In the literature, four principal strategies combining resistance genes over time and space have been considered to delay the evolution of virulent pathogen genotypes. We reviewed this literature with the aim of determining which deployment strategy results in the greatest durability of resistance genes. Although theoretical and empirical studies comparing deployment strategies of more than one resistance gene are very scarce, they suggest that the overall durability of disease resistance genes can be increased by combining their presence in the same plant (pyramiding). Retrospective analyses of field monitoring data also suggest that the pyramiding of disease resistance genes within a plant is the most durable strategy. By extension, we suggest that the combination of disease resistance genes with other practices for pathogen control (pesticides, farming practices) may be a relevant management strategy to slow down the evolution of virulent pathogen genotypes.

Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?

“…Indeed, mosaics of different cultivars are equivalent to treatments of different fields or animals (including humans), crop rotations refer to the periodic application of molecules and pyramiding matches with the combination of molecules in a single treatment. Previous empirical and modelling studies have variously evaluated the performance of all these strategies in controlling pathogens, but few of these allowed direct comparisons between all possible categories of strategies (REX Consortium 2013, 2016). Recently, a global approach has been proposed to compare periodic applications, treatment of different patients and combination strategies for antibiotic treatment in hospitals (Tepekule, Uecker, Derungs, Frenoy, & Bonhoeffer, 2017).…”

“…As a complement to experimentation, modelling is a useful tool to compare the durability and epidemiological efficiency of different strategies and to explore the wide range of spatiotemporal deployment options. To date, no such global comparison, using a single eco‐evolutionary framework and standardized assumptions, exists (REX Consortium 2013, 2016). …”

Spatio‐temporal connectivity and host resistance influence evolutionary and epidemiological dynamics of the canola pathogen Leptosphaeria maculans

“…Clearly, pathogen evolutionary trajectories (i.e., responses to selection pressure) are at least partly determined by the pathotype composition and number of infectivity alleles carried by individuals in the initial pathogen population. For example, the superiority of pyramiding resistance genes over crop rotation schemes or mixtures depends on the absence of corresponding isolates carrying appropriate combinations of infectivity genes (Lof, de Vallavieille‐Pope, & van der Werf, 2017; REX consortium, 2016). While precise early characterization might be hampered by low initial frequencies of infective pathotypes, the ability to expose many local populations to different resistance combinations and compare pathogen adaptive responses may enable identification of the optimal deployment strategies for a given cropping region.…”

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

“…Only a few studies explicitly compare two types of strategies [46][47][48][49][50] and only two studies evaluated more than two strategies [51,52]. As a result, a comprehensive evaluation of different deployment schemes is complicated, and currently only feasible via pairwise comparisons [53,54]. The situation for quantitative resistance is similar, since often only one [28,34,41,42,55], two [36,37,56], or a combination [26,44,49] of pathogen aggressiveness components are targeted, although quantitative resistance can affect several life-history traits of the pathogen.…”