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

The proportion of resistant hosts in mixtures should be biased towards the resistance with the lowest breaking cost

Current agricultural practices facilitate emergence and spread of plant diseases through the wide use of monocultures. Host mixtures are a promising alternative for sustainable plant disease control. Their effectiveness can be partly explained by priming-induced cross-protection among plants. Priming occurs when plants are challenged with non-infective pathogen genotypes, resulting in increased resistance to subsequent infections by infective pathogen genotypes. We developed an epidemiological model to explore how mixing two distinct resistant varieties can reduce disease prevalence. We considered a pathogen population composed of three genotypes infecting either one or both varieties. We found that host mixtures should not contain an equal proportion of resistant plants, but a biased ratio (e.g. 80 : 20) to minimize disease prevalence. Counter-intuitively, the optimal ratio of resistant varieties should contain a lower proportion of the costliest resistance for the pathogen to break. This benefit is amplified by priming. This strategy also prevents the invasion of pathogens breaking all resistances.

Host mixtures for plant disease control: benefits from pathogen selection and immune priming

Multiline and cultivar mixtures are highly effective methods for agroecological plant disease control. Priming‐induced cross protection, occurring when plants are challenged by avirulent pathogen genotypes and resulting in increased resistance to subsequent infection by virulent ones, is one critical key to their lasting performance against polymorphic pathogen populations. Strikingly, this mechanism was until recently absent from mathematical models aiming at designing optimal host mixtures. We developed an epidemiological model to explore the effect of host mixtures composed of variable numbers of single‐resistance cultivars on the equilibrium prevalence of the disease caused by pathogen populations polymorphic for virulence complexity. This model shows that a relatively large amount of resistance genes must be deployed to achieve low disease prevalence, as pathogen competition in mixtures tends to select for intermediate virulence complexity. By contrast, priming significantly reduces the number of plant genotypes needed to drop disease prevalence below an acceptable threshold. Given the limited availability of resistance genes in cultivars, this mechanism of plant immunity should be assessed when designing host mixtures.

Taking Advantage of Pathogen Diversity and Immune Priming to Minimize Disease Prevalence in Host Mixtures: A Model

Host mixtures are a promising method for agroecological plant disease control. Plant immunity is key to the success of host mixtures against polymorphic pathogen populations. This immunity results from priming-induced cross-protection, whereby plants able to resist infection by specific pathogen genotypes become more resistant to other pathogen genotypes. Strikingly, this phenomenon was absent from mathematical models aiming at designing host mixtures. We developed a model to specifically explore how priming affects the coexistence of two pathogen genotypes in host mixtures composed of two host genotypes and how it affects disease prevalence. The main effect of priming is to reduce the coexistence region in the parameter space (due to the cross-protection) and to generate a singular mixture of resistant and susceptible hosts corresponding to the maximal reduction disease prevalence (in absence of priming, a resistant pure stand is optimal). The epidemiological advantage of host mixtures over a resistant pure stand thus appears as a direct consequence of immune priming. We also showed that there is indirect cross-protection between host genotypes in a mixture. Moreover, the optimal mix prevents the emergence of a resistance-breaking pathogen genotype. Our results highlight the importance of considering immune priming to design optimal and sustainable host mixtures.

Genetic diversity and gene flow amongst admixed populations of Ganoderma boninense, causal agent of basal stem rot in African oil palm (Elaeis guineensis Jacq.) in Sarawak (Malaysia), Peninsular Malaysia, and Sumatra (Indonesia)

In 1911 and 1917, the first commercial plantings of African oil palm (Elaeis guineensis Jacq.) were made in Indonesia and Malaysia in Southeast Asia. In less than 15 years, basal stem rot (BSR) was reported in Malaysia. It took nearly another seven decades to identify the main causal agent of BSR as the fungus, Ganoderma boninense. Since then, research efforts have focused on understanding G. boninense disease epidemiology, biology, and etiology, but limited progress was made to characterize pathogen genetic diversity, spatial structure, pathogenicity, and virulence. This study describes pathogen variability, gene flow, population differentiation, and genetic structure of G. boninense in Sarawak (Malaysia), Peninsular Malaysia, and Sumatra (Indonesia) inferred by 16 highly polymorphic cDNA-SSR (simple sequence repeat) markers. Marker-inferred genotypic diversity indicated a high level of pathogen variability among individuals within a population and among different populations. This genetic variability is clearly the result of outcrossing between basidiospores to produce recombinant genotypes. Although our results indicated high gene flow among the populations, there was no significant genetic differentiation among G. boninense populations on a regional scale. It suggested that G. boninense genetic makeup is similar across a wide region. Furthermore, our results revealed the existence of three admixed genetic clusters of G. boninense associated with BSR-diseased oil palms sampled throughout Sarawak, Peninsular Malaysia, and Sumatra. We postulate that the population structure is likely a reflection of the high genetic variability of G. boninense populations. This, in turn, could be explained by highly successful outcrossing between basidiospores of G. boninense from Southeast Asia and introduced genetic sources from various regions of the world, as well as regional adaptation of various pathogen genotypes to different palm hosts. Pathogen variability and population structure could be employed to deduce the epidemiology of G. boninense, as well as the implications of plantation cultural practices on BSR disease control in different regions.

A Landscape of Opportunities for Microbial Ecology Research

Microbes encompass tremendous biodiversity, provide support to all living forms, including humans, and play an important role in many ecosystem services. The rules that govern microorganism community assembly are increasingly revealed due to key advances in molecular and analytical methods but their understanding remain a key challenge in microbial ecology. The existence of biogeographic patterns within microbial communities has been established and explained in relation to landscape-scale processes, including selection, drift, dispersal and mutation. The effect of habitat patchiness on microorganisms' assembly rules remains though incompletely understood. Here, we review how landscape ecology principles can be adapted to explore new perspectives on the mechanisms that determine microbial community structure. To provide a general overview, we characterize microbial landscapes, the spatial and temporal scales of the mechanisms that drive microbial assembly and the feedback between microorganisms and landscape structure. We provide evidence for the effects of landscape heterogeneity, landscape fragmentation and landscape dynamics on microbial community structure, and show that predictions made for macro-organisms at least partly also apply to microorganisms. We explain why emerging metacommunity approaches in microbial ecology should include explicit characterization of landscape structure in their development and interpretation. We also explain how biotic interactions, such as competition, prey-predator or mutualist relations may influence the microbial landscape and may be involved in the above-mentioned feedback process. However, we argue that the application of landscape ecology to the microbial world cannot simply involve transposing existing theoretical frameworks. This is due to the particularity of these organisms, in terms of size, generation time, and for some of them, tight interaction with hosts. These characteristics imply dealing with unusual and dependent space and time scales of effect. Evolutionary processes have also a strong importance in microorganisms' response to their landscapes. Lastly, microorganisms' activity and distribution induce feedback effects on the landscape that have to be taken into account. The transposition of the landscape ecology framework to microorganisms provides many challenging research directions for microbial ecology.

Rotating and stacking genes can improve crop resistance durability while potentially selecting highly virulent pathogen strains

Rotating crop cultivars with different resistance genes could slow the evolution of virulent strains of fungal pathogens, but could also produce highly virulent pathogen strains. We present a new model that links polycyclic pathogen epidemiology and population genetics in order to predict how different strategies of rotating cultivars with different resistances will affect the evolution of pathogen virulence and the breakdown of crop resistance. We modelled a situation where there were four different resistance genes that can be deployed within each crop cultivar, and four virulence genes that may be present within the pathogen. We simulated four different rotational management strategies: (i) no rotation; (ii) a different gene every year; (iii) a different gene every 5 years; and (iv) a different combination of two stacked genes each year. Results indicate that rotating cultivars can lead to longer periods of disease suppression but also to the selection of highly virulent strains. The efficacy and relative advantage of different resistant cultivar rotation strategies depended on the fitness penalties, initial virulence allele frequencies, and ability of non-virulent pathogen genotypes to grow and reproduce on resistant cultivars. By capturing the essential processes involved, our model provides a useful new tool for investigating the evolutionary dynamics of pathogen virulence and crop resistance breakdown.

When Does Spatial Diversification Usefully Maximize the Durability of Crop Disease Resistance?

Maximizing the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialized. The factors that interact with and determine the magnitude of this spatial suppressive effect are not currently well understood, however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant cultivars, and the way in which this effect depends on the parameters governing the pathosystem of interest. Our multiseason semidiscrete epidemiological model tracks spatial spread of wild-type and resistance-breaking pathogen strains, and incorporates a localized reservoir of inoculum, as well as the effects of within and between field transmission. The pathogen dispersal characteristics, any fitness cost(s) of the resistance-breaking trait, the efficacy of host resistance, and the length of the timeframe of interest all influence the strength of the spatial diversification effect. A key result is that spatial diversification has the strongest beneficial effect at intermediate fitness costs of the resistance-breaking trait, an effect driven by a complex set of nonlinear interactions. On the other hand, however, if the resistance-breaking strain is not fit enough to invade the landscape, then a partially effective resistance gene can result in spatial diversification actually worsening the epidemic. These results allow us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modeling and pathosystem specific evaluation. These results highlight the importance of studying the effect of genetics on landscape scale spatial dynamics within host-pathogen disease systems.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Determination of the Key Resistance Gene Analogs Involved in Ascochyta rabiei Recognition in Chickpea

Chickpea (Cicer arietinum L.) is an important cool season food legume, however, its production is severely constrained by the foliar disease Ascochyta blight caused by the fungus Ascochyta rabiei (syn. Phoma rabiei). Several disease management options have been developed to control the pathogen, including breeding for host plant resistance. However, the pathogen population is evolving to produce more aggressive isolates. For host resistance to be effective, the plant must quickly recognize the pathogen and instigate initial defense mechanisms, optimally at the point of contact. Given that the most resistant host genotypes display rapid pathogen recognition and response, the approach taken was to assess the type, speed and pattern of recognition via Resistance Gene Analog (RGA) transcription among resistant and susceptible cultivated chickpea varieties. RGAs are key factors in the recognition of plant pathogens and the signaling of inducible defenses. In this study, a suite of RGA loci were chosen for further investigation from both published literature and from newly mined homologous sequences within the National Center for Biotechnology Information (NCBI) database. Following their validation in the chickpea genome, 10 target RGAs were selected for differential expression analysis in response to A. rabiei infection. This was performed in a set of four chickpea varieties including two resistant cultivars (ICC3996 and PBA Seamer), one moderately resistant cultivar (PBA HatTrick) and one susceptible cultivar (Kyabra). Gene expression at each RGA locus was assessed via qPCR at 2, 6, and 24 h after A. rabiei inoculation with a previously characterized highly aggressive isolate. As a result, all loci were differentially transcribed in response to pathogen infection in at least one genotype and at least one time point after inoculation. Among these, the differential expression of four RGAs was significant and consistently increased in the most resistant genotype ICC3996 immediately following inoculation, when spore germination began and ahead of penetration into the plant's epidermal tissues. Further in silico analyses indicated that the differentially transcribed RGAs function through ADP-binding within the pathogen recognition pathway. These represent clear targets for future functional validation and potential for selective resistance breeding for introgression into elite cultivars.