Genetic analysis reveals long-standing population differentiation and high diversity in the rust pathogen Melampsora lini.

A priority for research on infectious disease is to understand how epidemiological and evolutionary processes interact to influence pathogen population dynamics and disease outcomes. However, little is understood about how population adaptation changes across time, how sexual vs. asexual reproduction contribute to the spread of pathogens in wild populations and how diversity measured with neutral and selectively important markers correlates across years. Here, we report results from a long-term study of epidemiological and genetic dynamics within several natural populations of the Linum marginale-Melampsora lini plant-pathogen interaction. Using pathogen isolates collected from three populations of wild flax (L. marginale) spanning 16 annual epidemics, we probe links between pathogen population dynamics, phenotypic variation for infectivity and genomic polymorphism. Pathogen genotyping was performed using 1567 genome-wide SNP loci and sequence data from two infectivity loci (AvrP123, AvrP4). Pathogen isolates were phenotyped for infectivity using a differential set. Patterns of epidemic development were assessed by conducting surveys of infection prevalence in one population (Kiandra) annually. Bayesian clustering analyses revealed host population and ecotype as key predictors of pathogen genetic structure. Despite strong fluctuations in pathogen population size and severe annual bottlenecks, analysis of molecular variance revealed that pathogen population differentiation was relatively stable over time. Annually, varying levels of clonal spread (0-44.8%) contributed to epidemics. However, within populations, temporal genetic composition was dynamic with rapid turnover of pathogen genotypes, despite the dominance of only four infectivity phenotypes across the entire study period. Furthermore, in the presence of strong fluctuations in population size and migration, spatial selection may maintain pathogen populations that, despite being phenotypically stable, are genetically highly dynamic.

Metapopulation Structure Predicts Population Dynamics in the Cakile maritima-Alternaria brassicicola Host-Pathogen Interaction.

AbstractIn symbiotic interactions, spatiotemporal variation in the distribution or population dynamics of one species represents spatial and temporal heterogeneity of the landscape for the other. Such interdependent demographic dynamics result in situations where the relative importance of biotic and abiotic factors in determining ecological processes is complicated to decipher. Using a detailed survey of three metapopulations of the succulent plant Cakile maritima and the necrotrophic fungus Alternaria brassicicola located along the southeastern Australian coast, we developed a series of statistical analyses-namely, synchrony analysis, patch occupancy dynamics, and a spatially explicit metapopulation model-to understand how habitat quality, weather conditions, dispersal, and spatial structure determine metapopulation dynamics. Climatic conditions are important drivers, likely explaining the high synchrony among populations. Host availability, landscape features facilitating dispersal, and habitat conditions also impact the occurrence and spread of disease. Overall, we show that the collection of extensive data on host and pathogen population dynamics, in combination with spatially explicit epidemiological modeling, makes it possible to accurately predict disease dynamics-even when there is extreme variability in host population dynamics. Finally, we discuss the importance of genetic information for predicting demographic dynamics in this pathosystem.

Phylogenetic signals and predictability in plant-soil feedbacks.

There is strong evidence for a phylogenetic signal in the degree to which species share co-evolved biotic partners and in the outcomes of biotic interactions. This implies there should be a phylogenetic signal in the outcome of feedbacks between plants and the soil microbiota they cultivate. However, attempts to identify a phylogenetic signal in plant-soil feedbacks have produced mixed results. Here we clarify how phylogenetic signals could arise in plant-soil feedbacks and use a recent compilation of data from feedback experiments to identify: whether there is a phylogenetic signal in the outcome of plant-soil feedbacks; and whether any signal arises through directional or divergent changes in feedback outcomes with evolutionary time. We find strong evidence for a divergent phylogenetic signal in feedback outcomes. Distantly related plant species show more divergent responses to each other's soil microbiota compared with closely related plant species. The pattern of divergence implies occasional co-evolutionary shifts in how plants interact with soil microbiota, with strongly contrasting feedback responses among some plant lineages. Our results highlight that it is difficult to predict feedback outcomes from phylogeny alone, other than to say that more closely related species tend to have more similar responses.

Novel model-based clustering reveals ecologically differentiated bacterial genomes across a large climate gradient.

A pervasive challenge in microbial ecology is understanding the genetic level where ecological units can be differentiated. Ecological differentiation often occurs at fine genomic levels, yet it is unclear how to utilise ecological information to define ecotypes given the breadth of environmental variation among microbial taxa. Here, we present an analytical framework that infers clusters along genome-based microbial phylogenies according to shared environmental responses. The advantage of our approach is the ability to identify genomic clusters that best fit complex environmental information whilst characterising cluster niches through model predictions. We apply our method to determine climate-associated ecotypes in populations of nitrogen-fixing symbionts using whole genomes, explicitly sampled to detect climate differentiation across a heterogeneous landscape. Although soil and plant host characteristics strongly influence distribution patterns of inferred ecotypes, our flexible statistical method enabled us to identify climate-associated genomic clusters using environmental data, providing solid support for ecological specialisation in soil symbionts.

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

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges. Therefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters. This article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies.

The emerging science of linked plant-fungal invasions.

Contents 1314 I. 1315 II. 1316 III. 1322 IV. 1323 V. 1325 VI. 1326 VII. 1326 VIII. 1327 1328 References 1328 SUMMARY: Invasions of alien plants are typically studied as invasions of individual species, yet interactions between plants and symbiotic fungi (mutualists and potential pathogens) affect plant survival, physiological traits, and reproduction and hence invasion success. Studies show that plant-fungal associations are frequently key drivers of plant invasion success and impact, but clear conceptual frameworks and integration across studies are needed to move beyond a series of case studies towards a more predictive understanding. Here, we consider linked plant-fungal invasions from the perspective of plant and fungal origin, simplified to the least complex representations or 'motifs'. By characterizing these interaction motifs, parallels in invasion processes between pathogen and mutualist fungi become clear, although the outcomes are often opposite in effect. These interaction motifs provide hypotheses for fungal-driven dynamics behind observed plant invasion trajectories. In some situations, the effects of plant-fungal interactions are inconsistent or negligible. Variability in when and where different interaction motifs matter may be driven by specificity in the plant-fungal interaction, the size of the effect of the symbiosis (negative to positive) on plants and the dependence (obligate to facultative) of the plant-fungal interaction. Linked plant-fungal invasions can transform communities and ecosystem function, with potential for persistent legacies preventing ecosystem restoration.

Trade-offs and evolution of thermal adaptation in the Irish potato famine pathogen Phytophthora infestans.

Temperature is one of the most important environmental parameters with crucial impacts on nearly all biological processes. Due to anthropogenic activity, average air temperatures are expected to increase by a few degrees in coming decades, accompanied by an increased occurrence of extreme temperature events. Such global trends are likely to have various major impacts on human society through their influence on natural ecosystems, food production and biotic interactions, including diseases. In this study, we used a combination of statistical genetics, experimental evolution and common garden experiments to investigate the evolutionary potential for thermal adaptation in the potato late blight pathogen, Phytophthora infestans, and infer its likely response to changing temperatures. We found a trade-off associated with thermal adaptation to heterogeneous environments in P. infestans, with the degree of the trade-off peaking approximately at the pathogen's optimum growth temperature. A genetic trade-off in thermal adaptation was also evidenced by the negative association between a strain's growth rate and its thermal range for growth, and warm climates selecting for a low pathogen growth rate. We also found a mirror effect of phenotypic plasticity and genetic adaptation on growth rate. At below the optimum, phenotypic plasticity enhances pathogen's growth rate but nature selects for slower growing genotypes when temperature increases. At above the optimum, phenotypic plasticity reduces pathogen's growth rate but natural selection favours for faster growing genotypes when temperature increases further. We conclude from these findings that the growth rate of P. infestans will only be marginally affected by global warming.

Addressing the Challenges of Pathogen Evolution on the World's Arable Crops.

Advances in genomic and molecular technologies coupled with an increasing understanding of the fine structure of many resistance and infectivity genes, have opened up a new era of hope in controlling the many plant pathogens that continue to be a major source of loss in arable crops. Some new approaches are under consideration including the use of nonhost resistance and the targeting of critical developmental constraints. However, the major thrust of these genomic and molecular approaches is to enhance the identification of resistance genes, to increase their ease of manipulation through marker and gene editing technologies and to lock a range of resistance genes together in simply manipulable resistance gene cassettes. All these approaches essentially continue a strategy that assumes the ability to construct genetic-based resistance barriers that are insurmountable to target pathogens. Here we show how the recent advances in knowledge and marker technologies can be used to generate more durable disease resistance strategies that are based on broad evolutionary principles aimed at presenting pathogens with a shifting, landscape of fluctuating directional selection.

Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters.

Network and multivariate statistical analyses were performed to determine interactions between bacterial and fungal community terminal restriction length polymorphisms as well as soil properties in paired woodland and pasture sites. Canonical correspondence analysis (CCA) revealed that shifts in woodland community composition correlated with soil dissolved organic carbon, while changes in pasture community composition correlated with moisture, nitrogen and phosphorus. Weighted correlation network analysis detected two distinct microbial modules per land use. Bacterial and fungal ribotypes did not group separately, rather all modules comprised of both bacterial and fungal ribotypes. Woodland modules had a similar fungal : bacterial ribotype ratio, while in the pasture, one module was fungal dominated. There was no correspondence between pasture and woodland modules in their ribotype composition. The modules had different relationships to soil variables, and these contrasts were not detected without the use of network analysis. This study demonstrated that fungi and bacteria, components of the soil microbial communities usually treated as separate functional groups as in a CCA approach, were co-correlated and formed distinct associations in these adjacent habitats. Understanding these distinct modular associations may shed more light on their niche space in the soil environment, and allow a more realistic description of soil microbial ecology and function.

C/N ratio drives soil actinobacterial cellobiohydrolase gene diversity.

Cellulose accounts for approximately half of photosynthesis-fixed carbon; however, the ecology of its degradation in soil is still relatively poorly understood. The role of actinobacteria in cellulose degradation has not been extensively investigated despite their abundance in soil and known cellulose degradation capability. Here, the diversity and abundance of the actinobacterial glycoside hydrolase family 48 (cellobiohydrolase) gene in soils from three paired pasture-woodland sites were determined by using terminal restriction fragment length polymorphism (T-RFLP) analysis and clone libraries with gene-specific primers. For comparison, the diversity and abundance of general bacteria and fungi were also assessed. Phylogenetic analysis of the nucleotide sequences of 80 clones revealed significant new diversity of actinobacterial GH48 genes, and analysis of translated protein sequences showed that these enzymes are likely to represent functional cellobiohydrolases. The soil C/N ratio was the primary environmental driver of GH48 community compositions across sites and land uses, demonstrating the importance of substrate quality in their ecology. Furthermore, mid-infrared (MIR) spectrometry-predicted humic organic carbon was distinctly more important to GH48 diversity than to total bacterial and fungal diversity. This suggests a link between the actinobacterial GH48 community and soil organic carbon dynamics and highlights the potential importance of actinobacteria in the terrestrial carbon cycle.

Below-ground abiotic and biotic heterogeneity shapes above-ground infection outcomes and spatial divergence in a host-parasite interaction.

We investigated the impact of below-ground and above-ground environmental heterogeneity on the ecology and evolution of a natural plant-pathogen interaction. We combined field measurements and a reciprocal inoculation experiment to investigate the potential for natural variation in abiotic and biotic factors to mediate infection outcomes in the association between the fungal pathogen Melampsora lini and its wild flax host, Linum marginale, where pathogen strains and plant lines originated from two ecologically distinct habitat types that occur in close proximity ('bog' and 'hill'). The two habitat types differed strikingly in soil moisture and soil microbiota. Infection outcomes for different host-pathogen combinations were strongly affected by the habitat of origin of the plant lines and pathogen strains, the soil environment and their interactions. Our results suggested that tradeoffs play a key role in explaining the evolutionary divergence in interaction traits among the two habitat types. Overall, we demonstrate that soil heterogeneity, by mediating infection outcomes and evolutionary divergence, can contribute to the maintenance of variation in resistance and pathogenicity within a natural host-pathogen metapopulation.

Evolution of pathogen specialisation in a host metapopulation: joint effects of host and pathogen dispersal.

Metapopulation processes are important determinants of epidemiological and evolutionary dynamics in host-pathogen systems, and are therefore central to explaining observed patterns of disease or genetic diversity. In particular, the spatial scale of interactions between pathogens and their hosts is of primary importance because migration rates of one species can affect both spatial and temporal heterogeneity of selection on the other. In this study we developed a stochastic and discrete time simulation model to specifically examine the joint effects of host and pathogen dispersal on the evolution of pathogen specialisation in a spatially explicit metapopulation. We consider a plant-pathogen system in which the host metapopulation is composed of two plant genotypes. The pathogen is dispersed by air-borne spores on the host metapopulation. The pathogen population is characterised by a single life-history trait under selection, the infection efficacy. We found that restricted host dispersal can lead to high amount of pathogen diversity and that the extent of pathogen specialisation varied according to the spatial scale of host-pathogen dispersal. We also discuss the role of population asynchrony in determining pathogen evolutionary outcomes.

Host ecotype generates evolutionary and epidemiological divergence across a pathogen metapopulation.

The extent and speed at which pathogens adapt to host resistance varies considerably. This presents a challenge for predicting when--and where--pathogen evolution may occur. While gene flow and spatially heterogeneous environments are recognized to be critical for the evolutionary potential of pathogen populations, we lack an understanding of how the two jointly shape coevolutionary trajectories between hosts and pathogens. The rust pathogen Melampsora lini infects two ecotypes of its host plant Linum marginale that occur in close proximity yet in distinct populations and habitats. In this study, we found that within-population epidemics were different between the two habitats. We then tested for pathogen local adaptation at host population and ecotype level in a reciprocal inoculation study. Even after controlling for the effect of spatial structure on infection outcome, we found strong evidence of pathogen adaptation at the host ecotype level. Moreover, sequence analysis of two pathogen infectivity loci revealed strong genetic differentiation by host ecotype but not by distance. Hence, environmental variation can be a key determinant of pathogen population genetic structure and coevolutionary dynamics and can generate strong asymmetry in infection risks through space.

Intramolecular interaction influences binding of the Flax L5 and L6 resistance proteins to their AvrL567 ligands.

L locus resistance (R) proteins are nucleotide binding (NB-ARC) leucine-rich repeat (LRR) proteins from flax (Linum usitatissimum) that provide race-specific resistance to the causal agent of flax rust disease, Melampsora lini. L5 and L6 are two alleles of the L locus that directly recognize variants of the fungal effector AvrL567. In this study, we have investigated the molecular details of this recognition by site-directed mutagenesis of AvrL567 and construction of chimeric L proteins. Single, double and triple mutations of polymorphic residues in a variety of AvrL567 variants showed additive effects on recognition strength, suggesting that multiple contact points are involved in recognition. Domain-swap experiments between L5 and L6 show that specificity differences are determined by their corresponding LRR regions. Most positively selected amino acid sites occur in the N- and C-terminal LRR units, and polymorphisms in the first seven and last four LRR units contribute to recognition specificity of L5 and L6 respectively. This further confirms that multiple, additive contact points occur between AvrL567 variants and either L5 or L6. However, we also observed that recognition of AvrL567 is affected by co-operative polymorphisms between both adjacent and distant domains of the R protein, including the TIR, ARC and LRR domains, implying that these residues are involved in intramolecular interactions to optimize detection of the pathogen and defense signal activation. We suggest a model where Avr ligand interaction directly competes with intramolecular interactions to cause activation of the R protein.

Microbial phylotype composition and diversity predicts plant productivity and plant-soil feedbacks.

The relationship between ecological variation and microbial genetic composition is critical to understanding microbial influence on community and ecosystem function. In glasshouse trials using nine native legume species and 40 rhizobial strains, we find that bacterial rRNA phylotype accounts for 68% of amoung isolate variability in symbiotic effectiveness and 79% of host specificity in growth response. We also find that rhizobial phylotype diversity and composition of soils collected from a geographical breadth of sites explains the growth responses of two acacia species. Positive soil microbial feedback between the two acacia hosts was largely driven by changes in diversity of rhizobia. Greater rhizobial diversity accumulated in association with the less responsive host species, Acacia salicina, and negatively affected the growth of the more responsive Acacia stenophylla. Together, this work demonstrates correspondence of phylotype with microbial function, and demonstrates that the dynamics of rhizobia on host species can feed back on plant population performance.

Microbial community responses to anthropogenically induced environmental change: towards a systems approach.

The soil environment is essential to many ecosystem services which are primarily mediated by microbial communities. Soil physical and chemical conditions are altered on local and global scales by anthropogenic activity and which threatens the provision of many soil services. Despite the importance of soil biota for ecosystem function, we have limited ability to predict and manage soil microbial community responses to change. To better understand causal relationships between microbial community structure and ecological function, we argue for a systems approach to prediction and management of microbial response to environmental change. This necessitates moving beyond concepts of resilience, resistance and redundancy that assume single optimum stable states, to ones that better reflect the dynamic and interactive nature of microbial systems. We consider the response of three soil groups (ammonia oxidisers, denitrifiers, symbionts) to anthropogenic perturbation to motivate our discussion. We also present a network re-analysis of a saltmarsh microbial community which illustrates how such approaches can reveal ecologically important connections between functional groups. More generally, we suggest the need for integrative studies which consider how environmental variables moderate interactions between functional groups, how this moderation affects biogeochemical processes and how these feedbacks ultimately drive ecosystem services provided by soil biota.

Rapid genetic change underpins antagonistic coevolution in a natural host-pathogen metapopulation.

Antagonistic coevolution is a critical force driving the evolution of diversity, yet the selective processes underpinning reciprocal adaptive changes in nature are not well understood. Local adaptation studies demonstrate partner impacts on fitness and adaptive change, but do not directly expose genetic processes predicted by theory. Specifically, we have little knowledge of the relative importance of fluctuating selection vs. arms-race dynamics in maintaining polymorphism in natural systems where metapopulation processes predominate. We conducted cross-year epidemiological, infection and genetic studies of multiple wild host and pathogen populations in the Linum-Melampsora association. We observed asynchronous phenotypic fluctuations in resistance and infectivity among demes. Importantly, changes in allelic frequencies at pathogen infectivity loci, and in host recognition of these genetic variants, correlated with disease prevalence during natural epidemics. These data strongly support reciprocal coevolution maintaining balanced resistance and infectivity polymorphisms, and highlight the importance of characterising spatial and temporal dynamics in antagonistic interactions.

Trophic network structure emerges through antagonistic coevolution in temporally varying environments.

Understanding the mechanisms underlying ecological specialization is central to our understanding of community ecology and evolution. Although theoretical work has investigated how variable environments may affect specialization in single species, little is known about how such variation impacts bipartite network structure in antagonistically coevolving systems. Here, we develop and analyse a general model of victim-enemy coevolution that explicitly includes resource and population dynamics. We investigate how temporal environmental heterogeneity affects the evolution of specialization and associated community structure. Environmental productivity influences victim investment in resistance, which will shape patterns of specialization through its regulating effect on enemy investment in infectivity. We also investigate the epidemiological consequences of environmental variability and show that enemy population density is maximized for intermediate lengths of productive seasons, which corresponds to situations where enemies can evolve higher infectivity than victims can evolve defence. We discuss our results in the light of empirical studies, and further highlight ways in which our model applies to a range of natural systems.