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.

Mixed infections alter transmission potential in a fungal plant pathogen.

Infections by more than one strain of a pathogen predominate under natural conditions. Mixed infections can have significant, though often unpredictable, consequences for overall virulence, pathogen transmission and evolution. However, effects of mixed infection on disease development in plants often remain unclear and the critical factors that determine the outcome of mixed infections remain unknown. The fungus Zymoseptoria tritici forms genetically diverse infections in wheat fields. Here, for a range of pathogen traits, we experimentally decompose the infection process to determine how the outcomes and consequences of mixed infections are mechanistically realized. Different strains of Z. tritici grow in close proximity and compete in the wheat apoplast, resulting in reductions in growth of individual strains and in pathogen reproduction. We observed different outcomes of competition at different stages of the infection. Overall, more virulent strains had higher competitive ability during host colonization, and less virulent strains had higher transmission potential. We showed that within-host competition can have a major effect on infection dynamics and pathogen population structure in a pathogen and host genotype-specific manner. Consequently, mixed infections likely have a major effect on the development of septoria tritici blotch epidemics and the evolution of virulence in Z. tritici.

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.

Resistance to natural and synthetic gene drive systems.

Scientists are rapidly developing synthetic gene drive elements intended for release into natural populations. These are intended to control or eradicate disease vectors and pests, or to spread useful traits through wild populations for disease control or conservation purposes. However, a crucial problem for gene drives is the evolution of resistance against them, preventing their spread. Understanding the mechanisms by which populations might evolve resistance is essential for engineering effective gene drive systems. This review summarizes our current knowledge of drive resistance in both natural and synthetic gene drives. We explore how insights from naturally occurring and synthetic drive systems can be integrated to improve the design of gene drives, better predict the outcome of releases and understand genomic conflict in general.

The long-term maintenance of a resistance polymorphism through diffuse interactions.

Plant resistance (R) genes are a crucial component in plant defence against pathogens. Although R genes often fail to provide durable resistance in an agricultural context, they frequently persist as long-lived balanced polymorphisms in nature. Standard theory explains the maintenance of such polymorphisms through a balance of the costs and benefits of resistance and virulence in a tightly coevolving host-pathogen pair. However, many plant-pathogen interactions lack such specificity. Whether, and how, balanced polymorphisms are maintained in diffusely interacting species is unknown. Here we identify a naturally interacting R gene and effector pair in Arabidopsis thaliana and its facultative plant pathogen, Pseudomonas syringae. The protein encoded by the R gene RPS5 recognizes an AvrPphB homologue (AvrPphB2) and exhibits a balanced polymorphism that has been maintained for over 2 million years (ref. 3). Consistent with the presence of an ancient balanced polymorphism, the R gene confers a benefit when plants are infected with P. syringae carrying avrPphB2 but also incurs a large cost in the absence of infection. RPS5 alleles are maintained at intermediate frequencies in populations globally, suggesting ubiquitous selection for resistance. However, the presence of P. syringae carrying avrPphB is probably insufficient to explain the RPS5 polymorphism. First, avrPphB homologues occur at very low frequencies in P. syringae populations on A. thaliana. Second, AvrPphB only rarely confers a virulence benefit to P. syringae on A. thaliana. Instead, we find evidence that selection for RPS5 involves multiple non-homologous effectors and multiple pathogen species. These results and an associated model suggest that the R gene polymorphism in A. thaliana may not be maintained through a tightly coupled interaction involving a single coevolved R gene and effector pair. More likely, the stable polymorphism is maintained through complex and diffuse community-wide interactions.

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.

Gene drives in plants: opportunities and challenges for weed control and engineered resilience.

Plant species, populations and communities are under threat from climate change, invasive pathogens, weeds and habitat fragmentation. Despite considerable research effort invested in genome engineering for crop improvement, the development of genetic tools for the management of wild plant populations has rarely been given detailed consideration. Gene drive systems that allow direct genetic management of plant populations via the spread of fitness-altering genetic modifications could be of great utility. However, despite the rapid development of synthetic tools and their enormous promise, little explicit consideration has been given to their application in plants and, to date, they remain untested. This article considers the potential utility of gene drives for the management of wild plant populations, and examines the factors that might influence the design, spread and efficacy of synthetic drives. To gain insight into optimal ways to design and deploy synthetic drive systems, we investigate the diversity of mechanisms underlying natural gene drives and their dynamics within plant populations and species. We also review potential approaches for engineering gene drives and discuss their potential application to plant genomes. We highlight the importance of considering the impact of plant life-history and genetic architecture on the dynamics of drive, investigate the potential for different types of resistance evolution, and touch on the ethical, regulatory and social challenges ahead.

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.

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.

AgroEcoList 1.0: A checklist to improve reporting standards in ecological research in agriculture.

Many publications lack sufficient background information (e.g. location) to be interpreted, replicated, or reused for synthesis. This impedes scientific progress and the application of science to practice. Reporting guidelines (e.g. checklists) improve reporting standards. They have been widely taken up in the medical sciences, but not in ecological and agricultural research. Here, we use a community-centred approach to develop a reporting checklist (AgroEcoList 1.0) through surveys and workshops with 23 experts and the wider agroecological community. To put AgroEcoList in context, we also assessed the agroecological community's perception of reporting standards in agroecology. A total of 345 researchers, reviewers, and editors, responded to our survey. Although only 32% of respondents had prior knowledge of reporting guidelines, 76% of those that had said guidelines improved reporting standards. Overall, respondents agreed on the need of AgroEcolist 1.0; only 24% of respondents had used reporting guidelines before, but 78% indicated they would use AgroEcoList 1.0. We updated AgroecoList 1.0 based on respondents' feedback and user-testing. AgroecoList 1.0 consists of 42 variables in seven groups: experimental/sampling set-up, study site, soil, livestock management, crop and grassland management, outputs, and finances. It is presented here, and is also available on github (https://github.com/AgroecoList/Agroecolist). AgroEcoList 1.0 can serve as a guide for authors, reviewers, and editors to improve reporting standards in agricultural ecology. Our community-centred approach is a replicable method that could be adapted to develop reporting checklists in other fields. Reporting guidelines such as AgroEcoList can improve reporting standards and therefore the application of research to practice, and we recommend that they are adopted more widely in agriculture and ecology.

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.

Provenance of rhizobial symbionts is similar for invasive and noninvasive acacias introduced to California.

Plant-soil interactions can be important drivers of biological invasions. In particular, the symbiotic relationship between legumes and nitrogen-fixing soil bacteria (i.e. rhizobia) may be influential in invasion success. Legumes, including Australian acacias, have been introduced into novel ranges around the world. Our goal was to examine the acacia-rhizobia symbiosis to determine whether cointroduction of non-native mutualists plays a role in invasiveness of introduced legumes. To determine whether acacias were introduced abroad concurrently with native symbionts, we selected four species introduced to California (two invasive and two noninvasive in the region) and identified rhizobial strains associating with each species in their native and novel ranges. We amplified three genes to examine phylogenetic placement (16S rRNA) and provenance (nifD and nodC) of rhizobia associating with acacias in California and Australia. We found that all Acacia species, regardless of invasive status, are associating with rhizobia of Australian origin in their introduced ranges, indicating that concurrent acacia-rhizobia introductions have occurred for all species tested. Our results suggest that cointroduction of rhizobial symbionts may be involved in the establishment of non-native acacias in their introduced ranges, but do not contribute to the differential invasiveness of Acacia species introduced abroad.

Cheating, trade-offs and the evolution of aggressiveness in a natural pathogen population.

The evolutionary dynamics of pathogens are critically important for disease outcomes, prevalence and emergence. In this study we investigate ecological conditions that may promote the long-term maintenance of virulence polymorphisms in pathogen populations. Recent theory predicts that evolution towards increased virulence can be reversed if less-aggressive social 'cheats' exploit more aggressive 'cooperator' pathogens. However, there is no evidence that social exploitation operates within natural pathogen populations. We show that for the bacterium Pseudomonas syringae, major polymorphisms for pathogenicity are maintained at unexpectedly high frequencies in populations infecting the host Arabidopsis thaliana. Experiments reveal that less-aggressive strains substantially increase their growth potential in mixed infections and have a fitness advantage in non-host environments. These results suggest that niche differentiation can contribute to the maintenance of virulence polymorphisms, and that both within-host and between-host growth rates modulate cheating and cooperation in P. syringae populations.

Maintenance of variation in virulence and reproduction in populations of an agricultural plant pathogen.

Genetic diversity within pathogen populations is critically important for predicting pathogen evolution, disease outcomes and prevalence. However, we lack a good understanding of the processes maintaining genetic variation and constraints on pathogen life-history evolution. Here, we analysed interactions between 12 wheat host genotypes and 145 strains of Zymoseptoria tritici from five global populations to investigate the evolution and maintenance of variation in pathogen virulence and reproduction. We found a strong positive correlation between virulence (amount of leaf necrosis) and reproduction (pycnidia density within lesions), with substantial variation in both traits maintained within populations. On average, highly virulent isolates exhibited higher reproduction, which might increase transmission potential in agricultural fields planted to homogeneous hosts at a high density. We further showed that pathogen strains with a narrow host range (i.e. specialists) for reproduction were on average less virulent, and those with a broader host range (i.e. generalists) were on average less fecund on a given specific host. These costs associated with adaptation to different host genotypes might constrain the emergence of generalists by disrupting the directional evolution of virulence and fecundity. We conclude that selection favouring pathogen strains that are virulent across diverse hosts, coupled with selection that maximizes fecundity on specific hosts, may explain the maintenance of these pathogenicity traits within and among populations.

Gene drive strategies of pest control in agricultural systems: Challenges and opportunities.

Recent advances in gene-editing technologies have opened new avenues for genetic pest control strategies, in particular around the use of gene drives to suppress or modify pest populations. Significant uncertainty, however, surrounds the applicability of these strategies to novel target species, their efficacy in natural populations and their eventual safety and acceptability as control methods. In this article, we identify issues associated with the potential use of gene drives in agricultural systems, to control pests and diseases that impose a significant cost to agriculture around the world. We first review the need for innovative approaches and provide an overview of the most relevant biological and ecological traits of agricultural pests that could impact the outcome of gene drive approaches. We then describe the specific challenges associated with using gene drives in agricultural systems, as well as the opportunities that these environments may offer, focusing in particular on the advantages of high-threshold gene drives. Overall, we aim to provide a comprehensive view of the potential opportunities and the remaining uncertainties around the use of gene drives in agricultural systems.

Models of Plant Resistance Deployment.

Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

Diversity and evolution of effector loci in natural populations of the plant pathogen Melampsora lini.

Genetic variation for pathogen infectivity is an important driver of disease incidence and prevalence in both natural and managed systems. Here, we use the interaction between the rust pathogen, Melampsora lini, and two host plants, Linum marginale and Linum usitatissimum, to examine how host-pathogen interactions influence the maintenance of polymorphism in genes underlying pathogen virulence. Extensive sequence variation at two effector loci (AvrP123, AvrP4) was found in M. lini isolates collected from across the native range of L. marginale in Australia, as well as in isolates collected from a second host, the cultivated species L. usitatissimum. A highly significant excess of nonsynonymous compared with synonymous polymorphism was found at both loci, suggesting that diversifying selection is important for the maintenance of the observed sequence diversity. Agrobacterium-mediated transient transformation assays were used to demonstrate that variants of both the AvrP123 and AvrP4 genes are differentially recognized by resistance genes in L. marginale. We further characterized patterns of nucleotide variation at AvrP123 and AvrP4 in 10 local populations of M. lini infecting the wild host L. marginale. Populations were significantly differentiated with respect to allelic representation at the Avr loci, suggesting the possibility of local selection maintaining distinct genetic structures between pathogen populations, whereas limited diversity may be explained via selective sweeps and demographic bottlenecks. Together, these results imply that interacting selective and nonselective factors, acting across a broad range of scales, are important for the generation and maintenance of adaptively significant variation in populations of M. lini.

Gene technologies in weed management: a technical feasibility analysis.

With the advent of new genetic technologies such as gene silencing and gene drive, efforts to develop additional management tools for weed management is gaining significant momentum. These technologies promise novel ways to develop sustainable weed control options because gene silencing can switch-off genes mediating adaptation (e.g. growth, herbicide resistance), and gene drive can be used to spread modified traits and to engineer wild populations with reduced fitness. However, applying gene silencing and/or gene drive is expected to be inherently complex as their application is constrained by several methodological and technological difficulties. In this review we explore the challenges of these technologies, and discuss strategies and resources accessible to accelerate the development of gene-tech based tools for weed management. We also highlight how gene technologies can be integrated into existing management tactics such as classical biological control, and their possible interactions.

Positive selection in AvrP4 avirulence gene homologues across the genus Melampsora.

Pathogen genes involved in interactions with their plant hosts are expected to evolve under positive Darwinian selection or balancing selection. In this study a single copy avirulence gene, AvrP4, in the plant pathogen Melampsora lini, was used to investigate the evolution of such a gene across species. Partial translation elongation factor 1-alpha sequences were obtained to establish phylogenetic relationships among the Melampsora species. We amplified AvrP4 homologues from species pathogenic on hosts from different plant families and orders, across the inferred phylogeny. Translations of the AvrP4 sequences revealed a predicted signal peptide and towards the C-terminus of the protein, six identically spaced cysteines were identified in all sequences. Maximum likelihood analysis of synonymous versus non-synonymous substitution rates indicated that positive selection played a role in the evolution of the gene during the diversification of the genus. Fourteen codons under significant positive selection reside in the C-terminal 28 amino acid region, suggesting that this region interacts with host molecules in most sequenced accessions. Selection pressures on the gene may be either due to the pathogenicity or avirulence function of the gene or both.