Global analysis of Arabidopsis/downy mildew interactions reveals prevalence of incomplete resistance and rapid evolution of pathogen recognition

Interactions between Arabidopsis thaliana and its native obligate oomycete pathogen Hyaloperonospora arabidopsidis (Hpa) represent a model system to study evolution of natural variation in a host/pathogen interaction. Both Arabidopsis and Hpa genomes are sequenced and collections of different sub-species are available. We analyzed ～400 interactions between different Arabidopsis accessions and five strains of Hpa. We examined the pathogen's overall ability to reproduce on a given host, and performed detailed cytological staining to assay for pathogen growth and hypersensitive cell death response in the host. We demonstrate that intermediate levels of resistance are prevalent among Arabidopsis populations and correlate strongly with host developmental stage. In addition to looking at plant responses to challenge by whole pathogen inoculations, we investigated the Arabidopsis resistance attributed to recognition of the individual Hpa effectors, ATR1 and ATR13. Our results suggest that recognition of these effectors is evolutionarily dynamic and does not form a single clade in overall Arabidopsis phylogeny for either effector. Furthermore, we show that the ultimate outcome of the interactions can be modified by the pathogen, despite a defined gene-for-gene resistance in the host. These data indicate that the outcome of disease and disease resistance depends on genome-for-genome interactions between the host and its pathogen, rather than single gene pairs as thought previously.     

Tracking the dynamics of pathogen interactions: modeling ecological and immune-mediated processes in a two-pathogen single-host system

Traditionally, epidemiological studies have focused on understanding the dynamics of a single pathogen, assuming no interactions with other pathogens. Recently, a large body of work has begun to explore the effects of immune-mediated interactions, arising from cross-immunity and antibody-dependent enhancement, between related pathogen strains. In addition, ecological processes such as a temporary period of convalescence and pathogen-induced mortality have led to the concept of ecological interference between unrelated diseases. There remains, however, the need for a systematic study of both immunological and ecological processes within a single framework. In this paper, we develop a general two-pathogen single-host model of pathogen interactions that simultaneously incorporates these mechanisms. We are then able to mechanistically explore how immunoecological processes mediate interactions between diseases for a pool of susceptible individuals. We show that the precise nature of the interaction can induce either competitive or cooperative associations between pathogens. Understanding the dynamic implications of multi-pathogen associations has potentially important public health consequences. Such a framework may be especially helpful in disentangling the effects of partially cross-immunizing infections that affect populations with a pre-disposition towards immunosuppression such as children and the elderly.     

Inferential biases linked to unobservable states in complex occupancy models

Modeling of species distributions has undergone a shift from relying on equilibrium assumptions to recognizing transient system dynamics explicitly. This shift has necessitated more complex modeling techniques, but the performance of these dynamic models has not yet been assessed for systems where unobservable states exist. Our work is motivated by the impacts of the emerging infectious disease chytridiomycosis, a disease of amphibians that is associated with declines of many species worldwide. Using this host‐pathogen system as a general example, we first illustrate how misleading inferences can result from failing to incorporate pathogen dynamics into the modeling process, especially when the pathogen is difficult or impossible to survey in the absence of a host species. We found that traditional modeling techniques can underestimate the effect of a pathogen on host species occurrence and dynamics when the pathogen can only be detected in the host, and pathogen information is treated as a covariate. We propose a dynamic multistate modeling approach that is flexible enough to account for the detection structures that may be present in complex multistate systems, especially when the sampling design is limited by a species’ natural history or sampling technology. When multistate occupancy models are used and an unobservable state is present, parameter estimation can be influenced by model complexity, data sparseness, and the underlying dynamics of the system. We show that, even with large sample sizes, many models incorporating seasonal variation in vital rates may not generate reasonable estimates, indicating parameter redundancy. We found that certain types of missing data can greatly hinder inference, and we make study design recommendations to avoid these issues. Additionally, we advocate the use of time‐varying covariates to explain temporal trends in the data, and the development of sampling techniques that match the biology of the system to eliminate unobservable states when possible.     

Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection

Computer models of disease take a systems biology approach toward understanding host-pathogen interactions. In particular, data driven computer model calibration is the basis for inference of immunological and pathogen parameters, assessment of model validity, and comparison between alternative models of immune or pathogen behavior. In this paper we describe the calibration and analysis of an agent-based model of Leishmania major infection. A model of macrophage loss following uptake of necrotic tissue is proposed to explain macrophage depletion following peak infection. Using Gaussian processes to approximate the computer code, we perform a sensitivity analysis to identify important parameters and to characterize their influence on the simulated infection. The analysis indicates that increasing growth rate can favor or suppress pathogen loads, depending on the infection stage and the pathogen's ability to avoid detection. Subsequent calibration of the model against previously published biological observations suggests that L. major has a relatively slow growth rate and can replicate for an extended period of time before damaging the host cell.     

Making pathogens sociable: The emergence of high relatedness through limited host invasibility

Cooperation depends upon high relatedness, the high genetic similarity of interacting partners relative to the wider population. For pathogenic bacteria, which show diverse cooperative traits, the population processes that determine relatedness are poorly understood. Here, we explore whether within-host dynamics can produce high relatedness in the insect pathogen Bacillus thuringiensis. We study the effects of host/pathogen interactions on relatedness via a model of host invasion and fit parameters to competition experiments with marked strains. We show that invasibility is a key parameter for determining relatedness and experimentally demonstrate the emergence of high relatedness from well-mixed inocula. We find that a single infection cycle results in a bottleneck with a similar level of relatedness to those previously reported in the field. The bottlenecks that are a product of widespread barriers to infection can therefore produce the population structure required for the evolution of cooperative virulence.     

Enemies make you stronger: Coevolution between fruit fly host and bacterial pathogen increases postinfection survivorship in the host

Multiple laboratory studies have evolved hosts against a nonevolving pathogen to address questions about evolution of immune responses. However, an ecologically more relevant scenario is one where hosts and pathogens can coevolve. Such coevolution between the antagonists, depending on the mutual selection pressure and additive variance in the respective populations, can potentially lead to a different pattern of evolution in the hosts compared to a situation where the host evolves against a nonevolving pathogen. In the present study, we used Drosophila melanogaster as the host and Pseudomonas entomophila as the pathogen. We let the host populations either evolve against a nonevolving pathogen or coevolve with the same pathogen. We found that the coevolving hosts on average evolved higher survivorship against the coevolving pathogen and ancestral (nonevolving) pathogen relative to the hosts evolving against a nonevolving pathogen. The coevolving pathogens evolved greater ability to induce host mortality even in nonlocal (novel) hosts compared to infection by an ancestral (nonevolving) pathogen. Thus, our results clearly show that the evolved traits in the host and the pathogen under coevolution can be different from one‐sided adaptation. In addition, our results also show that the coevolving host–pathogen interactions can involve certain general mechanisms in the pathogen, leading to increased mortality induction in nonlocal or novel hosts.     

Prediction of protein-protein interactions between human host and a pathogen and its application to three pathogenic bacteria

Molecular understanding of disease processes can be accelerated if all interactions between the host and pathogen are known. The unavailability of experimental methods for large-scale detection of interactions across host and pathogen organisms hinders this process. Here we apply a simple method to predict protein-protein interactions across a host and pathogen organisms. We use homology detection approaches against the protein-protein interaction databases, DIP and iPfam in order to predict interacting proteins in a host-pathogen pair. In the present work, we first applied this approach to the test cases involving the pairs phage T4 -Escherichia coli and phage lambda -E. coli and show that previously known interactions could be recognized using our approach. We further apply this approach to predict interactions between human and three pathogens E. coli, Salmonella enterica typhimurium and Yersinia pestis. We identified several novel interactions involving proteins of host or pathogen that could be thought of as highly relevant to the disease process. Serendipitously, many interactions involve hypothetical proteins of yet unknown function. Hypothetical proteins are predicted from computational analysis of genome sequences with no laboratory analysis on their functions yet available. The predicted interactions involving such proteins could provide hints to their functions.     

Host–pathogen interactions: a proteomic view

Host–pathogen interactions reflect the balance of host defenses and pathogen virulence mechanisms. Advances in proteomic technologies now afford opportunities to compare protein content between complex biologic systems ranging from cells to animals and clinical samples. Thus, it is now possible to characterize host–pathogen interactions from a global proteomic view. Most reports to date focus on cataloging protein content of pathogens and identifying virulence-associated proteins or proteomic alterations in host response. A more in-depth understanding of host–pathogen interactions has the potential to improve our mechanistic understanding of pathogenicity and virulence, thereby defining novel therapeutic and vaccine targets. In addition, proteomic characterization of the host response can provide pathogen-specific host biomarkers for rapid pathogen detection and characterization, as well as for early and specific detection of infectious diseases. A review of host–pathogen interactions focusing on proteomic analyses of both pathogen and host will be presented. Relevant genomic studies and host model systems will be also be discussed.     

The interaction between plant competition and disease

It is well documented that pathogens can affect the survival, reproduction, and growth of individual plants. Drawing together insights from diverse studies in ecology and agriculture, we evaluate the evidence for pathogens affecting competitive interactions between plants of both the same and different species. Our objective is to explore the potential ecological and evolutionary consequences of such interactions. First, we address how disease interacts with intraspecific competition and present a simple graphical model suggesting that diverse outcomes should be expected. We conclude that the presence of pathogens may have either large or minimal effects on population dynamics depending on many factors including the density-dependent compensatory ability of healthy plants and spatial patterns of infection. Second, we consider how disease can alter competitive abilities of genotypes, and thus may affect the genetic composition of populations. These genetic processes feed back on population dynamics given trade-offs between disease resistance and other fitness components. Third, we examine how the effect of disease on interspecific plant interactions may have potentially far-reaching effects on community composition. A host-specific pathogen, for example, may alter a competitive hierarchy that exists between host and non-host species. Generalist pathogens can also induce indirect competitive interactions between host species. We conclude by highlighting lacunae in our current understanding and suggest that future studies should (1) examine a broader taxonomic range of pathogens since work to date has largely focused on fungal pathogens; (2) increase the use of field competition studies; (3) follow interactions for multiple generations; (4) characterize density-dependent processes; and (5) quantify pathogen, as well as plant, population and community dynamics.     

Red light imaging for programmed cell death visualization and quantification in plant–pathogen interactions

Studies on plant–pathogen interactions often involve monitoring disease symptoms or responses of the host plant to pathogen-derived immunogenic patterns, either visually or by staining the plant tissue. Both these methods have limitations with respect to resolution, reproducibility, and the ability to quantify the results. In this study we show that red light detection by the red fluorescent protein (RFP) channel of a multipurpose fluorescence imaging system that is probably available in many laboratories can be used to visualize plant tissue undergoing cell death. Red light emission is the result of chlorophyll fluorescence on thylakoid membrane disassembly during the development of a programmed cell death process. The activation of programmed cell death can occur during either a hypersensitive response to a biotrophic pathogen or an apoptotic cell death triggered by a necrotrophic pathogen. Quantifying the intensity of the red light signal enables the magnitude of programmed cell death to be evaluated and provides a readout of the plant immune response in a faster, safer, and nondestructive manner when compared to previously developed chemical staining methodologies. This application can be implemented to screen for differences in symptom severity in plant–pathogen interactions, and to visualize and quantify in a more sensitive and objective manner the intensity of the plant response on perception of a given immunological pattern. We illustrate the utility and versatility of the method using diverse immunogenic patterns and pathogens.     

Multiple guests in a single host: interactions across symbiotic and phytopathogenic bacteria in phloem‐feeding vectors – a review

Some pathogenic phloem‐limited bacteria are a major threat for worldwide agriculture due to the heavy economic losses caused to many high‐value crops. These disease agents – phytoplasmas, spiroplasmas, liberibacters, and Arsenophonus‐like bacteria – are transmitted from plant to plant by phloem‐feeding Hemiptera vectors. The associations established among pathogens and vectors result in a complex network of interactions involving also the whole microbial community harboured by the insect host. Interactions among bacteria may be beneficial, competitive, or detrimental for the involved microorganisms, and can dramatically affect the insect vector competence and consequently the spread of diseases. Interference is observed among pathogen strains competing to invade the same vector specimen, causing selective acquisition or transmission. Insect bacterial endosymbionts are another pivotal element of interactions between vectors and phytopathogens, because of their central role in insect life cycles. Some symbionts, either obligate or facultative, were shown to have antagonistic effects on the colonization by plant pathogens, by producing antimicrobial substances, by stimulating the production of antimicrobial substances by insects, or by competing for host infection. In other cases, the mutual exclusion between symbiont and pathogen suggests a possible detrimental influence on phytopathogens displayed by symbiotic bacteria; conversely, examples of microbes enhancing pathogen load are available as well. Whether and how bacterial exchanges occurring in vectors affect the relationship between insects, plants, and phytopathogens is still unresolved, leaving room for many open questions concerning the significance of particular traits of these multitrophic interactions. Such complex interplays may have a serious impact on pathogen spread and control, potentially driving new strategies for the containment of important diseases.     

The Problem of Auto-Correlation in Parasitology

Explaining the contribution of host and pathogen factors in driving infection dynamics is a major ambition in parasitology. There is increasing recognition that analyses based on single summary measures of an infection (e.g., peak parasitaemia) do not adequately capture infection dynamics and so, the appropriate use of statistical techniques to analyse dynamics is necessary to understand infections and, ultimately, control parasites. However, the complexities of within-host environments mean that tracking and analysing pathogen dynamics within infections and among hosts poses considerable statistical challenges. Simple statistical models make assumptions that will rarely be satisfied in data collected on host and parasite parameters. In particular, model residuals (unexplained variance in the data) should not be correlated in time or space. Here we demonstrate how failure to account for such correlations can result in incorrect biological inference from statistical analysis. We then show how mixed effects models can be used as a powerful tool to analyse such repeated measures data in the hope that this will encourage better statistical practices in parasitology.     

Host-pathogen interactions: a proteomic view

Host-pathogen interactions reflect the balance of host defenses and pathogen virulence mechanisms. Advances in proteomic technologies now afford opportunities to compare protein content between complex biologic systems ranging from cells to animals and clinical samples. Thus, it is now possible to characterize host-pathogen interactions from a global proteomic view. Most reports to date focus on cataloging protein content of pathogens and identifying virulence-associated proteins or proteomic alterations in host response. A more in-depth understanding of host-pathogen interactions has the potential to improve our mechanistic understanding of pathogenicity and virulence, thereby defining novel therapeutic and vaccine targets. In addition, proteomic characterization of the host response can provide pathogen-specific host biomarkers for rapid pathogen detection and characterization, as well as for early and specific detection of infectious diseases. A review of host-pathogen interactions focusing on proteomic analyses of both pathogen and host will be presented. Relevant genomic studies and host model systems will be also be discussed.     

Social immunity modulates competition between coinfecting pathogens

Coinfections with multiple pathogens can result in complex within‐host dynamics affecting virulence and transmission. While multiple infections are intensively studied in solitary hosts, it is so far unresolved how social host interactions interfere with pathogen competition, and if this depends on coinfection diversity. We studied how the collective disease defences of ants – their social immunity – influence pathogen competition in coinfections of same or different fungal pathogen species. Social immunity reduced virulence for all pathogen combinations, but interfered with spore production only in different‐species coinfections. Here, it decreased overall pathogen sporulation success while increasing co‐sporulation on individual cadavers and maintaining a higher pathogen diversity at the community level. Mathematical modelling revealed that host sanitary care alone can modulate competitive outcomes between pathogens, giving advantage to fast‐germinating, thus less grooming‐sensitive ones. Host social interactions can hence modulate infection dynamics in coinfected group members, thereby altering pathogen communities at the host level and population level.     

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.     

Evolution of pathogen virulence: the role of variation in host phenotype

Selection on pathogens tends to favour the evolution of growth and reproductive rates and a concomitant level of virulence (damage done to the host) that maximizes pathogen fitness. Yet, because hosts often pose varying selective environments to pathogens, one level of virulence may not be appropriate for all host types. Indeed, if a level of virulence confers high fitness to the pathogen in one host phenotype but low fitness in another host phenotype, alternative virulence strategies may be maintained in the pathogen population. Such strategies can occur either as polymorphism, where different strains of pathogen evolve specialized virulence strategies in different host phenotypes or as polyphenism, where pathogens facultatively express alternative virulence strategies depending on host phenotype. Polymorphism potentially leads to specialist pathogens capable of infecting a limited range of host phenotypes, whereas polyphenism potentially leads to generalist pathogens capable of infecting a wider range of hosts. Evaluating how variation among hosts affects virulence evolution can provide insight into pathogen diversity and is critical in determining how host pathogen interactions affect the phenotypic evolution of both hosts and pathogens.     

The acquisition of hypovirulence in host-pathogen systems with three trophic levels

A major focus of research on the dynamics of host-pathogen interactions has been the evolution of pathogen virulence, which is defined as the loss in host fitness due to infection. It is usually assumed that changes in pathogen virulence are the result of selection to increase pathogen fitness. However, in some cases, pathogens have acquired hypovirulence by themselves becoming infected with hyperparasites. For example, the chestnut blight fungus Cryphonectria parasitica has become hypovirulent in some areas by acquiring a double-stranded RNA hyperparasite that debilitates the pathogen, thereby reducing its virulence to the host. In this article, we develop and analyze a mathematical model of the dynamics of host-pathogen interactions with three trophic levels. The system may be dominated by either uninfected (virulent) or hyperparasitized (hypovirulent) pathogens, or by a mixture of the two. Hypovirulence may allow some recovery of the host population, but it can also harm the host population if the hyperparasite moves the transmission rate of the pathogen closer to its evolutionarily stable strategy. In the latter case, the hyperparasite is effectively a mutualist of the pathogen. Selection among hyperparasites will often minimize the deleterious effects, or maximize the beneficial effects, of the hyperparasite on the pathogen. Increasing the frequency of multiple infections of the same host individual promotes the acquisition of hypovirulence by increasing the opportunity for horizontal transmission of the hyperparasite. This effect opposes the usual theoretical expectation that multiple infections promote the evolution of more virulent pathogens via selection for rapid growth within hosts.     

Coevolution at multiple spatial scales: Linum marginale–Melampsora lini – from the individual to the species

Coevolutionary processes are intrinsically spatial as well as temporal, and occur at many different scales. These range from single populations dominated by demographic and genetic stochasticity, to metapopulations in which colonisation/extinction dynamics have a large influence, and larger geographic regions where phylogenetic patterns and historical events become important. We present data for the genetically and demographically well-characterised plant host–pathogen interaction, the Linum marginale–Melampsora lini system, and use this to demonstrate the varying nature of resistance and virulence structure across these spatial scales. At the within population level, our results indicate considerable variability in resistance and virulence, but little evidence of coordinated changes in host and pathogen. Studies involving comparisons among multiple demes within a single metapopulation show that adjacent populations often have asynchronous disease dynamics and large differences in diversity and frequency of resistance and virulence phenotypes. Nevertheless, at this scale, there is also evidence of spatial structure in that more closely adjacent host populations are significantly more likely to have similar resistance phenotypes and mean levels of resistance. At larger scales, comparisons among adjacent metapopulations indicate that quantitative differences in host mating system and other life history features can have further major consequences for how host and pathogen variation is packaged. Finally, comparisons at continental and among host-species levels show variation consistent with specialisation and speciation in the pathogen. This revised version was published online in July 2006 with corrections to the Cover Date.     

Spatial analysis of vector-borne disease: A four-species model

Summary Disease can influence a host population's dynamics directly or indirectly through effects on the host's interactions with competitors and exploiters. We present a stochastic, spatially explicit model for the epidemiological landscape of a vector-borne disease. Two host species, of unequal competitive strength, are attacked by a selective parasite; the parasite serves as a vector for a pathogen. We emphasize the importance of the ecological stencil, i.e. the local area where ecological interactions govern a site's species composition. We demonstrate analytically that varying the size of the ecological stencil critically affects the dynamics of the host densities and the potential equilibrium configuration of the system. We point out how parallel computing can efficiently employ the geometry of the stencil's local transitions to predict large-scale spatio-temporal patterns of the model community.