Pathogen avoidance by insect predators

Insects can detect cues related to the risk of attack by their natural enemies. Pathogens are among the natural enemies of insects and entomopathogenic fungi attack a wide array of host species. Evidence documents that social insects in particular have adapted behavioural mechanisms to avoid infection by fungal pathogens. These mechanisms are referred to as 'behavioural resistance'. However, there is little evidence for similar adaptations in non-social insects. We have conducted experiments to assess the potential of common insect predators to detect and avoid their entomopathogenic fungal natural enemy Beauveria bassiana. The predatory bug Anthocoris nemorum was able to detect and avoid nettle leaves that were treated with B. bassiana. Females laid fewer eggs on leaf halves contaminated with the pathogen. Similarly, females were very reluctant to contact nettle leaves contaminated with the fungus compared to uncontaminated control leaves in ‘no-choice’ experiments. Adult seven spot ladybirds, Coccinella septempunctata, overwinter in the litter layer often in groups. Adult C. septempunctata modified their overwintering behaviour in relation to the presence of B. bassiana conidia in soil and sporulating conspecifics by moving away from sources of infection. Furthermore active (non-overwintering) adult C. septempunctata were also able to detect and avoid B. bassiana conidia on different substrates; bean leaves, soil and sporulating on dead conspecifics. Our studies show that insect predators have evolved mechanisms to detect and avoid pathogens that they are susceptible to. Fungal pathogens may be significant mortality factors among populations of insect predators, especially long-lived species that must diapause before reproduction. Likewise, actively foraging species are more likely to come in contact with pathogens than predators that sit and wait for prey. These particular groups of insects will benefit from adaptations to avoid pathogens

100K Pathogen Genome Project.

The 100K Pathogen Genome Project is producing draft and closed genome sequences from diverse pathogens. This project expanded globally to include a snapshot of global bacterial genome diversity. The genomes form a sequence database that has a variety of uses from systematics to public health

Sustainable deployment of QTLs conferring quantitative resistance to crops: first lessons from a stochastic model

Quantitative plant disease resistance is believed to be more durable than qualitative resistance, since it exerts less selective pressure on the pathogens. However, the process of progressive pathogen adaptation to quantitative resistance is poorly understood, which makes it difficult to predict its durability or to derive principles for its sustainable deployment. Here, we study the dynamics of pathogen adaptation in response to quantitative plant resistance affecting pathogen reproduction rate and its carrying capacity. We developed a stochastic model for the continuous evolution of a pathogen population within a quantitatively resistant host. We assumed that pathogen can adapt to a host by the progressive restoration of reproduction rate or of carrying capacity, or of both. Our model suggests that a combination of QTLs affecting distinct pathogen traits was more durable if the evolution of repressed traits was antagonistic. Otherwise, quantitative resistance that depressed only pathogen reproduction was more durable. In order to decelerate the progressive pathogen adaptation, QTLs that decrease the pathogen's ability to extend must be combined with QTLs that decrease the spore production per lesion or the infection efficiency or that increase the latent period. Our theoretical framework can help breeders to develop principles for sustainable deployment of quantitative trait loci.

Host stress drives Salmonella recrudescence

Host stress is well known to result in flare-ups of many bacterial, viral and parasitic infections. The mechanism by which host stress is exploited to increase pathogen loads, is poorly understood. Here we show that Salmonella enterica subspecies enterica serovar Typhimurium employs a dedicated mechanism, driven by the scsA gene, to respond to the host stress hormone cortisol. Through this mechanism, cortisol increases Salmonella proliferation inside macrophages, resulting in increased intestinal infection loads in DBA/2J mice. ScsA directs overall Salmonella virulence gene expression under conditions that mimic the intramacrophagic environment of Salmonella, and stimulates the host cytoskeletal alterations that are required for increased Salmonella proliferation inside cortisol exposed macrophages. We thus provide evidence that in a stressed host, the complex interplay between a pathogen and its host endocrine and innate immune system increases intestinal pathogen loads to facilitate pathogen dispersal

Genetic and phenotypic insights in the adaptation of Magnaporthe oryzae to rice.

Magnaporthe oryzae is the fungal pathogen causing rice blast. Rice resistance is commonly used to control this widely distributed and damaging disease of a major food crop. Pathogen adaptation to its host is illustrated by frequent events or resistance breakdown after deployment of resistant cultivars. Cloning of resistance genes and of their corresponding avirulence genes provide a unique opportunity to document how resistance breakdowns occurred and to try to imagine how they could be avoided in the future. On the basis of genetic data, we will present several examples of evolution of avirulence genes and try to propose from these case studies some general assumptions on the pathogen adaptation. In addition, based on phenotypic characterization of interactions and population genetics of the pathogen, we will show evidences of a longer coevolution process that leaded to specialization on different rice genetics groups

Livestock abundance predicts vampire bat demography, immune profiles, and bacterial infection risk

Human activities create novel food resources that can alter wildlife–pathogen interactions. If resources amplify or dampen, pathogen transmission probably depends on both host ecology and pathogen biology, but studies that measure responses to provisioning across both scales are rare. We tested these relationships with a 4-year study of 369 common vampire bats across 10 sites in Peru and Belize that differ in the abundance of livestock, an important anthropogenic food source. We quantified innate and adaptive immunity from bats and assessed infection with two common bacteria. We predicted that abundant livestock could reduce starvation and foraging effort, allowing for greater investments in immunity. Bats from high-livestock sites had higher microbicidal activity and proportions of neutrophils but lower immunoglobulin G and proportions of lymphocytes, suggesting more investment in innate relative to adaptive immunity and either greater chronic stress or pathogen exposure. This relationship was most pronounced in reproductive bats, which were also more common in high-livestock sites, suggesting feedbacks between demographic correlates of provisioning and immunity. Infection with both Bartonella and haemoplasmas were correlated with similar immune profiles, and both pathogens tended to be less prevalent in high-livestock sites, although effects were weaker for haemoplasmas. These differing responses to provisioning might therefore reflect distinct transmission processes. Predicting how provisioning alters host–pathogen interactions requires considering how both within-host processes and transmission modes respond to resource shifts

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens