Biological and biomedical implications of the co-evolution of pathogens and their hosts

Woolhouse, Mark; Webster, Joanne P.; Domingo, Esteban; Charlesworth, Brian; Levin, Bruce R.

doi:10.1038/ng1202-569

Cited by 756 publications

(744 citation statements)

References 101 publications

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“…This can be positive selection that drives resistance alleles through fixation (Woolhouse et al . 2002; Bangham et al . 2007; Magwire et al .…”

Section: Introductionmentioning

confidence: 99%

“…2005), existing pathogens evolve to escape host defences (Woolhouse et al . 2002), or environmental conditions change. However, most theoretical attention has been paid to models in which co‐evolution between hosts and pathogens results in negative frequency‐dependent selection that can maintain both resistant and susceptible alleles of a gene in populations (Clark 1976; Stahl et al .…”

Section: Introductionmentioning

confidence: 99%

“…1999; Woolhouse et al . 2002). This process is of particular interest as it may favour the evolution of sexual reproduction and recombination (Jaenike 1978).…”

Section: Introductionmentioning

confidence: 99%

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The genetic architecture of resistance to virus infection in Drosophila

et al. 2016

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Variation in susceptibility to infection has a substantial genetic component in natural populations, and it has been argued that selection by pathogens may result in it having a simpler genetic architecture than many other quantitative traits. This is important as models of host–pathogen co‐evolution typically assume resistance is controlled by a small number of genes. Using the Drosophila melanogaster multiparent advanced intercross, we investigated the genetic architecture of resistance to two naturally occurring viruses, the sigma virus and DCV (Drosophila C virus). We found extensive genetic variation in resistance to both viruses. For DCV resistance, this variation is largely caused by two major‐effect loci. Sigma virus resistance involves more genes – we mapped five loci, and together these explained less than half the genetic variance. Nonetheless, several of these had a large effect on resistance. Models of co‐evolution typically assume strong epistatic interactions between polymorphisms controlling resistance, but we were only able to detect one locus that altered the effect of the main effect loci we had mapped. Most of the loci we mapped were probably at an intermediate frequency in natural populations. Overall, our results are consistent with major‐effect genes commonly affecting susceptibility to infectious diseases, with DCV resistance being a near‐Mendelian trait.

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“…This can be positive selection that drives resistance alleles through fixation (Woolhouse et al . 2002; Bangham et al . 2007; Magwire et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The genetic architecture of resistance to virus infection in Drosophila

et al. 2016

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“…Understanding the maintenance of this variation, despite its apparent disadvantage in terms of host fitness, has been at the core of immunoecology for three decades (Sheldon and Verhulst, 1996;Lazzaro and Little, 2009). It is now widely recognized that immunoheterogeneity results from an interplay between evolutionary and proximate causes, including host-parasite coevolution (Woolhouse et al, 2002), life history (Schmid-Hempel, 2003), immunological and physiological tradeoffs (Schmid-Hempel, 2003;Cotter et al, 2004) or phenotypic plasticity (Bocher et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Landscape features and helminth co-infection shape bank vole immunoheterogeneity, with consequences for Puumala virus epidemiology

et al. 2013

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Heterogeneity in environmental conditions helps to maintain genetic and phenotypic diversity in ecosystems. As such, it may explain why the capacity of animals to mount immune responses is highly variable. The quality of habitat patches, in terms of resources, parasitism, predation and habitat fragmentation may, for example, trigger trade-offs ultimately affecting the investment of individuals in various immunological pathways. We described spatial immunoheterogeneity in bank vole populations with respect to landscape features and co-infection. We focused on the consequences of this heterogeneity for the risk of Puumala hantavirus (PUUV) infection. We assessed the expression of the Tnf-a and Mx2 genes and demonstrated a negative correlation between PUUV load and the expression of these immune genes in bank voles. Habitat heterogeneity was partly associated with differences in the expression of these genes. Levels of Mx2 were lower in large forests than in fragmented forests, possibly due to differences in parasite communities. We previously highlighted the positive association between infection with Heligmosomum mixtum and infection with PUUV. We found that Tnf-a was more strongly expressed in voles infected with PUUV than in uninfected voles or in voles co-infected with the nematode H. mixtum and PUUV. H. mixtum may limit the capacity of the vole to develop proinflammatory responses. This effect may increase the risk of PUUV infection and replication in host cells. Overall, our results suggest that close interactions between landscape features, co-infection and immune gene expression may shape PUUV epidemiology.

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“…These models are more closely related to epidemiological models and merit a review on their own (e.g. Woolhouse et al, 2002). In this review, I will focus on models that describe the host-pathogen interaction within individual hosts, that is, models belonging to the first two categories.…”

Section: Three Categories Of Host-pathogen Interaction Modelsmentioning

confidence: 99%

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

Doeschl‐Wilson

2011

Animal

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Compared with the application of mathematical models to study human diseases, models that describe animal responses to pathogen challenges are relatively rare. The aim of this review is to explain and show the role of mathematical host-pathogen interaction models in providing underpinning knowledge for improving animal health and sustaining livestock production. Existing host-pathogen interaction models can be assigned to one of three categories: (i) models of the infection and immune system dynamics, (ii) models that describe the impact of pathogen challenge on health, survival and production and (iii) models that consider the co-evolution of host and pathogen. State-of-the-art approaches are presented and discussed for models belonging to the first two categories only, as they concentrate on the host-pathogen dynamics within individuals. Models of the third category fall more into the class of epidemiological models, which deserve a review by themselves. An extensive review of published models reveals a rich spectrum of methodologies and approaches adopted in different modelling studies, and a strong discrepancy between models concerning diseases in animals and models aimed at tackling diseases in humans (most of which belong to the first category), with the latter being generally more sophisticated. The importance of accounting for the impact of infection not only on health but also on production poses a considerable challenge to the study of host-pathogen interactions in livestock. This has led to relatively simplistic representations of host-pathogen interaction in existing models for livestock diseases. Although these have proven appropriate for investigating hypotheses concerning the relationships between health and production traits, they do not provide predictions of an animal's response to pathogen challenge of sufficient accuracy that would be required for the design of appropriate disease control strategies. A synthesis between the modelling methodologies adopted in categories 1 and 2 would therefore be desirable. The progress achieved in mathematical modelling to study immunological processes relevant to human diseases, together with the current advances in the generation and analysis of biological data related to animal diseases, offers a great opportunity to develop a new generation of host-pathogen interaction models that take on a fundamental role in the study and control of disease in livestock.

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Biological and biomedical implications of the co-evolution of pathogens and their hosts

Cited by 756 publications

References 101 publications

The genetic architecture of resistance to virus infection in Drosophila

The genetic architecture of resistance to virus infection in Drosophila

Landscape features and helminth co-infection shape bank vole immunoheterogeneity, with consequences for Puumala virus epidemiology

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

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