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Specialization for resistance in wild host-pathogen interaction networks.

Barrett LG, Encinas-Viso F, Burdon JJ, Thrall PH - Front Plant Sci (2015)

Bottom Line: At the individual level, specialization was strongly linked to partial resistance, such that partial resistance was effective against a greater number of pathogens compared to full resistance.Second, we found that all networks were significantly nested.Third, we found that resistance networks were significantly modular in two spatial networks, clearly reflecting spatial and ecological structure within one of the networks.

View Article: PubMed Central - PubMed

Affiliation: Commonwealth Scientific and Industrial Research Organization Agriculture Flagship Canberra, ACT, Australia.

ABSTRACT
Properties encompassed by host-pathogen interaction networks have potential to give valuable insight into the evolution of specialization and coevolutionary dynamics in host-pathogen interactions. However, network approaches have been rarely utilized in previous studies of host and pathogen phenotypic variation. Here we applied quantitative analyses to eight networks derived from spatially and temporally segregated host (Linum marginale) and pathogen (Melampsora lini) populations. First, we found that resistance strategies are highly variable within and among networks, corresponding to a spectrum of specialist and generalist resistance types being maintained within all networks. At the individual level, specialization was strongly linked to partial resistance, such that partial resistance was effective against a greater number of pathogens compared to full resistance. Second, we found that all networks were significantly nested. There was little support for the hypothesis that temporal evolutionary dynamics may lead to the development of nestedness in host-pathogen infection networks. Rather, the common patterns observed in terms of nestedness suggests a universal driver (or multiple drivers) that may be independent of spatial and temporal structure. Third, we found that resistance networks were significantly modular in two spatial networks, clearly reflecting spatial and ecological structure within one of the networks. We conclude that (1) overall patterns of specialization in the networks we studied mirror evolutionary trade-offs with the strength of resistance; (2) that specific network architecture can emerge under different evolutionary scenarios; and (3) network approaches offer great utility as a tool for probing the evolutionary and ecological genetics of host-pathogen interactions.

No MeSH data available.


Related in: MedlinePlus

Network structure properties of host-pathogen interactions. Two important properties of ecological networks are nestedness and modularity. Here we show four cartoons representing different host-pathogen genetic interaction matrices, each with different levels of nestedness (A, B) and modularity (C, D). For each matrix we show hosts in columns and pathogens in rows. Black squares in each matrix represent resistance between a plant and a pathogen genotype and gray squares represent host susceptibility. In (C), red solid lines define host-pathogen interaction modules.
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Figure 1: Network structure properties of host-pathogen interactions. Two important properties of ecological networks are nestedness and modularity. Here we show four cartoons representing different host-pathogen genetic interaction matrices, each with different levels of nestedness (A, B) and modularity (C, D). For each matrix we show hosts in columns and pathogens in rows. Black squares in each matrix represent resistance between a plant and a pathogen genotype and gray squares represent host susceptibility. In (C), red solid lines define host-pathogen interaction modules.

Mentions: A network can be defined as a set of items, called nodes, connected by links if they interact. In host pathogen interactions, networks have two sets of nodes (each set representing individual hosts or pathogens) and are therefore termed “bipartite networks.” Links between nodes represent resistance or infection phenotypes (depending on the focus of the analysis). In such a network, specificity can be estimated for individuals or the network as a whole simply based on the number of links an individual has with its partners. In addition, patterns of specialization are often characterized by estimating two key network properties commonly known as nestedness and modularity (Figure 1). In host-pathogen networks, nestedness relates to the differentiation of resistance or infectivity specificities along a contained gradient, within which specialists (individuals with few links) interact with subsets of the partners interacting with generalists (individuals with many links) (Flores et al., 2012). For example, in a maximally nested infection network, the most specialized pathogen can infect only the hosts most susceptible to infection. The next most specialized pathogen could infect the host most susceptible to infection as well as one additional host, and so on (Figure 1A). Nestedness is a commonly encountered property in mutualistic networks (Jordano et al., 2003), and significant nested structures have been also found in studies of host-parasite (Vázquez et al., 2005; Vacher et al., 2008) and bacteria-phage (Flores et al., 2012; Poisot et al., 2013) networks. Modularity indicates the extent to which resistance or infectivity interactions can be partitioned into groups (referred to as modules: Figure 1C) with many interactions within groups but few among them (Blüthgen et al., 2008). In a maximally modular network there would be no cross-infections between pathogens in one module and hosts in another. Modularity differs from nestedness in that specificities cannot be simply ranked by increasing range. Rather, interactions take place among distinct clusters of host and pathogen individuals, within which distinct patterns of specificity (including nestedness) may be evident (Flores et al., 2012). Like nestedness, modularity is commonly detected in species interaction networks (Olesen et al., 2007; Fortuna et al., 2010).


Specialization for resistance in wild host-pathogen interaction networks.

Barrett LG, Encinas-Viso F, Burdon JJ, Thrall PH - Front Plant Sci (2015)

Network structure properties of host-pathogen interactions. Two important properties of ecological networks are nestedness and modularity. Here we show four cartoons representing different host-pathogen genetic interaction matrices, each with different levels of nestedness (A, B) and modularity (C, D). For each matrix we show hosts in columns and pathogens in rows. Black squares in each matrix represent resistance between a plant and a pathogen genotype and gray squares represent host susceptibility. In (C), red solid lines define host-pathogen interaction modules.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585140&req=5

Figure 1: Network structure properties of host-pathogen interactions. Two important properties of ecological networks are nestedness and modularity. Here we show four cartoons representing different host-pathogen genetic interaction matrices, each with different levels of nestedness (A, B) and modularity (C, D). For each matrix we show hosts in columns and pathogens in rows. Black squares in each matrix represent resistance between a plant and a pathogen genotype and gray squares represent host susceptibility. In (C), red solid lines define host-pathogen interaction modules.
Mentions: A network can be defined as a set of items, called nodes, connected by links if they interact. In host pathogen interactions, networks have two sets of nodes (each set representing individual hosts or pathogens) and are therefore termed “bipartite networks.” Links between nodes represent resistance or infection phenotypes (depending on the focus of the analysis). In such a network, specificity can be estimated for individuals or the network as a whole simply based on the number of links an individual has with its partners. In addition, patterns of specialization are often characterized by estimating two key network properties commonly known as nestedness and modularity (Figure 1). In host-pathogen networks, nestedness relates to the differentiation of resistance or infectivity specificities along a contained gradient, within which specialists (individuals with few links) interact with subsets of the partners interacting with generalists (individuals with many links) (Flores et al., 2012). For example, in a maximally nested infection network, the most specialized pathogen can infect only the hosts most susceptible to infection. The next most specialized pathogen could infect the host most susceptible to infection as well as one additional host, and so on (Figure 1A). Nestedness is a commonly encountered property in mutualistic networks (Jordano et al., 2003), and significant nested structures have been also found in studies of host-parasite (Vázquez et al., 2005; Vacher et al., 2008) and bacteria-phage (Flores et al., 2012; Poisot et al., 2013) networks. Modularity indicates the extent to which resistance or infectivity interactions can be partitioned into groups (referred to as modules: Figure 1C) with many interactions within groups but few among them (Blüthgen et al., 2008). In a maximally modular network there would be no cross-infections between pathogens in one module and hosts in another. Modularity differs from nestedness in that specificities cannot be simply ranked by increasing range. Rather, interactions take place among distinct clusters of host and pathogen individuals, within which distinct patterns of specificity (including nestedness) may be evident (Flores et al., 2012). Like nestedness, modularity is commonly detected in species interaction networks (Olesen et al., 2007; Fortuna et al., 2010).

Bottom Line: At the individual level, specialization was strongly linked to partial resistance, such that partial resistance was effective against a greater number of pathogens compared to full resistance.Second, we found that all networks were significantly nested.Third, we found that resistance networks were significantly modular in two spatial networks, clearly reflecting spatial and ecological structure within one of the networks.

View Article: PubMed Central - PubMed

Affiliation: Commonwealth Scientific and Industrial Research Organization Agriculture Flagship Canberra, ACT, Australia.

ABSTRACT
Properties encompassed by host-pathogen interaction networks have potential to give valuable insight into the evolution of specialization and coevolutionary dynamics in host-pathogen interactions. However, network approaches have been rarely utilized in previous studies of host and pathogen phenotypic variation. Here we applied quantitative analyses to eight networks derived from spatially and temporally segregated host (Linum marginale) and pathogen (Melampsora lini) populations. First, we found that resistance strategies are highly variable within and among networks, corresponding to a spectrum of specialist and generalist resistance types being maintained within all networks. At the individual level, specialization was strongly linked to partial resistance, such that partial resistance was effective against a greater number of pathogens compared to full resistance. Second, we found that all networks were significantly nested. There was little support for the hypothesis that temporal evolutionary dynamics may lead to the development of nestedness in host-pathogen infection networks. Rather, the common patterns observed in terms of nestedness suggests a universal driver (or multiple drivers) that may be independent of spatial and temporal structure. Third, we found that resistance networks were significantly modular in two spatial networks, clearly reflecting spatial and ecological structure within one of the networks. We conclude that (1) overall patterns of specialization in the networks we studied mirror evolutionary trade-offs with the strength of resistance; (2) that specific network architecture can emerge under different evolutionary scenarios; and (3) network approaches offer great utility as a tool for probing the evolutionary and ecological genetics of host-pathogen interactions.

No MeSH data available.


Related in: MedlinePlus