Pathogen emergence: virus infections cannot be isolated from their environment

To predict disease dynamics and emergence we must understand the impact of the environment on host-pathogen interactions. To attain this goal, our team of plant virologists (Soledad Sacristán, Aurora Fraile and Fernando García-Arenal) in Madrid collected virus infection data in four different habitats of an agricultural ecosystem over three consecutive years. The theoretical group (Blai Vidiella, Raul Montáñez and Sergi Valverde) at Barcelona modelled the empirical data using bipartite networks, to be analysed by the joint team.

A bipartite network is a mathematical object that describes the pattern of interactions among two sets of species. The patterns of ecological networks have been classified in two main groups, nested or modular. Theory relates these patterns to the degree of species co-evolution: if co-evolution is weak, nestedness is expected, while modularity reflects strong coevolutionary pressure. However, our networks showed a confounding mix of nestedness and modularity depending on seasonality and/or habitat. We realised that we cannot fully understand environmental effects without accounting for habitat interactions. Links in a network do not capture interactions involving pathogens, hosts and habitats, i.e., more than two entities. Three way interactions are defined with hyperlinks, which can be visualized as a 3-D matrix, the hypergraph. From the hypergraph, we can recover not only the bipartite network, but also new kinds of networks, like the ecotype-pathogen network. Pathogens infect host ecotypes, an ecotype being a variant or breed that has adapted to a specific environment. We could thus generate an ecotype-pathogen network representing the full dataset without losing the environmental dependencies.

Computational modelisation of this empirical hypergraph suggests coexistence of nestedness and modularity is not only possible, but the reflection that natural selection imposes significant constraints on some structural patterns (modularity) but negligible constraints on others (nestedness). Depending on the spatial and temporal scales involved, apparently antagonistic structural patterns may coexist in ecosystems. Using this approach, we could predict the response of an ecosystem to different perturbations. This is relevant to understand how climatic variations can modulate infection rates and accelerate the spreading of viruses and the diseases associated with them.