Modeling host–microbiome interactions to improve mechanistic understanding of aphid vectored plant pathogens

Abstract

Insect transmission of plant pathogens involves multi-layered interactions between vectors, viruses, host plants and environmental factors. Adding to the complexity of vector–virus relationships are diverse microbial communities, which are hypothesized to influence pathogen transmission. Although vector–virus interaction research has flourished, the role played by microbes in vector competence and disease epidemiology remains unclear in many pathosystems. We therefore aimed to develop a novel ecological modeling approach to identify environmental drivers of complex vector–virus–microbiome interactions, particularly differences in the abundance of microbial symbionts within vector microbiomes and relationships between symbionts and the probability of virus acquisition. Our approach combines established molecular tools for profiling microbial communities with underutilized Bayesian hierarchical modeling and data integration techniques. We used a globally relevant aphid–virus pathosystem to develop custom vector–microbiome models that incorporate environmental covariates (e.g., temperature, landcover) and applied them to individual vector symbionts to identify the extent to which environmental factors drive changes in microbial communities that then influence virus acquisition by the host aphid. Specifically, we focus on the aphid obligate symbiont (Buchnera) and a wide-spread facultative symbiont (Serratia) as proof of concept to develop models for two major vector species that include a single environmental covariate (i.e., temperature). Overall, we demonstrate how community-level modeling and microbiome profiling can identify candidate microbes and environmental variables associated with vector competence. Our modeling framework can accommodate a range of microbial symbionts with different abundances, overcome spatial misalignment of data streams, and is robust to varying levels of disease incidence. Results show Buchnera relative abundance is strongly negatively associated with virus acquisition by the vector S.avenae, but not R. padi. Although Serratia was not associated with R. padi vector competence, relative abundance was influenced by differences in spring temperatures. This work lays the foundation for developing a broader modeling framework for predicting disease dynamics in agroecosystems and deploying microbiome-targeted pest management tactics.