Multi-level dynamics of viral co-infection

流行病学数据表明，在人群中共同传播的病毒以独特的方式相互作用

Epidemiological data suggest that viruses that co-circulate within human populations interact in unique ways that can result in altered replication, pathogenesis, and transmission dynamics compared to how they would operate in isolation. In order to understand the effects of viral co-infection at population levels, it is critical to dissect the mechanisms of interaction between viruses at multiple levels within their shared host. A system in which multiple viruses and hosts can be manipulated is critical for modeling how unrelated viruses interact within their shared hosts and how these interactions alter the effects of infection. The long-term goal of this research is to uncover properties of viral co-infection that can be tested for generality in other systems. The objectives of the proposed study are to establish a tractable invertebrate model system of viral infection and co-infection, and to develop mathematical models to understand how viruses interact with each other and their host to ultimately affect the host pathology and population dynamics. Drosophila and associated viruses will be used to test the central hypothesis that co-infection results in non-additive effects relative to single infections, and that these effects are correlated at different levels of organization. Oral infection of adult flies with Drosophila C virus (DCV) and Drosophila X virus (DXV) will be used to test the effects of co-infection on viral growth dynamics, host gene expression, viral transmission rates, and host demography, including fecundity, developmental rate, and mortality. Aim 1 will focus on molecular interactions, by quantifying viral growth dynamics and characterizing the host transcriptome in response to single and dual virus infections in adult flies. In Aim 2, the effects of co-infection on both direct and environmental transmission of the viruses will be determined. Fecundity, offspring developmental rate, and mortality are major contributors to population dynamics and will thus be the focus of Aim 3. Understanding how co-infection alters fly demography will lead to modeling of long-term impacts of co-circulating viruses in infected populations. Statistical and mathematical modeling within and between the three aims will be used to describe the interactions between DCV and DXV within their shared host. This study will advance the field by generating rich datasets to test models of viral co- infection and by establishing a tractable model system for the study of viral co-infection at multiple organizational levels.