Emerging infectious diseases (EIDs) threaten global health and security, but when and where they will occur remains unfortunately hard to predict. In part, this may be because pathogens are usually studied in isolation. An alternative approach is to consider emerging pathogens as members of an ecological community of interacting microbes. The community ecology of viruses co-occurring in hosts -- the virome -- has yet to be explored as an ecological community even though viruses are known to be important emerging disease agents. The research supported by this award will address this deficiency by focusing on the viromes living inside two widespread species that live in close association with humans: the white footed mouse (Peromyscus leucopus) and the blacklegged tick (Ixodes scapularis). Blacklegged ticks feed abundantly on white-footed mice, and the two species freely exchange microbes during the lengthy blood meals. By exploring how the viromes of these two species are assembled and how they interact and flow between mice and ticks, the proposed research will reveal how viromes shape transmission of existing and potential pathogens. The research will be carried out in the northeast corridor of the United States, an area known to be an excellent location for studying virus emergence and the role played by white-footed mice and blacklegged ticks in the transmission of zoonotic pathogens (those transmitted from vertebrate animals to humans). White-footed mice and blacklegged ticks serve as reservoir host and vector, respectively, of many emerging diseases of humans, including Lyme disease. Pilot studies have revealed that these same two species also harbor a community of previously undescribed viruses that may emerge as zoonotic diseases in the future.
The researchers take an explicitly ecological approach to understanding viromes of reservoir and vector hosts. The researchers will: (1) characterize the viromes of both mouse and tick; (2) determine patterns of virome assembly throughout the lifetimes of both mouse and tick; (3) determine how virus communities affect the transmission of several important vector borne zoonotic pathogens; (4) identify whether viromes affect differences between individual mice and ticks in their abilities to transmit known and new pathogens; and (5) determine whether viromes and transmission probabilities change along with changing abundance of mice and between suburban/urban and more rural habitats. Data generated by each of these specific aims will be modeled using statistical (machine learning) algorithms, which accommodate diverse data types and apply a model-free analytical approach. By using complex and high-dimensional empirical data describing hosts, vectors, viromes, and their shared natural environments, these algorithms can achieve superior pattern detection (such as virome composition) and improved ability to make useful predictions (such as what combinations of traits of vector viromes and mammal hosts best predict virus transmission).
