Social interactions cause the spread of parasites but it is not known which behaviors are particularly important in the promotion or prevention of transmission events. To answer this question, both the behavior of the host and the spread of the pathogen must be studied. By simultaneously analyzing social data from wild ringtailed lemurs and genetic data from a common gut bacterium, the researchers aim to identify the characteristics of the host and of their relationships that tend to prevent or promote transmission events. This study will provide one of the most direct tests of socially facilitated transmission to date and in doing so, can inform predictive models for the future spread of parasites in human populations. To this end, this study will also employ novel genetic methods that can potentially be widely applied to many questions about the mechanisms of parasite spread.
The researchers will examine if and to what extent patterns of association explain the transmission dynamics of an actual bacterial organism by correlating the social interaction network of ringtailed lemurs with incidences of shared haplotypes of E. coli. They will collect data over the course of a year to assess how seasonal shifts in social behavior influence the pattern of shared haplotypes, and to determine the resident and transient nature of E. coli haplotypes across time. Individuals typically harbor a unique resident haplotype, which when found as a transient haplotype in a group member, can potentially inform each individual's role in transmission. The researchers will use E. coli as a model "parasite" because its ubiquity among mammals facilitates the determination of transmission dynamics among all individuals and the extensive knowledge of its genome permits the use of a novel one-locus high throughput sequencing approach that will allow rapid differentiation of many more isolates than is feasible using traditional multi-locus methods. High throughput sequencing methods that target one locus, as proposed here, are currently used in microbiome research to differentiate species or genera of bacteria, but they have not been modified to differentiate strains of a single species. The researchers propose some of the first loci to do this in E. coli. Data from this study will stored on "life.bio" server. All sequence data will be uploaded onto GenBank.
