Understanding the mechanisms and processes involved in the generation and dynamics of pathogen diversity is critical if we want to manage disease risk, especially for diseases that infect multiple host species and exhibit the poorly understood phenomenon of “spillover” to new hosts. Gene and genome diversity, and associated phenotypes and functionality, are a consequence of processes operating at varying levels from genomes to ecosystems. New genetic variants may alter host-pathogen interactions by allowing infection of new host species or changing virulence. However, their subsequent dynamics will depend on the fitness of the new variant in the context of the prevailing population and ecosystem conditions. There is an urgent need to understand the drivers of pathogen dynamics and diversity in fragmented multi-host populations.
Bartonella infections in wild rodent populations are an ideal model system to examine these issues. Bartonella are intracellular bacteria that are transmitted by fleas. Rodent-associated Bartonella species exhibit extremely high genetic diversity, high prevalence within host populations (>40%), and include several species associated with human disease. Within European populations, there are at least 4 species with differing levels of genotypic diversity, and genotypes exhibit varying levels of host specificity. Genomic analyses indicate that these species have evolved strategies to promote diversity, with many genes associated with host-adaptation packaged randomly into bacteriophage particles, generating an extremely effective mechanism for gene transfer. Flea vectors appear to have a key role by promoting lateral gene transfer. To date studies have focussed on host-specificity of different genotypes. However, other key traits such as infection length and competitive ability in interspecific interactions also contribute to a pathogen’s ability to persist in fragmented populations.
This project will use new and archived samples from water vole metapopulations to examine the generation and dynamics of Bartonella diversity. Studies over the last 20 years provide unparalleled knowledge of metapopulation dynamics (e.g. bottlenecks, extinction-recolonisation). The three Bartonella species detected exhibit negative interactions, with coinfected individuals and subpopulations occurring less often than expected.
To investigate the key drivers of these spatial and temporal dynamics, the project will
(1) quantify spatio-temporal patterns of intraspecific diversity for the three Bartonella species using multi-locus sequence analysis (MLSA) and whole genome sequencing approaches,
(2) assess phenotypic properties of different genotypes (host specificity, vector specificity, infection length, competitive ability in mixed infections)
(3) determine how the spatial-temporal dynamics of different Bartonella are influenced by e.g. host connectivity, distribution of alternative hosts and coinfection.
The project suits a student with a background in molecular ecology or epidemiology and numerical skills, who has interests in spatial ecology, disease ecology and landscape genetics. The project will be conducted in association with an ongoing field study of water vole metapopulations. The student will use next generation sequencing approaches and hierarchical spatial models to account for uncertainty in detection at all stages of the sampling process. The student will be given a thorough training in laboratory skills, the analysis of genomic and genetic data and advanced statistical modelling that make it possible to remove sampling noise from pathogen detection data.
Berglund, E. C., Ehrenborg, C., Pettersson, O. V., Granberg, F., Näslund, K., Holmberg, M., & Andersson, S. G. E. (2010). Genome dynamics of Bartonella grahamii in micro-populations of woodland rodents. BMC Genomics 11:152.
Buffet, J.-P., Kosoy, M., & Vayssier-Taussat, M. (2013). Natural history of Bartonella infecting rodents in light of new knowledge on genomics, diversity and evolution. Future Microbiology, 8, 1117–1128.
Miller, D.A.W., Talley, B.L., Lips, K.R., and Campbell Grant, E.H. (2012). Estimating patterns and drivers of infection prevalence and intensity when detection is imperfect and sampling error occurs. Methods Ecol. Evol. 3:850–859.
If you wish to apply for this project, pleadse go to this link.