COS 68-8
Phylogeography of Bartonella bacteria in Eidolon spp. fruit bats across Africa

Wednesday, August 12, 2015: 10:30 AM
326, Baltimore Convention Center
Clifton D. McKee, Department of Biology, Colorado State University, Fort Collins, CO
Ying Bai, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, CO
Nels G. Johnson, Department of Biology, Colorado State University, Fort Collins, CO
Cara E. Brook, Department of Ecology and Evolutionary Biology, Princeton University
Ivan Kuzmin, Global Alliance for Rabies Control
Lynn M. Osikowicz, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, CO
Alison J. Peel, Environmental Futures Research Institute, Griffith University
Richard Suu-Ire, Wildlife Division, Forestry Commission of Ghana, Accra, Ghana
Andrew A. Cunningham, Institute of Zoology, Zoological Society of London, London, United Kingdom
James L. N. Wood, Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
Michael Y. Kosoy, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, Fort Collins, CO
Colleen T. Webb, Department of Biology, Colorado State University, Fort Collins, CO
David T. S. Hayman, Molecular Epidemiology and Public Health Laboratory, Infectious Disease Research Centre, Massey University
Background/Question/Methods

Detecting structure in well-mixed, migratory animal populations with molecular tools can be challenging. However, previous research has demonstrated that using genetic data from animal parasites can provide greater resolution for revealing cryptic population structure in hosts. Genetic data from widely distributed and migratory fruit bats (Eidolon helvum and E. dupreanum) indicate that these populations are panmictic across Africa and Madagascar. We hypothesize that measuring species abundances in a highly diverse genus of bacterial parasites (Bartonella) in blood from Eidolon spp. fruit bats may reveal population structure in these hosts that was undetected from genetic data. In the process, we have developed a novel multi-gene PCR platform using replicate amplification and sequencing of six neutral loci to detect potentially co-occurring Bartonella species in each blood sample. Since each set of primers may have different amplification bias for particular Bartonella species, we have adapted a hierarchical Bayesian multinomial model to account for biases and integrate species abundances assessed from each primer set into a single measure of relative species abundance from each sample. Finally, a Bayesian multinomial logistic model is used to assess the role of location, age class, and sex in explaining relative Bartonella species abundances in the sampled bats.

Results/Conclusions

Prevalence of Bartonella spp. DNA was high (>50%) across all sampled locations with the highest prevalence in bats from the island Annobón. In addition to the six known Bartonella species isolated from E. helvum, we report the presence of additional Bartonella species in these bats that were previously only found in ectoparasitic bat flies. We did not detect significant geographic or demographic patterns in the distribution of Bartonella species from samples, which supports genetic evidence of panmixia of E. helvum. Island bat populations known to be genetically distinct, most conspicuously for the endemic E. dupreanum on Madagascar, also lacked significant geographic patterns, which may reflect ongoing transmission of Bartonella species by occasional island migrants or intermediate bat hosts. The novel techniques demonstrated in this study could be applied to future research that will improve our knowledge of the distribution of other co-occurring Bartonella species in their zoonotic hosts and our understanding of the ecology and evolution of these cryptic parasites.