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Novel molecular approaches for understanding the epidemiology of endemic anthrax


Anthrax, caused by the bacterium Bacillus anthracis, is consistently ranked amongst the major poverty-related neglected zoonotic diseases. In addition to being a major public health issue, anthrax is highly impactful on food security and livelihoods in vulnerable communities due to the livestock losses it engenders. I will apply cutting-edge techniques and analytical methods at the forefront of bacterial genomics to answer key questions about the epidemiology of anthrax that currently limit its control in endemic settings. My study, which focuses on highly affected rural communities in Tanzania, will be the first large-scale comparative genomic study of B. anthracis conducted within a One Health framework. A Bayesian inference approach combining spatio-temporal and genetic information will be used to reconstruct the patterns of B. anthracis exposure and transmission (Obj. 1), based on genomic data from multiple host species collected within and between outbreaks. This framework will allow some of the major assumptions about the epidemiology of anthrax to be tested, including if B. anthracis is a host generalist or whether certain strains are more likely to infect particular hosts (Obj. 2). This will be determined using tests for strain clustering by host species, while genome-wide association studies and machine/deep-learning approaches will be used to test for a genetic basis of host predilection. Finally, quantifying and accounting for within-host diversity of B. anthracis will be an important factor in accurately reconstructing transmission networks and assessing host-specificity. To do so, I will apply newly-developed software for variant profiling and haplotype reconstruction to population genomic data (Obj. 3). Overall, this project will result in major advances in the analytical methods available for genomic data, and will deliver a step-change in our understanding of the epidemiology of anthrax with profound implications for how it is understood and managed.

Taya Forde
University of Glasgow
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