Human African trypanosomiasis (HAT) kills thousands of people each year in sub-Saharan Africa. The disease is caused by African trypanosomes transmitted by the tsetse fly. HAT transmission is complex; it requires mammalian and invertebrate hosts and involves domestic and wild reservoirs. No mammalian vaccines exist and therapeutic drugs have serious side effects with increasing resistance seen in patients. In contrast, reduction of tsetse populations is highly efficacious for disease control. However, the implementation of the tsetse control programs, which rely on traps and targets, have been difficult to sustain because they are not practical and require extensive community participation. A paratransgenic strategy has been developed which exploits the unique biology of tsetse and its maternally inherited bacterial symbionts. In this strategy, tsetse's mutualist symbiont Sodalis is harnessed to express trypanosome inhibitory molecules in tsetse's midgut to impair trypanosome transmission. Transgenic Sodalis bacterium conferring refractoriness may be driven into natural tsetse populations by cytoplasmic incompatibility phenomenon mediated by tsetse's symbiont, Wolbachia. We propose to investigate the biogeography of the human disease vector species, Glossina fuscipes fuscipes, its Trypanosoma parasite(s), and its Wolbachia and Sodalis symbionts. Using a combination of laboratory and field experiments, we will investigate the potential for a Wolbachia mediated gene-drive mechanism to aid in the application of paratransgenic flies. In addition, we will elucidate the basic genetic structure of this human disease vector population, for which no information exists. This information is necessary for the efficacious implementation and monitoring of either the traditional or novel control strategies. Knowledge obtained on symbiont biology, maternal linkage of tsetse's multiple symbionts, Wolbachia infection phenotype, potential strength of Wolbachia mediated drive, population genetics and epidemiological dynamics will provide the parameters needed to develop a mathematically based model framework. This model will allow us to test the predictive nature of the empirical data, design the optimal strategies for population control, and predict feasibility and robustness for the success of the replacement strategy. This interdisciplinary application will combine epidemiology, population genetics and modeling with model parameterization and verification from laboratory and field research.
Funding: R01AI068932 NIH/NIAID