Research Supervisor Connect
Using a novel targeted metagenomics protocol, the EMPOWER study has generated complete infection profiles and rich microbiome data for over 1,300 women and girls in low-resource settings, with the aim of understanding the genetics and epidemiology of co-circulating pathogens threatening the reproductive health of women and girls. EMPOWER (Enrichment Metagenomics Profiling for Women’s Reproductive health) is a research program funded by the Gates Grand Challenges scheme (GCC), a partnership between the University of Sydney and ZAMBART at the University of Zambia.
Camperdown - School of Medical Sciences
Sexually Transmitted Infections (STIs) and other infections of the reproductive tract, such as the parasitic disease Female Genital Schistosomiasis (FGS), are pressing global health challenges. These conditions have severe implications for women's health, including an increased risk of HIV. Diagnosing these diseases is particularly difficult in resource-limited settings due to overlapping symptoms and inadequate specialised tests. Targeted metagenomic sequencing (tNGS) can deliver a comprehensive diagnostic system for multiple pathogens, and have been used effectively in several settings, such as for diagnosis and sequencing of respiratory pathogens. These approaches can be further enhanced by including specific targets for known drug resistance genes associated with STI pathogens, and host biomarkers such as genes upregulated in inflammation. The EMPOWER study (Enrichment Metagenomics Profiling for Women’s Reproductive health) is a research program funded by the Gates Foundation Grand Challenges scheme (GCC), and is a partnership between the University of Sydney and Zambart (University of Zambia Medical School). EMPOWER aims to develop and deliver a cost-effective, high-throughput solution for diagnosing STIs and FGS, addressing a significant healthcare gap in resource-limited settings.
Experience in working with data is essential; some knowledge of phylogenetics, phylogeographic analyses or epidemiological modelling would be beneficial. Laboratory work may be available for interested candidates, but is not required.
The opportunity ID for this research opportunity is 3705