Escherichia coli is responsible for the most bacterial deaths each year related to antimicrobial resistance. The World Health Organization has recommended research on developing vaccines to prevent or reduce the impact of these infections as an important priority.

Developing vaccines against E. coli is challenging because the immune mechanisms associated with protection from urinary tract infection and invasive disease remain poorly defined. Human studies can identify responses associated with protection, but these observations require mechanistic investigation to determine causality.

This DPhil will define immune correlates of protection against E. coli infection by studying human cohorts including individuals with recurrent urinary tract infection, patients with E. coli bacteraemia, high risk individuals and participants in controlled human infection studies. Using systems immunology approaches, the project will identify immune signatures associated with protection or susceptibility.

Experimental infection models will then be used to test mechanistic hypotheses arising from human studies and to investigate the functional role of candidate protective immune responses. Together, these complementary approaches will define immune correlates of protection that can guide the rational development and evaluation of E. coli vaccines.

You will receive training and supervision at the Peter Medawar Building for Pathogen Research, University of Oxford in immunology techniques. Functional antibody assays including opsonisation phagocytosis assay and NK-mediated antibody-dependent cell-mediated cytotoxicity assays will be used alongside cellular immunology approaches including spectral flow cytometry and Activation-Induced Marker assay followed by single-cell RNA sequencing (AIM-seq). Immune responses associated with protection in human studies will be cross linked with protection against E. coli challenge in the mouse model. You will also receive training in and mouse models of infection. The project will provide opportunities to apply computational and machine-learning methods to integrated immunological datasets.