Placing the T3SS effector-network paradigm within a systems level understanding of in vivo infection
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AI plain-English summaryBacterial pathogens that cause diarrhoeal disease inject dozens of proteins into gut cells to hijack their machinery, and this project will map how those proteins work together as a network rather than in isolation. Most research has studied these injected proteins — called effectors — one at a time, but removing a single effector rarely stops an infection. The team has shown that effectors form redundant subnetworks inside host cells, so the bacteria can lose several effectors and still cause disease. This explains why vaccines or drugs targeting a single effector often fail. The project will use the mouse pathogen *Citrobacter rodentium* to identify which effectors become essential only in specific contexts — for example, in young versus adult animals, or in different genetic backgrounds — and to build computational models that predict infection outcomes. If successful, this work could shift how researchers design treatments for diarrhoeal diseases, which remain a major global health burden. Instead of targeting one bacterial protein at a time, drug developers might aim to disrupt entire subnetworks or exploit context-dependent vulnerabilities. The project is fundamental science — it asks how complex infection systems actually work — but understanding the rules of effector networks could eventually lead to more effective vaccines or therapies for gut infections such as *E. coli* and *Shigella*.
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