Clustering of Tir during enteropathogenic E. coli infection triggers calcium influx-dependent pyroptosis in intestinal epithelial cells.

Clustering of the enteropathogenic Escherichia coli (EPEC) type III secretion system (T3SS) effector translocated intimin receptor (Tir) by intimin leads to actin polymerisation and pyroptotic cell death in macrophages. The effect of Tir clustering on the viability of EPEC-infected intestinal epithelial cells (IECs) is unknown. We show that EPEC induces pyroptosis in IECs in a Tir-dependent but actin polymerisation-independent manner, which was enhanced by priming with interferon gamma (IFNγ). Mechanistically, Tir clustering triggers rapid Ca2+ influx, which induces lipopolysaccharide (LPS) internalisation, followed by activation of caspase-4 and pyroptosis. Knockdown of caspase-4 or gasdermin D (GSDMD), translocation of NleF, which blocks caspase-4 or chelation of extracellular Ca2+, inhibited EPEC-induced cell death. IEC lines with low endogenous abundance of GSDMD were resistant to Tir-induced cell death. Conversely, ATP-induced extracellular Ca2+ influx enhanced cell death, which confirmed the key regulatory role of Ca2+ in EPEC-induced pyroptosis. We reveal a novel mechanism through which infection with an extracellular pathogen leads to pyroptosis in IECs

Type III secretion system effectors form robust and flexible intracellular virulence networks

Infections with many Gram-negative pathogens, including Escherichia coli, Salmonella, Shigella, and Yersinia, rely on type III secretion system (T3SS) effectors. We hypothesized that while hijacking processes within mammalian cells, the effectors operate as a robust network that can tolerate substantial contractions. This was tested in vivo using the mouse pathogen Citrobacter rodentium (encoding 31 effectors). Sequential gene deletions showed that effector essentiality for infection was context dependent and that the network could tolerate 60% contraction while maintaining pathogenicity. Despite inducing very different colonic cytokine profiles (e.g., interleukin-22, interleukin-17, interferon-γ, or granulocyte-macrophage colony-stimulating factor), different networks induced protective immunity. Using data from >100 distinct mutant combinations, we built and trained a machine learning model able to predict colonization outcomes, which were confirmed experimentally. Furthermore, reproducing the human-restricted enteropathogenic E. coli effector repertoire in C. rodentium was not sufficient for efficient colonization, which implicates effector networks in host adaptation. These results unveil the extreme robustness of both T3SS effector networks and host responses