4.8 Article

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

Journal

SCIENCE
Volume 371, Issue 6534, Pages 1122-+

Publisher

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.abc9531

Keywords

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Funding

  1. CRUK Centre grant [C309/A25144]
  2. Manna Center Program for Food Safety and Security at Tel Aviv University
  3. Edmond J. Safra Center for Bioinformatics at Tel-Aviv University
  4. Spanish government [FPU2017/04179]
  5. AEI/FEDER, Spain [PID2019-106960GB-I00]
  6. InGEMICS-CM, FSE/FEDER, Comunidad de Madrid Project [B2017/BMD-3691]
  7. AEI/MICIU/FEDER, European Union [BIO2017-89081-R]
  8. MRC program grant [MR/R02671/]
  9. Royal Society grant [IC160080]
  10. Wellcome Investigator Award [107057/Z/15/Z]
  11. Wellcome Trust [107057/Z/15/Z] Funding Source: Wellcome Trust

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The study demonstrates the extreme robustness of both T3SS effector networks and host responses, as pathogenicity can be maintained even with a 60% contraction in the effector network. Different effector networks induce varying colonic cytokine profiles, yet all can induce protective immunity, implicating the importance of effector networks in host adaptation.
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-g, 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.

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