Centre for Molecular Microbiology and Infection, Department of Life Sciences, Imperial College, London, UK.
Functional Proteomics Group, Chester Beatty Laboratories, Institute of Cancer Research, London, UK.
Science. 2021 Mar 12;371(6534). doi: 10.1126/science.abc9531.
Infections with many Gram-negative pathogens, including , , , and , 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 (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 effector repertoire in 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.
许多革兰氏阴性病原体的感染，包括 、 、 、 和 ，都依赖于 III 型分泌系统（T3SS）效应子。我们假设，在劫持哺乳动物细胞内的过程中，效应子作为一个强大的网络，可以耐受大量的收缩。这在体内使用鼠病原体 （编码 31 种效应子）进行了测试。连续的基因缺失表明，效应子对感染的必要性取决于具体情况，并且该网络可以在保持致病性的同时耐受 60%的收缩。尽管诱导的结肠细胞因子谱非常不同（例如白细胞介素-22、白细胞介素-17、干扰素-γ 或粒细胞-巨噬细胞集落刺激因子），但不同的网络诱导了保护性免疫。利用来自 100 多个不同突变组合的数据，我们构建并训练了一个能够预测定植结果的机器学习模型，该模型在实验中得到了证实。此外，在 中重现人类受限的肠致病性 效应子库不足以实现有效的定植，这意味着效应子网络参与了宿主适应。这些结果揭示了 T3SS 效应子网络和宿主反应的极端稳健性。
