A MAPK-Driven Feedback Loop Suppresses Rac Activity to Promote RhoA-Driven Cancer Cell Invasion.
A MAPK-Driven Feedback Loop Suppresses Rac Activity to Promote RhoA-Driven Cancer Cell Invasion.
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DOI：
10.1371/journal.pcbi.1004909
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发表时间：
2016-05
影响因子：
4.3
通讯作者：
Caswell PT
中科院分区：
文献类型：
--
作者：
Hetmanski JH;Zindy E;Schwartz JM;Caswell PT
3D微环境中的细胞迁移对于发育、体内平衡和癌症等疾病的病理生物学至关重要。Rab偶联蛋白（RCP）依赖性α5β1和EGFR 1的共同转运通过增强侵袭细胞前部的EGFR 1信号传导，促进癌细胞侵袭含有纤维连接蛋白（FN）的细胞外基质（ECM）。这促进了RhoGT 3信号转导的开关，以抑制Rac 1并激活RhoA-ROCK-Formin同源结构域3（FHOD 3）通路，并产生驱动入侵的丝状伪足肌动蛋白-刺突突起。为了进一步了解驱动RCP驱动的侵入性迁移的信号网络，我们基于现有的网络路径/模型生成了一个布尔逻辑模型，其中每个节点都可以通过计算模拟进行查询。该模型预测了一个意想不到的反馈回路，Raf/MEK/ERK信号通过抑制Rac激活的Sos 1-Eps 8-Abi 1复合物来维持对Rac 1的抑制，从而使RhoA活性在侵入性突起中占主导地位。MEK抑制足以促进板状伪足的形成和对抗丝状伪足肌动蛋白峰的形成，并导致在三维矩阵中移动的细胞的前缘处的Rac的激活和RhoA的失活。此外，MEK抑制消除RCP/α5β1/EGFR 1驱动的侵袭性迁移。然而，敲低Eps 8（以抑制Sos 1-Abi 1-Eps 8复合物）后，MEK抑制对RhoGT 3活性没有影响，也不对抗侵袭性迁移，这表明MEK-ERK信号传导抑制Rac激活的Sos 1-Abi 1-Eps 8复合物以维持RhoA活性，并促进丝状伪足肌动蛋白尖峰形成和侵袭性迁移。我们的研究强调了数学建模方法的预测潜力，并证明了简单的干预（MEK抑制）可能在预防侵袭性迁移和转移方面具有治疗益处。大多数癌症相关的死亡是由癌细胞从原发部位转移形成转移引起的，因此理解支持细胞迁移和通过其局部环境入侵的信号传导机制至关重要。关于导致侵入性细胞迁移的关键事件已经发现了很多。在这里，我们利用这些先验知识，基于简单的ON/OFF关系和逻辑建立了一个强大的预测模型，以确定潜在的干预目标，以减少有害的侵入性迁移。通过研究我们的模型，我们已经确定了一个对决定侵入性迁移的信号传导很重要的负反馈回路，该回路的破坏使细胞恢复为较慢、侵入性较低的表型。我们已经支持这种反馈回路的预测使用一系列的体外实验中进行的细胞内的2-D和生理相关的3-D环境。我们的研究结果证明了这种建模技术的预测能力，并可能成为预防某些癌症转移的临床干预的基础。
Cell migration in 3D microenvironments is fundamental to development, homeostasis and the pathobiology of diseases such as cancer. Rab-coupling protein (RCP) dependent co-trafficking of α5β1 and EGFR1 promotes cancer cell invasion into fibronectin (FN) containing extracellular matrix (ECM), by potentiating EGFR1 signalling at the front of invasive cells. This promotes a switch in RhoGTPase signalling to inhibit Rac1 and activate a RhoA-ROCK-Formin homology domain-containing 3 (FHOD3) pathway and generate filopodial actin-spike protrusions which drive invasion. To further understand the signalling network that drives RCP-driven invasive migration, we generated a Boolean logical model based on existing network pathways/models, where each node can be interrogated by computational simulation. The model predicted an unanticipated feedback loop, whereby Raf/MEK/ERK signalling maintains suppression of Rac1 by inhibiting the Rac-activating Sos1-Eps8-Abi1 complex, allowing RhoA activity to predominate in invasive protrusions. MEK inhibition was sufficient to promote lamellipodia formation and oppose filopodial actin-spike formation, and led to activation of Rac and inactivation of RhoA at the leading edge of cells moving in 3D matrix. Furthermore, MEK inhibition abrogated RCP/α5β1/EGFR1-driven invasive migration. However, upon knockdown of Eps8 (to suppress the Sos1-Abi1-Eps8 complex), MEK inhibition had no effect on RhoGTPase activity and did not oppose invasive migration, suggesting that MEK-ERK signalling suppresses the Rac-activating Sos1-Abi1-Eps8 complex to maintain RhoA activity and promote filopodial actin-spike formation and invasive migration. Our study highlights the predictive potential of mathematical modelling approaches, and demonstrates that a simple intervention (MEK-inhibition) could be of therapeutic benefit in preventing invasive migration and metastasis. The majority of cancer-related fatalities are caused by the movement of cancer cells away from the primary site to form metastases, making understanding the signalling mechanisms which underpin cell migration and invasion through their local environment of paramount importance. Much has been discovered about key events leading to invasive cell migration. Here, we have taken this prior knowledge to build a powerful predictive model based on simple ON/OFF relations and logic to determine potential intervention targets to reduce harmful invasive migration. Interrogating our model, we have identified a negative feedback loop important to the signalling that determines invasive migration, the breaking of which reverts cells to a slower, less invasive phenotype. We have supported this feedback loop prediction using an array of in vitro experiments performed in cells within 2-D and physiologically relevant 3-D environments. Our findings demonstrate the predictive power of such modelling techniques, and could form the basis for clinical intervention to prevent metastasis in certain cancers.