Molecular and biophysical mechanism of plasma membrane internalization during nonclathrin endocytosis

在胞吞作用中，细胞质膜变形并内化， 货物和跨膜受体。非网格蛋白/非小窝（CLIC/GEEC）内吞作用内化 糖基化受体和细胞外液，并连接到细胞极性的发展，起泡， 癌转移过程中上皮细胞向间充质细胞的转化。从分子和力学角度理解 CLIC是了解CLIC如何协调膜曲率和肌动蛋白聚合， 抵抗膜张力内化质膜。 研究CLIC的主要限制是缺乏明确的过程标记。一 赤松博士博士后实验室使用机器学习方法对真正的内吞事件进行分类， 适用于相对于其它类型的内吞消除CLIC内吞标记的歧义。这将 定义了CLIC的第一个独特标记，并将揭示CLIC膜过程中蛋白质组装的顺序 内化，这对于理解每种蛋白质的功能至关重要。为了验证这个假设， 膜张力控制CLIC进展，Akamatsu博士将联合收割机晶格光片显微镜与 他在博士后研究期间开发了一种校准方法，将荧光强度转换为数字 活细胞中的分子。有了这种新方法，分子计数晶格光片显微镜，他将 测量CLIC内吞蛋白分子在顶面和基底面的数量， 极化的iPS细胞，其膜张力不同。他将对渗透压下的细胞进行成像， 增加细胞膜张力。最后，了解膜之间的反馈关系， 曲率敏感BAR蛋白和肌动蛋白聚合在CLIC膜内化，他将 BAR蛋白掺入膜微管及其与肌动蛋白丝的相互作用 成核蛋白转化为多尺度的数学模型在他的博士后工作。模拟 该模型将预测细胞膜曲率，张力和 肌动蛋白聚合所必需的CLIC内吞作用的及时完成。该模型的预测将 在他自己的实验室中通过成像细胞内源性表达蛋白质结构域截短进行测试， 肌动蛋白成核和聚合的抑制剂。 Akamatsu博士长期以来一直对将物理建模与活细胞定量相结合感兴趣。 实验一到两年的额外博士后培训将使他能够充分发展这两种技能， 为了在他自己的实验室里有效地实现一个高度协同的反馈回路。在实验中共同提供咨询 大卫·德鲁宾的方法和加州大学圣地亚哥分校的Padmini Rangamani的计算模型给了他 这是这种综合方法的基础。来自Padmini Rangamani和Hernan的额外理论培训 Garcia，以及Eric Betzig，Matt Welch和Dan弗莱彻的定量实验方法将充分 让他做好准备，领导一个自己的综合建模和实验室。

During endocytosis, the cell's plasma membrane is deformed and internalized to bring in extracellular cargo and transmembrane receptors. Nonclathrin/noncaveolar (CLIC/GEEC) endocytosis internalizes glycosylated receptors and extracellular fluid, and is connected to cell polarity development, blebbing, and the epithelial-to-mesenchymal transition during cancer metastasis. A molecular and mechanical understanding of CLIC is necessary to understand how CLIC coordinates membrane curvature and actin polymerization to internalize the plasma membrane against membrane tension. The primary limitation in studying CLIC has been the lack of unambiguous markers for the process. A machine learning approach used in Dr. Akamatsu's postdoctoral lab to classify bona fide endocytic events will be adapted to disambiguate markers for CLIC endocytosis relative to other types of endocytosis. This will define the first unique markers for CLIC and will reveal the order of protein assembly during CLIC membrane internalization, which is essential for understanding the function of each protein. To test the hypothesis that membrane tension controls CLIC progression, Dr. Akamatsu will combine lattice light-sheet microscopy with a calibration method he developed during his postdoctoral research to convert fluorescence intensity to numbers of molecules in live cells. With this new method, molecule-counting lattice light-sheet microscopy, he will measure the numbers of molecules of CLIC endocytic proteins at both the apical and basolateral surfaces of polarized iPS cells, which differ in their membrane tension. He will image the cells under osmotic stress to increase cellular membrane tension. Finally, to understand the feedback relationship between membrane curvature-sensing BAR proteins and actin polymerization during CLIC membrane internalization, he will incorporate membrane tubulation by BAR proteins and their reciprocal interactions with actin filament nucleation proteins into a multi-scale mathematical model developed during his postdoctoral work. Simulations of this model will predict the critical feedback relationships between plasma membrane curvature, tension and actin polymerization necessary for the timely completion of CLIC endocytosis. Predictions from the model will be tested in his own lab by imaging cells endogenously expressing protein domain truncations in the presence of inhibitors of actin nucleation and polymerization. Dr. Akamatsu has a longstanding interest in combining physical modeling with live-cell quantitative experiments. One to two years of additional postdoctoral training will allow him to fully develop both skills in order to effectively implement a highly synergistic feedback loop in his own lab. Co-advising in experimental approaches by David Drubin and in computational modeling by Padmini Rangamani at UCSD have given him the foundation for this integrated approach. Additional training in theory from Padmini Rangamani and Hernan Garcia, and in quantitative experimental methods from Eric Betzig, Matt Welch, and Dan Fletcher will fully prepare him to lead an integrated modeling and experimental lab of his own.