DMS/NIGMS 2: Regulation of Cellular Stemness during the Epithelial-Mesenchymal Transition (EMT)

本研究将采用理论/实验相结合的方法来解决上皮-间质转化（EMT）诱导过程中细胞命运轨迹的问题。EMT转变最初是在发育生物学的背景下发现的，它指的是细胞可以显著地改变其表型行为，从久坐、强粘附的生活方式（“上皮”）到以运动和较弱的细胞-细胞偶联为特征的生活方式（“间充质”）。这种转变后来被证明与原发性肿瘤转移扩散的发生直接相关。最近的研究表明，上皮细胞既可以直接重编程为间充质状态，也可以变得更像干细胞，并表现出中间的杂交E/M特性。后一种状态似乎是最有效的产生新的肿瘤，因此是最危险的。基于研究人员的初步调查，最先进的单细胞测量技术将与先进的数学建模框架一起使用，以了解细胞如何选择特定的命运，并定量地揭示导致这些细胞沿着其特定轨迹的遗传和表观遗传动力学。研究人员将开发新的数学概念，如挫折在细胞命运网络中的作用；这里的挫折是指各种遗传相互作用的不相容，理解它如何使上述中间状态能够帮助开发阻止其影响的想法。研究人员还将研究“表观遗传”修饰剂的作用，即蛋白质的化学修饰，帮助包装DNA，直接影响在不同表型之间切换的难易程度。而且，除了直接的智力价值和可能的转化应用溢出效应外，该项目还将通过与HBCU合作，将这一研究领域引入代表性不足的群体的本科生，从而为美国国家科学基金会“缺失的数百万”目标做出贡献。从技术上讲，计划的研究将包括几个相互交织的领域，将数学与生物学结合起来。研究人员将充分利用现代单细胞技术创建数据集，用于定量确定EMT的逐步进展和伴随的细胞干性特性，测量转录谱和染色质依赖性表观遗传学的特征。这些数据将有助于建立新的动态模型，包括确定性和随机性，将转录调控耦合到动态修改的底层表观遗传景观。这些模型将用于理解挫折的作用，即在给定的网络表型状态下不满意的调节相互作用，在具有增强可塑性的中间状态的启用中；这种理解可能具有直接的翻译相关性，因为可塑性与肿瘤起始和肿瘤治疗抵抗有关。这些数据还将有助于开发和更好地理解先进数据分析技术的有效性，例如允许轨迹推断的最佳传输概念。这个强大的想法需要使用成本函数来关联不同时间点的数据，研究人员将使用合成和实际数据来探索不完整的机制模型可以用来创建更好的成本函数的想法。总而言之，这项研究将极大地提高对细胞命运转变的理解，以及它们如何依赖于控制转录和翻译过程的遗传电路的详细分子水平基础，以及这些电路相互耦合的表观遗传景观。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This research will involve a joint theoretical/experimental approach to address cell-fate trajectories that occur during induction of the epithelial-mesenchymal transition (EMT). The EMT transition was originally discovered in the context of developmental biology and refers to the fact that cells can dramatically change their phenotypic behavior from a sedentary, strongly-adherent lifestyle (“epithelial”) to one characterized by motility and weaker cell-cell coupling (“mesenchymal”). This transition was later shown to be directly relevant for the onset of metastatic spread of primary tumors. Recent efforts have indicated that epithelial cells can either undergo direct reprogramming to mesenchymal states or alternatively become more stem-like and exhibit intermediate, hybrid E/M properties. These latter states appear to be the most effective at initiating new tumors and hence the most dangerous. Based on the investigators’ preliminary investigations, state-of-the-art single cell measurement technology will be used together with advanced mathematical modeling frameworks to understand how cells choose specific fates and to quantitatively unravel the genetic and epigenetic dynamics that leads these cells along their particular trajectories. The investigators will develop new mathematical concepts such as the role of frustration in cell fate networks; here frustration refers to the incompatibility of various genetic interactions and understanding how it enables the aforementioned intermediate states can help develop ideas to interdict their effects. The investigators will also study the role of “epigenetic” modifiers, chemical modifications of proteins that help package the DNA that directly affect how easy it is to switch between different phenotypes. And, aside for the direct intellectual merit and possible spillovers into translational applications, this program will contribute the NSF’s “missing millions” goal by partnering with an HBCU to introduce this area of research to undergraduates from underrepresented groups.Technically, the projected research will consist of several interwoven areas coupling mathematics to biology. The investigators will take full advantage of modern single-cell technology to create datasets that will be used to quantitatively determine the step-by-step progress of EMT and concomitant stemness properties of cells, measuring both transcriptional profiles and features of the chromatin-dependent epigenetics. These data will help formulate new types of dynamical models, both deterministic and stochastic, coupling transcriptional regulation to a dynamically modifiable underling epigenetic landscape. These models will be used to understand the role of frustration, i.e. unsatisfied regulatory interactions in a given phenotypic state of the network, in the enabling of intermediate states with enhanced plasticity; this understanding could have direct translational relevance, as plasticity has been implicate in tumor initiation and in tumor therapy resistance. The data will also help develop and better understand the validity of advanced data analysis techniques such as the concept of optimal transport which allows for trajectory inference. This powerful idea requires the use of cost functions to relate data at different timepoints and the investigators will use both synthetic and actual data to explore the idea that incomplete mechanistic models can be used to create better such cost functions. All told, this research will greatly improve the understanding of cell-fate transitions and how they depend on the detailed molecular level underpinnings of genetic circuits governing transcriptional and translational processes and the epigenetic landscape to which these circuits are reciprocally coupled.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.