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

这项研究将涉及一种联合的理论/实验方法来解决在诱导上皮-间充质转化(EMT)过程中发生的细胞命运轨迹。EMT转变最初是在发育生物学的背景下发现的，指的是细胞可以极大地改变其表型行为，从久坐的、强烈附着的生活方式(“上皮”)转变为以运动和较弱的细胞-细胞耦合为特征的生活方式(“间充质”)。这种转变后来被证明与原发肿瘤转移性扩散的开始直接相关。最近的研究表明，上皮细胞可以直接重编程为间充质状态，也可以变得更像干细胞，表现出中间的、杂交的E/M特性。后一种状态似乎在引发新的肿瘤方面最有效，因此也是最危险的。根据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，如挫折感在细胞命运网络中的作用；在这里，挫折感指的是各种基因相互作用的不相容，理解它如何使上述中间状态能够帮助形成阻止其影响的想法。研究人员还将研究“表观遗传”修饰物的作用，这是一种蛋白质的化学修饰，有助于包装DNA，直接影响在不同表型之间转换的容易程度。此外，除了直接的智力优势和可能的翻译应用溢出效应外，该项目还将通过与HBCU合作，将这一领域的研究介绍给来自代表性不足群体的本科生，从而为美国国家科学基金“失踪的数百万人”的目标做出贡献。从技术上讲，计划中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属性，测量依赖染色质的表观遗传学的转录图谱和特征。这些数据将有助于形成新类型的动力学模型，既有确定性的，也有随机性的，将转录调控与动态可修改的表观遗传格局相结合。这些模型将被用来理解挫折的作用，即在给定的网络表型状态下，不满意的调控相互作用，使中间状态具有增强的可塑性；这种理解可能具有直接的翻译相关性，因为可塑性与肿瘤的启动和肿瘤治疗抵抗有关。这些数据还将有助于开发和更好地了解先进数据分析技术的有效性，例如允许进行轨迹推断的最佳运输概念。这一强大的想法需要使用成本函数来关联不同时间点的数据，调查人员将使用合成数据和实际数据来探索可以使用不完整的机械模型来创建更好的此类成本函数的想法。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学图景。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.