Mapping p53 dynamics to cell-fate outcomes in reprogramming and oncogenesis

项目摘要 细胞的命运是随着有机体的发育而决定的。在人类发育中，多能干细胞 分化为外胚层、中胚层和内胚层三层。这些类型的组织进一步 分化成具有特定功能的特定细胞类型，包括神经元、免疫细胞和皮肤细胞。这些 身份是稳定的;一旦细胞分化成其最终状态，它不会恢复到干细胞状态，也不会 它会转化成另一种细胞类型。皮肤细胞不会自发地变成神经元，即使神经元是 损坏然而，Takahashi和Yamanaka证明，细胞有可能恢复到正常状态。 当他们通过强迫将小鼠成纤维细胞重编程为诱导多能干细胞（iPSC）时， 干细胞特异性转录因子的过表达。2010年，Vierbuchen和同事证明了 成纤维细胞可以通过神经元特异性转录因子直接重编程为神经元， 对iPSC中间体的需求。然而，这些系统中的每一个的重编程效率都很低; 很少有细胞能够改变它们的细胞身份。 2019年，Babos和Galloway大大提高了直接运动神经元的重编程效率。 重编程，证明改进的重编程产量比原始过程高100倍。 他们利用了增强另一种细胞命运转变的因素：癌症。促进健康细胞 向癌症的转变也提高了细胞改变其细胞类型的能力。因此，重编程可以作为 一个癌症发生的模型。通过了解这些致癌基因促进肿瘤细胞增殖的分子机制， 通过重新编程，我们可以理解癌基因如何逃避细胞屏障而形成肿瘤。 在F99阶段的研究中，我将研究抑癌蛋白p53在肿瘤发生中的作用。 癌基因介导的重编程p53是癌症中最常见的突变基因。而不是p53 由于在癌症中失去表达，它最常突变以产生不能执行其指定功能的蛋白质。 功能和积累到异常高的水平。作为合成生物学家，我将设计合成基因电路 在重编程过程中追踪并报告p53水平。我将分离出积累p53的细胞， 他们重新编程的能力 在K 00阶段的研究中，我将把我对p53的研究扩展到三维， 卵巢癌的模型。卵巢癌通常在癌症转移后的晚期被诊断出来， 从而导致患者的不良结果。肿瘤发生的3D模型可以揭示卵巢癌的早期阶段 癌症，并使临床医生能够早期发现癌症，当疾病最容易治疗时。通过诱导 在卵巢癌的3D模型中癌症的启动和使用p53传感器跟踪癌症进展，我将确定 肿瘤形成的驱动因素和与早期疾病相关的因素。

Project Summary Cell fates are decided as an organism develops. In human development, pluripotent stem cells differentiate into the three layers of ectoderm, mesoderm, and endoderm. These classes of tissue further differentiate into specific cell types with specific functions including neurons, immune cells, and skin cells. These identities are stable; once a cell differentiates into its final state, it will not revert back to a stem cell state, nor will it transform into another cell type. A skin cell will not spontaneously become a neuron, even if the neuron is damaged. However, Takahashi and Yamanaka demonstrated that cells have the potential to revert back to a stem cell fate when they reprogrammed mouse fibroblasts into induced pluripotent stem cells (iPSCs) by forced overexpression of stem cell-specifying transcription factors. In 2010, Vierbuchen and colleagues demonstrated that fibroblasts could be reprogrammed directly to neurons using neuron-specific transcription factors, bypassing the need for an iPSC-intermediate. However, reprogramming efficiencies in each of these systems was low; very few cells are actually capable of changing their cellular identity. In 2019, Babos and Galloway greatly improve reprogramming efficiencies in direct motor neuron reprogramming, demonstrating improved reprogramming yields 100 times greater than the original process. They drew upon factors that enhanced another cell fate transition: cancer. Genes that promote a healthy cell’s transition to cancer also improved the ability of a cell to change its cell type. Thus, reprogramming can serve as a model of cancer initiation. By understanding the molecular mechanisms by which these oncogenes promote reprogramming, we can understand how oncogenes evade cellular barriers to cancer and establish tumors. In the F99-phase of the proposed research, I will investigate the role of the tumor suppressor protein p53 in oncogene-mediated reprogramming. p53 is the most frequently mutated gene in cancer. Rather than p53 expression being lost in cancer, it is most often mutated to create a protein unable to perform its designated functions and accumulates to abnormally high levels. As a synthetic biologist, I will design synthetic gene circuits that track and report p53 levels during reprogramming. I will isolate cells that accumulate p53 and investigate their ability to reprogram. In the K00-phase of the proposed research, I will extend my investigations of p53 to three-dimensional models of ovarian cancer. Ovarian cancer is often diagnosed at late stages, after the cancer has metastasized, leading to poor patient outcomes. 3D models of tumor initiation can shed light on the early stages of ovarian cancer and enable clinicians to catch the cancer early, when the disease is most easily treated. By inducing cancer initiation in 3D models of ovarian cancer and tracking cancer progression using p53-sensors, I will identify the drivers of tumor establishment and factors associated with early-stage disease.