Identification of Master Transcription Factors of Dental Epithelial Stem Cell by Computational Method

计算方法鉴定牙上皮干细胞主转录因子

• 批准号: 10239100
• 负责人: Huojun Cao
• 金额: $ 23.04万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10239100
• 关键词: Address; Adult; Animals; Bayesian Modeling; Cells; ChIP-seq; Computer Models; Computing Methodologies; Consumption; Data; Dental; Dental Libraries; Development; Ectopic Expression; Epigenetic Process; Epithelial; Fibroblasts; Fluorescence-Activated Cell Sorting; Foundations; Future; Genetic Transcription; Goals; Hepatocyte; Human; Incisor; Lead; Life; Literature; Liver; Mesenchymal Stem Cells; Methods; Modeling; Molecular; Molecular Biology; Molecular Profiling; Mus; Mutant Strains Mice; Pattern; Performance; Process; Protocols documentation; Research; Source; Technology; Testing; Therapeutic; Time; Tooth regeneration; Tooth structure; Transgenic Mice; Validation; base; cell type; embryonic stem cell; epigenome; epithelial stem cell; experimental study; in silico; induced pluripotent stem cell; interest; novel; repaired; restoration; self renewing cell; stem cell biomarkers; stem cell therapy; stem cells; transcription factor; transcriptome; transcriptome sequencing

PROJECT SUMMARY Stem cell based treatments are promising approaches for tooth repair, restoration and replacement. However, there is no available source of dental epithelial stem cells (DESCs), which is essential for these approaches. Fortunately, recent development in cell reprogramming technology has made it possible to generate desired cell type from other available cell types by ectopic expression of only a handful master transcription factors (MTFs). However, experimental identification of MTFs is time- consuming and expensive. Hence a comprehensive identification of MTFs for DESCs is lacking and presents a critical barrier for understanding molecular biology of DESCs and realization its therapeutic potential. The objective of this application is to generate transcriptome (RNA-seq) and epigenome (H3K27ac ChIP-seq) profiles of DESCs and comprehensively identify MTFs of DESCs based on its molecular profiles. Our preliminary study shows that by fluorescence activated cell sorting (FACS) we can isolate DESCs from incisors of Sox2-GFP transgenic mouse, in which GFP expression is driven by DESCs specific marker Sox2. In addition, we developed a computational MTFs prediction method termed “MTFinder” (Master Transcription Factor-Finder) that incorporates both transcriptome and epigenome data into a Bayesian statistical model. In this project, we will first define dynamic transcriptome and epigenome profiles of mouse DESCs and its progeny (Aim 1). And then apply our recently developed computational model MTFinder to comprehensively identify MTFs for mouse DESCs (Aim 2). At the completion of this project, we will generate molecular profiles of mouse DESCs. This will significantly expand our understanding of transcriptional and epigenetics landscape of DESCs. We expect that a list of known and novel MTFs of DESCs will be identified. This provides foundation for future cell reprogramming research. Furthermore, this project will validate the performance of MTFinder computational method, which can be applied to other cell types of interests.

项目摘要 基于干细胞的治疗方法是牙齿修复，恢复和替换的方法。 但是，没有可用的牙齿上皮干细胞（DESC）来源，这对于 这些方法。幸运的是，细胞重编程技术的最新发展使得 仅通过仅一个可用的细胞类型来生成所需的细胞类型 少数主要转录因子（MTF）。但是，MTF的实验鉴定是时间 - 消耗且昂贵。因此，缺乏对DESC的MTF的全面识别，并且缺乏 提出了理解DESC分子生物学并实现其治疗的关键障碍 潜在的。该应用的目的是生成转录组（RNA-SEQ）和表观基因组 （H3K27AC芯片seq）descs的概况，并根据其全面识别descs的MTF 分子曲线。我们的初步研究表明，通过荧光激活的细胞分选（FACS）我们 可以从Sox2-GFP转基因小鼠的切牙中分离出desc，其中GFP表达式由 descs特定标记SOX2。此外，我们开发了一种计算MTFS预测方法 称为“ mtfinder”（主转录因子 - 芬德），同时包含转录组和 表观基因组数据成为贝叶斯统计模型。在这个项目中，我们将首先定义动态 小鼠descs及其后代的转录组和表观基因组轮廓（AIM 1）。然后应用我们的 最近开发的计算模型MTFinder可以全面识别鼠标的MTF descs（目标2）。该项目完成时，我们将生成小鼠descs的分子曲线。 这将大大扩展我们对Descs的转录和表观遗传学景观的理解。 我们预计将确定已知和新颖的DESC MTF列表。这为基础提供了 未来的细胞重编程研究。此外，该项目将验证mtfinder的性能 计算方法，可以应用于其他兴趣的细胞类型。