Systems biology of quiescence entry

进入静止的系统生物学

基本信息

批准号：
10115766
负责人：
Orlando Argüello-Miranda
金额：
$ 9.59万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2021-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10115766
关键词：
Algorithms Animal Model Binding Biochemical Biochemistry Bioinformatics Biological Assay Biological Markers Carbon Cell Cycle Cell Cycle Arrest Cell Cycle Progression Cell Cycle Regulation Cell Proliferation Cell physiology Cells Cellular Structures Cellular biology Color Complement Computational Biology Computing Methodologies Core Facility Cyclic AMP Cyclic AMP-Dependent Protein Kinases DNA biosynthesis Data Decision Making Development Disease Environment Failure Fibrosis Goals Histone Acetylation Human Image Immunoprecipitation In complete remission Institution Label Lead Machine Learning Malignant Neoplasms Mass Spectrum Analysis Measures Meiosis Mentors Metabolic Methods Microfluidics Microscopy Molecular Nitrogen Nuclear Nutrient Nutrient Depletion Organ Organism Pathway interactions Pattern Phase Phosphotransferases Positioning Attribute Problem Solving Process Property Protein Biosynthesis Proteins Publishing Reporter Research Role Saccharomyces cerevisiae Signal Pathway Signal Transduction Source Starvation Stress Systems Biology Techniques Testing Time Series Analysis Tissues Training Transcription Repressor Work XBP1 gene anaphase-promoting complex base biological adaptation to stress career cellular imaging computational platform computer framework design detection of nutrient experimental study fluorescence imaging gene repression genetic approach imaging approach imaging system in vivo prevent response single cell analysis temporal measurement tool yeast genetics

项目摘要

Abstract This proposal aims to provide crucial training for the candidate’s long-term career plan to study how cellular quiescence is established through decision-making processes. The decision to undergo quiescence in response to stress or developmental signals is a fundamental and understudied property of living systems. Failure to maintain quiescence can lead to cell proliferation disorders in humans, such as fibrosis or cancer. Quiescence entry is triggered when multiple nutrient- and stress-sensing signaling pathways arrest the cell cycle machinery. However, the molecular mechanisms that coordinate stress response pathways with the cell cycle during quiescence remain largely unclear. This is, in part, due to the difficulties to simultaneously quantify multiple stress pathways at the single cell level in vivo. To solve this limitation, the candidate will use a microfluidics- fluorescent imaging system that tracks up to six different pathways simultaneously during the transition from proliferation into quiescence. Using this approach, the coordination between stress responses and the cell cycle machinery can be quantified with unprecedented temporal resolution in the model organism S. cerevisiae. A computational platform based on machine learning and time series analysis will be used to process the large imaging data derived from tracking six biomarkers simultaneously in single cells. An initial version of this framework found that during the onset of quiescence the nuclear levels of the conserved DNA-replication kinase Cdc7 are dynamically regulated. This approach also identified that the nuclear levels of the stress-activated transcriptional repressor Xbp1 define how the cell cycle is stopped during quiescence entry. Combining this computational approach with biochemical techniques will determine the molecular mechanisms for the establishment of cellular quiescence by modulation of stress responses and the cell cycle machinery. The candidate is to acquire crucial training in computational biology during the K99 phase of this proposal to complement his previous training in biochemistry, cell biology and yeast genetics. The candidate will be mentored by a leader in computational biology Dr. Gaudenz Danuser, whose lab develops advanced machine learning and time series analysis to study cellular signal transduction. This proposal harnesses the commitment of an entire bioinformatics core facility and the training environment of a world-class research institution at UTSW. Establishing a unique computational and imaging framework, combined with biochemical approaches for the study of quiescence, will support the candidate’s transition to an independent research academic position and will lead to the discovery of biomedically relevant principles of quiescence and cell cycle regulation.

抽象的该提案旨在为候选人的长期职业规划提供重要的培训，以研究细胞如何静止是通过决策过程建立的，决定进行静止作为响应。应激或发育信号是生命系统的一个基本且未被充分研究的特性。保持静止会导致人类细胞增殖紊乱，例如纤维化或癌症。当多个营养和压力感应信号通路阻止细胞周期时，就会触发静止状态然而，协调应激反应途径与细胞周期的分子机制。静止期间的情况在很大程度上仍不清楚，部分原因是难以同时量化多个。为了解决这一限制，候选人将使用微流体- 荧光成像系统，可在从使用这种方法，应激反应和细胞周期之间的协调。模型生物酿酒酵母 A 中的机制可以以前所未有的时间分辨率进行量化。基于机器学习和时间序列分析的计算平台将用于处理大量数据成像数据同时来自单细胞中的六个跟踪生物标记。框架发现，在静止期开始时，保守的 DNA 复制激酶的核水平 Cdc7 是动态调节的，这种方法还确定了应激激活的核水平。转录抑制因子 Xbp1 定义了细胞周期在进入静止期间如何停止。生物化学技术的计算方法将确定其分子机制通过调节应激反应和细胞周期机制建立细胞静止。候选人将在本提案的 K99 阶段获得计算生物学方面的重要培训，以他之前在生物化学、细胞生物学和酵母遗传学方面的培训将是对候选人的补充。由计算生物学领域的领导者 Gaudenz Danuser 博士领导，他的导师实验室开发先进的机器该提案利用了这一承诺。 UTSW 的整个生物信息学核心设施和世界一流研究机构的培训环境。建立独特的计算和成像框架，结合生化方法对静止的研究，将支持候选人过渡到独立研究学术职位，并且将导致发现生物医学相关的静止和细胞周期调节原理。