Decoding the Logic of Cellular Signaling Through the Integration of Dynamic, Single-Cell and Multiplexed Methods

通过动态、单细胞和多重方法的集成解码细胞信号传导的逻辑

• 批准号: 9796251
• 负责人: Mohammad Fallahi-Sichani
• 金额: $ 36.22万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9796251
• 关键词: Cell physiology; Cells; Computer Simulation; Disease; Environment; Family; Gene Expression; Genes; Goals; Intrinsic factor; Knowledge; Learning; Ligands; Logic; MAPK14 gene; MAPK8 gene; Maps; Measurement; Mediating; Methods; Molecular; Pathway interactions; Pattern; Pharmaceutical Preparations; Play; Post-Translational Protein Processing; Process; Protein Family; Regulation; Research; Signal Pathway; Signal Transduction; Stimulus; Stress; System; Thromboplastin; Transcription Factor AP-1; activating transcription factor; activator 1 protein; combinatorial; cytokine; environmental change; genome-wide analysis; improved; precision medicine; predicting response; programs; receptor; response; single cell technology; tool; transcription factor

PROJECT SUMMARY Cells respond to a wide range of stimuli through signaling pathways. These pathways modulate transcription factor activities, expression of target genes and changes in cellular states and decisions. It is now well- established that the temporal dynamics of pathway activities play a key role in signal transduction. However, decoding the logic by which these dynamic patterns determine cellular response is still a challenging goal. The challenge is particularly formidable when these responses are: (i) subject to combinatorial control by multiple pathways encoded by common or distinct ligand-receptor interactions, (ii) mediated by a multiplicity of independent or co-regulated transcription factors, and (iii) altered by the cellular context, e.g. differentiation state. These challenges, despite an increased understanding of cellular signaling mechanisms, have complicated our ability to accurately predict the response of cells to stress, ligands and drugs. Our long-term goal is to understand how cells process dynamic information from combinations of tightly regulated signaling pathways to modulate downstream transcription factor dynamics, and how such dynamics coordinate both “context-dependent” and “stimulus-specific” responses. Our proposed research program focuses on Activator Protein 1 (AP-1), a classical paradigm for transcription factors, which cells utilize to orchestrate responses to a variety of environmental changes, and thereby decide whether to divide, differentiate, adapt to environment, or die. While the molecular regulation of the AP-1 factors have been extensively investigated, how they function as a dynamic network, and how this network integrates patterns of ERK, JNK and p38 signaling to regulate gene expression programs that drive diverse and context-dependent cell decisions, have remained unclear. The gap in knowledge has been largely due to the lack of system-wide measurements, single-cell precision, and computational modeling in the previous studies of AP-1 dynamics, in which interdependencies between a whole array of AP-1 family proteins (including Jun, Fos and closely related ATF sub-families), their interactions, post-translational modifications, upstream regulators and their partners have remained incompletely mapped out. In this research program, we will develop an integrated platform, combining high- throughput, highly multiplexed measurements, single-cell technologies in live and fixed cells, genome-wide analysis and computational modeling, as a means to overcome these gaps and challenges. We will use these tools to: (1) uncover how distinct combinatorial patterns of AP-1 dynamics mediate a diverse range of seemingly unrelated functions, (2) decode the logic by which stimulus-specific information encoded in ERK, JNK and p38 pathway dynamics is transmitted to the AP-1 network, and (3) define the mechanisms by which the network integrates this information with cell-intrinsic factors to drive context-dependent decisions. From a better understanding of these fundamental mechanisms, we can learn to improve the responses of healthy cells to harmful stimuli, and develop strategies to induce selective killing in unhealthy cells when necessary.

项目摘要 细胞通过信号通路对广泛的刺激做出反应。这些途径调节转录 因子活动，靶基因的表达以及细胞状态和决策的变化。现在很好 确定途径活动的临时动力学在信号转导中起着关键作用。然而， 解码这些动态模式确定细胞响应的逻辑仍然是一个挑战目标。 当这些响应是：（i）由多个进行组合控制时，挑战特别强大 通过常见或不同的配体 - 受体相互作用编码的途径，（ii）由多样性介导 独立或共同调节的转录因子，（iii）因细胞上下文而改变，例如分化 状态。这些挑战，希望对细胞信号传导机制有更多的了解， 使我们准确预测细胞对压力，配体和药物的反应的能力变得复杂。我们的长期 目标是了解细胞如何从严格调节信号的组合中处理动态信息 调节下游转录因子动力学的途径，以及这种动力学如何坐标 “与上下文有关”和“刺激特定”响应。我们提出的研究计划的重点是激活器 蛋白1（AP-1），一种用于转录因子的经典范例，细胞利用对A的响应进行编排 各种环境变化，从而决定是分裂，区分，适应环境还是 死。尽管已经广泛研究了AP-1因子的分子调节，但它们的功能如何 作为动态网络，以及该网络如何集成ERK，JNK和P38信号的模式以调节 驱动多样化和与上下文依赖性细胞决策的基因表达程序尚不清楚。 知识的差距很大程度上是由于缺乏全系统测量值，单细胞精度， 以及先前对AP-1动力学的研究中的计算建模，其中一个相互依存关系 全部AP-1家族蛋白（包括JUN，FOS和密切与ATF亚元素），它们 互动，翻译后修改，上游监管机构及其合作伙伴仍然存在 未完全映射。在该研究计划中，我们将开发一个集成平台，结合高度 吞吐量，高度多路复用的测量，活细胞和固定细胞中的单细胞技术，全基因组 分析和计算建模，作为克服这些差距和挑战的手段。我们将使用这些 工具：（1）发现AP-1动力学的不同组合模式如何介导潜水员的范围 看似无关的函数，（2）解码逻辑，该逻辑在ERK中编码的刺激特定信息， JNK和P38途径动力学已传输到AP-1网络，（3）定义了该机制 该网络将这些信息与细胞中的因素集成在一起，以推动与上下文有关的决策。来自 更好地了解这些基本机制，我们可以学会改善健康的反应 细胞进行有害刺激，并在必要时制定策略以在不健康的细胞中诱导选择性杀死。