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: 10581813
• 负责人: Mohammad Fallahi-Sichani
• 金额: $ 14.9万
• 依托单位: UNIVERSITY OF VIRGINIA
• 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 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10581813
• 关键词: Cell physiology; Cells; Computer Models; Disease; Environment; Family; Gene Expression; Genes; Goals; Intrinsic factor; Knowledge; Learning; Ligands; Logic; 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; combinatorial; cytokine; environmental change; genome-wide analysis; improved; p38 Mitogen Activated Protein Kinase; 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.

项目概要 细胞通过信号通路对多种刺激做出反应。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。现在已经好了—— 确定通路活动的时间动态在信号转导中发挥关键作用。然而， 解码这些动态模式决定细胞反应的逻辑仍然是一个具有挑战性的目标。这 当这些响应是：（i）受到多个组合控制时，挑战尤其艰巨 由共同或不同的配体-受体相互作用编码的途径，（ii）由多种介导 独立或共同调节的转录因子，以及（iii）被细胞环境改变，例如差异化 状态。尽管人们对细胞信号传导机制的了解有所增加，但这些挑战仍然存在 使我们准确预测细胞对压力、配体和药物反应的能力变得更加复杂。我们的长期 目标是了解细胞如何处理来自严格调控的信号组合的动态信息 调节下游转录因子动态的途径，以及这种动态如何协调两者 “依赖于情境”和“特定于刺激”的反应。我们提出的研究计划重点关注激活剂 蛋白质 1 (AP-1)，转录因子的经典范例，细胞利用它来协调对转录因子的反应 各种环境变化，从而决定是否分化、分化、适应环境或 死。虽然 AP-1 因子的分子调控已被广泛研究，但它们如何发挥作用 作为一个动态网络，以及该网络如何整合 ERK、JNK 和 p38 信号传导模式来调节 驱动多样化且依赖于环境的细胞决策的基因表达程序仍不清楚。 知识差距很大程度上是由于缺乏全系统测量、单细胞精度、 以及之前 AP-1 动力学研究中的计算模型，其中 整个 AP-1 家族蛋白（包括 Jun、Fos 和密切相关的 ATF 亚家族），它们的 相互作用、翻译后修饰、上游监管机构及其合作伙伴仍然存在 未完全绘制出来。在这个研究计划中，我们将开发一个集成平台，结合高 通量、高度多重测量、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) 揭示 AP-1 动力学的不同组合模式如何介导各种不同的 看似不相关的功能，(2) 解码 ERK 中编码的特定刺激信息的逻辑， JNK 和 p38 通路动态传输至 AP-1 网络，并且 (3) 定义其机制 该网络将这些信息与细胞内在因素整合起来，以驱动依赖于上下文的决策。来自一个 更好地了解这些基本机制，我们可以学习改善健康的反应 细胞对有害刺激的反应，并在必要时制定诱导选择性杀死不健康细胞的策略。