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

项目摘要 细胞通过信号通路对各种刺激做出反应。这些途径调节转录 因子活性、靶基因表达以及细胞状态和决定的变化。它现在很好- 确定了通路活动的时间动态在信号转导中起关键作用。然而，在这方面， 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。的 当这些反应是：（i）受到多个组合控制时，挑战是特别艰巨的 由共同或不同的配体-受体相互作用编码的途径，（ii）由多种 独立的或共调节的转录因子，和（iii）被细胞环境改变，例如分化 状态尽管对细胞信号传导机制的理解有所增加，但这些挑战 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 我们的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，以及这些动态如何协调 “情境依赖”和“刺激特异性”反应。我们建议的研究计划侧重于激活剂 蛋白1（AP-1），转录因子的经典范例，细胞利用其来协调对转录因子的应答。 环境变化的多样性，从而决定是否划分，区分，适应环境，或 死的虽然AP-1因子的分子调控已被广泛研究，但它们如何发挥作用， 作为一个动态的网络，以及这个网络如何整合ERK，JNK和p38信号转导的模式来调节 驱动多样性和环境依赖性细胞决定的基因表达程序仍然不清楚。 知识上的差距主要是由于缺乏系统范围的测量，单细胞精度， 和计算建模在以前的研究AP-1的动力学，其中相互依赖性之间的 整个AP-1家族蛋白（包括Jun、Fos和密切相关的ATF亚家族）， 相互作用，翻译后修饰，上游调控因子及其合作伙伴仍然存在 不完全的映射。在这项研究计划中，我们将开发一个综合平台，结合高- 通量、高度多路复用测量、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。我们将会用这些 工具：（1）揭示AP-1动力学的不同组合模式如何介导多种多样的 看似无关的功能，（2）解码ERK中编码的刺激特异性信息的逻辑， JNK和p38通路动力学被传递到AP-1网络，以及（3）定义了 该网络将该信息与细胞内在因素整合以驱动上下文相关的决策。从 更好地了解这些基本机制，我们可以学会改善健康的反应。 细胞对有害刺激的反应，并制定策略，在必要时诱导选择性杀死不健康的细胞。

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.