Systems Pharmacology for overcoming cell variability

克服细胞变异性的系统药理学

• 批准号: 10810110
• 负责人: Srinivas Ravi V Iyengar
• 金额: $ 2.07万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10810110
• 关键词: Agonist; Axon; Biological; Biological Models; CNR1 gene; Cell physiology; Cells; Combination Drug Therapy; Combined Modality Therapy; Complex; Computer Models; Coupled; Data; Development; Disease; Drug Combinations; Electrophysiology (science); Fiber; Functional disorder; GTP-Binding Proteins; Genes; In Vitro; Injury; Light; Maps; Modeling; Natural regeneration; Neurites; Neurons; Optic Nerve; Optic Nerve Injuries; Organ; Pathway interactions; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Population; Rattus; Receptor Activation; Recovery; Regulatory Pathway; Signal Transduction; Site; System; Systems Biology; Technology; Testing; Tissues; Visual Cortex; cell type; density; design; graph theory; in vivo; nerve injury; receptor; response; restoration; single cell technology; single-cell RNA sequencing; transcriptomics; treatment response

Project Summary: Recent technological advances in single cell RNA-Seq have highlighted the possibility of a hitherto unrecognized cell-to cell variability in many cell types across a wide range of tissues and organs. Such variability results in the multiple subtypes of cells of a single type. This variability results in differing cell biological capabilities, which has important consequences for drug therapy for complex diseases. A systems pharmacology approach that takes into account variable responses of the subtypes could be useful in development of effective combination therapy. Our systems pharmacology approaches includes integration of computational modeling whereby we combine graph theory and dynamical models to analyze single cell transcriptomic data so as to identify relevant regulatory pathways and subnetworks involved in a model system that produces a whole cell response to receptor stimulation which in vivo can play a role recovery from pathophysiology in response to drugs. Based on these criteria we have been studying G protein coupled cannabinoid 1 receptor regulated neurite outgrowth of primary neurons in vitro to identify targetable nodes for combination drug therapy that can be tested to treat injury to the optic nerve in rats in vivo. After injury, two receptor agonists drugs applied at the cell body and the two other two drugs at the injury site restores light dependent electrophysiological signals in the visual cortex. Although we see signal reliably in the visual cortex, the amplitude of restored signal is small. We hypothesize that identifying genes responsible for long neurites in subtypes of cells using single cell RNA-Seq will map cellular mechanisms to identify drugs for regeneration of denser axonal bundles and lead to greater restoration of the light stimulated electrophysiological signals in the visual cortex. To test this hypothesis we have three specific aims: 1) Will analyze variability of single cell transcriptomic responses to receptor activation to identify the determinants that control cells to put out long neurites in a population of cells. 2) Will use computational systems biology to develop integrated network and dynamical models to identify the subcellular processes and drugs that regulate the expression of up and downregulated genes in cells with long neurites. 3) Will use the optic nerve injury model in rats to test if neurite lengthening drugs along with or substituting for the current four-drug combination results in increased density of regenerated fibers and higher amplitude of the electrophysiological responses in the visual cortex. We anticipate this will provide general fundamental understanding of the subcellular processes that control cell-to cell variability in whole cell responses and how to use it for efficacious drug therapy.

项目总结： 最近在单细胞RNA-Seq方面的技术进步突显了一种 到目前为止，在广泛的组织和组织中，许多细胞类型的细胞间变异性尚未被识别 器官。这种可变性导致了单一类型的细胞的多个亚类型。这种可变性 导致不同的细胞生物学能力，这对药物治疗具有重要影响 对于复杂的疾病。一种考虑变量的系统药理学方法 这些亚型的反应可能有助于开发有效的联合治疗。我们的 系统药理学方法包括计算建模的集成，由此我们 将图论和动力学模型结合起来分析单细胞转录数据，以便 确定模型系统中涉及的相关监管路径和子网络 对受体刺激的全细胞反应，在体内可以起到恢复的作用 对药物作出反应的病理生理学。基于这些标准，我们一直在研究G蛋白 偶联大麻素1受体调控原代神经元突起生长的体外鉴定 可用于联合药物治疗的靶向结节，可用于治疗视神经损伤 在活体大鼠体内。损伤后，两种受体激动剂药物应用于细胞体和另外两种 损伤部位的两种药物恢复视觉中的光依赖电生理信号 大脑皮层。虽然我们在视皮层可以可靠地看到信号，但恢复的信号的幅度是 小的。我们假设，在不同类型的细胞中识别与长轴突有关的基因 使用单细胞RNA-Seq将映射细胞机制以识别用于再生的药物 更密集的轴突束，并导致更大的恢复光刺激的电生理 视觉皮质中的信号。为了验证这一假设，我们有三个具体目标：1)将分析 单细胞转录反应对受体激活的可变性以确定决定因素 控制细胞在一群细胞中长出长的神经突起。2)将使用计算系统 生物学发展综合网络和动力学模型以识别亚细胞过程 以及调节长神经突起细胞中上调和下调基因表达的药物。 3)将使用大鼠视神经损伤模型来测试神经突起延长药物是否与或 替代目前的四联用药导致再生密度增加。 视皮层的神经纤维和较高的电生理反应幅度。我们 预计这将提供对亚细胞过程的一般基本理解， 控制整个细胞反应中的细胞间变异性，以及如何将其用于有效的药物治疗。