Systems Pharmacology for overcoming cell variability

克服细胞变异性的系统药理学

• 批准号: 10437864
• 负责人: Srinivas Ravi V Iyengar
• 金额: $ 46.92万
• 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/10437864
• 关键词: Ablation; Adult; Agonist; Axon; Biological; Biological Models; CNR1 gene; Caliber; Cell model; Cell physiology; Cells; Characteristics; Clustered Regularly Interspaced Short Palindromic Repeats; Combination Drug Therapy; Combined Modality Therapy; Complex; Computer Models; Coupled; Couples; Data; Databases; Development; Disease; Drug Combinations; Drug usage; Electrophysiology (science); Fiber; Fluorescent in Situ Hybridization; Functional disorder; Funding; GTP-Binding Proteins; Gene Expression; Genes; Genetic Transcription; Grant; Growth; Growth Cones; Histologic; In Vitro; Injury; Interleukin 6 Receptor; Interleukin-6; Kinetics; Knowledge; Lead; Length; Ligands; Light; Location; Logic; Maps; Measures; Mediating; Medicine; Membrane; Microtubule Stabilization; Microtubules; Modeling; Molecular; Monitor; Morphology; Movement; Natural regeneration; Nerve Crush; Nerve Regeneration; Neurites; Neurons; Optic Nerve; Optic Nerve Injuries; Organ; Paclitaxel; Pathway interactions; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Phenotype; Play; Population; Predisposition; Proteomics; Rattus; Receptor Activation; Recovery; Regulation; Regulatory Pathway; Research Project Grants; Role; STAT3 gene; Science; Serine Protease; Signal Transduction; Site; Small Interfering RNA; Stimulus; System; Systems Biology; Testing; Time; Tissues; Transcript; Vesicle; Visual Cortex; activated Protein C; axon regeneration; base; bioinformatics network; cell type; cellular targeting; college; density; design; differential expression; drug testing; experimental study; graph theory; in vivo; mRNA sequencing; nerve injury; network models; neurite growth; promoter; receptor; response; restoration; single cell technology; single-cell RNA sequencing; synergism; 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.

项目概要： 单细胞RNA-Seq的最新技术进展突出了 迄今为止，在广泛的组织中，许多细胞类型中存在未被认识到的细胞间变异性， 机关这种变异性导致单一类型细胞的多种亚型。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 治疗复杂疾病一种系统药理学方法， 亚型的反应可用于开发有效的联合治疗。我们 系统药理学方法包括计算建模的集成， 联合收割机结合图论和动力学模型分析单细胞转录组数据， 确定模型系统中涉及的相关监管途径和子网络， 对受体刺激的全细胞反应，其在体内可以从 病理生理学对药物的反应。基于这些标准，我们一直在研究G蛋白 偶联大麻素1受体调节体外原代神经元的轴突生长， 用于联合药物治疗的靶向节点，可以测试其治疗视神经损伤 in rats大鼠in vivo体内.损伤后，两种受体激动剂药物应用于细胞体，另两种受体激动剂药物应用于细胞体。 在损伤部位的两种药物恢复视觉中的光依赖性电生理信号， 皮层虽然我们在视觉皮层中可靠地看到信号，但恢复信号的幅度是 小了我们假设，在细胞亚型中识别出负责长神经突的基因 使用单细胞RNA-Seq将绘制细胞机制，以确定用于再生的药物， 致密的轴突束，并导致光刺激的电生理学的更大恢复 视觉皮层的信号。为了验证这一假设，我们有三个具体目标：1）将分析 单细胞转录组学对受体活化反应的变异性，以确定决定因素 控制细胞在细胞群中长出长的神经突。2)将使用计算系统 生物学开发集成网络和动力学模型，以识别亚细胞过程 以及调节长神经突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延长药物是否沿着或 取代目前的四种药物组合导致再生的密度增加， 纤维和更高幅度的电生理反应的视觉皮层。我们 预计这将提供亚细胞过程的一般基本理解， 控制全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。