Systems Pharmacology for overcoming cell variability

克服细胞变异性的系统药理学

基本信息

批准号：
10656377
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
项目类别：
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/10656377
关键词：
Ablation Adult Agonist Anesthesia procedures Axon Bioinformatics Biological Biological Models CNR1 gene Cell model Cell physiology Cells Characteristics Classification Clustered Regularly Interspaced Short Palindromic Repeats Color Combination Drug Therapy Combined Modality Therapy Complex Computer Models Coupled Couples Data Databases Development Diameter 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 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 Population Predisposition Proteomics Rattus Receptor Activation Recovery Regulation Regulatory Pathway Reproducibility Research Project Grants Role STAT3 gene Science Serine Protease Signal Transduction Site Small Interfering RNA Stimulus System Systems Biology Technology Testing Tissues Transcript Vesicle Visual Cortex activated Protein C axon regeneration cell type cellular targeting college density design differential expression 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 success synergism transcriptomics treatment response

项目摘要

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.

项目摘要：单细胞RNA-seq的最新技术进步强调了一种可能性迄今为止在许多细胞类型中，尚未识别的细胞对细胞变异性在广泛的组织中的许多细胞类型和器官。这种可变性导致单型细胞的多个亚型。这种变异性导致不同的细胞生物学能力，这对药物治疗产生了重要的影响对于复杂的疾病。考虑变量的系统药理学方法亚型的反应对于开发有效的组合疗法可能很有用。我们的系统药理学方法包括集成计算建模，我们结合图理论和动态模型来分析单细胞转录组数据，以便确定与产生模型系统有关的相关监管途径和子网整个细胞对受体刺激的反应，体内可以从中扮演角色恢复病理生理对药物的反应。基于这些标准，我们一直在研究G蛋白偶联的大麻素1受体调节的原发性神经元的神经突在体外，以鉴定可以测试以治疗视神经的损伤的组合药物治疗的有针对性节点在体内的老鼠中。受伤后，两种受体激动剂药物在细胞体和其他两个受伤部位的两种药物恢复了视觉中依赖光的电生理信号皮质。尽管我们在视觉皮层中可靠地看到信号，但还原信号的幅度为小的。我们假设识别细胞亚型中长神经突的基因使用单细胞RNA-seq将绘制细胞机制，以识别用于再生的药物浓密的轴突束，并导致更大的光刺激电生理学恢复视觉皮层中的信号。为了检验这一假设，我们有三个具体目标：1）将分析单细胞转录组对受体激活的可变性以识别决定因素控制细胞在细胞群中发出长长的神经突。 2）将使用计算系统生物学开发集成网络和动态模型以识别亚细胞过程以及调节长神经突细胞中向上和下调基因表达的药物。 3）将在大鼠中使用视神经损伤模型来测试神经突加长药物以及OR 代替当前的四药组合导致再生的密度增加视觉皮层中电生理反应的纤维和较高的振幅。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对细胞变异性以及如何将其用于有效的药物治疗。