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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：
10246261
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/10246261
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

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.

项目总结: