High Content Sceening for Hereditary Stroke Syndrome
High Content Sceening for Hereditary Stroke Syndrome
批准号:
8577461
负责人:
DEAN Yaw LI
金额:
$32.59万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-15 至 2016-07-31
关键词:
Animal ModelAnimalsBiological AssayBlood VesselsCCM1 geneCavernous MalformationCell Culture SystemCell LineCellsCerebrumChemicalsChronicClassificationClinical TrialsDataDefectDermalDevelopmentDiseaseEndothelial CellsEndotheliumGenesGeneticHumanImageImaging TechniquesIn VitroInheritedKnockout MiceLesionLibrariesLongitudinal StudiesMachine LearningMagnetic Resonance ImagingMeasuresMedicalMedicineModelingMolecular TargetMusMutationNeuraxisPathogenesisPathway interactionsPatientsPatternPermeabilityPhenotypePilot ProjectsReportingRheumatoid ArthritisSepsisSmall Interfering RNAStrokeSyndromeTechniquesTelangiectasisTestingTherapeuticToxic effectUniversitiesUtahVascular DiseasesWorkbaseelectric impedanceflexibilityhigh throughput analysisin vitro Assayin vivomonolayermouse modelnervous system disorderneurosurgerynovelpharmacophorepreclinical studypublic health relevancescreeningsmall moleculesmall molecule librariestempol
中文摘要
描述(由申请人提供):脑海绵状血管畸形(CCM)是一种遗传性卒中综合征,除神经外科手术外无其他治疗方法。三个基因之一的缺失导致CCM。通过减少这些基因之一(CCM 2)在血管细胞(内皮细胞)中的表达,产生独特的结构和功能表型。我们将
在细胞培养系统中使用化学抑制筛选来发现拯救这些不同表型的小分子。我们的高通量平台由两个主要屏幕组成:第一个是基于机器学习的表型分析的成像屏幕;第二个屏幕使用电细胞基质阻抗传感来检测内皮屏障功能的变化。然后,我们将在人类CCM疾病的小鼠模型中进一步筛选最佳候选化合物。该项目的结果将是治疗由CCM 2基因突变引起的CCM的个性化药物候选人。
英文摘要
DESCRIPTION (provided by applicant): Cerebral Cavernous Malformation (CCM) is a hereditary stroke syndrome that has no treatment other than neurosurgery. Loss of one of three genes causes CCM. By reducing the expression of one of these genes (CCM2) in the cells that line blood vessels (endothelial cells), unique structural and functional phenotypes result. We will
use a chemical suppression screen in cell culture systems to discover small molecules that rescue these distinct phenotypes. Our high-throughput platform consists of two primary screens: the first is an imaging screen with machine-learning based phenotype analysis; the second screen uses electric cell substrate impedance sensing to detect changes in endothelial barrier function. We will then further screen the top candidate compounds in a mouse model of human CCM disease. The result of this project will be personalized medicine candidates for the treatment of CCM caused by mutations in the CCM2 gene.
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会议论文
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