Systems Genetics of Fluoxetine-Induced Neurogenesis and Antidepressant Response
Systems Genetics of Fluoxetine-Induced Neurogenesis and Antidepressant Response
批准号:
8300480
负责人:
James Joseph Crowley
金额:
$15.67万
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-03-01 至 2016-02-29
关键词:
AdultAdverse effectsAnimalsAntidepressive AgentsBehavioralBiologicalBiological MarkersBiologyBreedingBromodeoxyuridineCandidate Disease GeneChronicClinical MedicineClinical TrialsComplexComputer SimulationDNADataData SetDissectionEpigenetic ProcessFDA approvedFluoxetineFundingGene Expression ProfileGenesGeneticGenetic screening methodGenomicsGenotypeGoalsHaloperidolHaplotypesHeterogeneityHippocampus (Brain)HumanImmunohistochemistryInbreedingIndividual DifferencesInvestmentsK-Series Research Career ProgramsLasersMapsMeasuresMentorsMethylationModelingMolecularMolecular GeneticsMolecular ProfilingMolecular TargetMotor ActivityMouse StrainsMusPartner in relationshipPathway AnalysisPatientsPatternPharmaceutical PreparationsPharmacogenomicsPhenotypePhysiciansPlacebo EffectPlacebosPopulation HeterogeneityProzacPsychiatryRNAReactionRecombinantsResourcesRodentSamplingSliceSystemTail SuspensionTechnologyTestingTherapeuticTimeTissuesUnited States National Institutes of HealthWorkbasecostdentate gyrusdesignexperiencefollow-upgenetic associationgenome wide association studygenome-widehuman subjectmalemouse genomeneurogenesisnext generationnovelpre-clinicalresearch studyresponseskillstrait
中文摘要
描述(由申请人提供):
在这个项目中,候选人建议阐明临床前系统中重要的抗抑郁反应表型的遗传基础,即来自协作杂交(CC)小鼠品系的重组近交系(Rix)。实验将使用氟西汀(百忧解),一种高度处方的抗抑郁药。虽然氟西汀没有重大副作用,但只有大约50%的患者有治疗反应。首先,我们将成年雄性Rix暴露在与人类相似的稳定浓度的氟西汀中(250只暴露在笼子里,250只对照笼友),并评估与啮齿动物抗抑郁反应相关的两种表型的变化:尾部悬挂试验中的行为绝望和海马神经发生的定量测量。对于这些性状中的每一个,我们将使用现有的基因组数据来进行全基因组关联图谱和路径分析。其次,我们使用新的和强大的方法来评估海马齿状回转录组和甲基组(下一代测序技术)来提炼这些关联。第三,我们通过产生预期表现出高或低氟西汀敏感性(N=10Rix)的新动物来测试这些遗传和分子生物标记物的预测有效性。这项临床前系统药物基因组学项目旨在确定调节小鼠对氟西汀反应的关键基因。后续工作将检查人类临床试验样本中的这些候选基因。
公共卫生相关性:
本研究的目的是了解氟西汀(百忧解)是如何起作用的。虽然氟西汀是处方最多的精神药物之一,但不幸的是,它只对一半的患者有效。如果有可能提前知道患者对氟西汀没有反应,
比方说,通过基因测试,医生可以开出不同的药物。由于在人类中很难识别导致药物反应的个体间差异的关键基因,
这个项目的目标是在条件可以得到更严格监管的地方使用老鼠。我们还建议使用一套现代技术来深入了解。
英文摘要
DESCRIPTION (provided by applicant):
In this project, the candidate proposes to elucidate the genetic basis of important antidepressant response phenotypes in a pre-clinical system, recombinant-inbred intercrosses (RIX) from the Collaborative Cross (CC) mouse lines. Experiments will be performed using fluoxetine (Prozac), a highly prescribed antidepressant. While fluoxetine does not have major side effects, only about 50% of patients experience a therapeutic response. First, we will expose adult male RIX to human-like steady state concentrations of fluoxetine (250 exposed, 250 control cage mates) and assess changes in two phenotypes relevant to antidepressant response in rodents: behavioral despair in the tail suspension test and quantitative measures of hippocampal neurogenesis. For each of these traits, we will use existing genomic data to conduct genome-wide association mapping and pathway analysis. Second, we refine these associations using new and powerful ways to assess the hippocampal dentate gyrus transcriptome and methylome (next generation sequencing technologies). Third, we test the predictive validity of these genetic and molecular biomarkers by generating novel animals expected to show high or low fluoxetine sensitivity (N=10 RIX each). This pre-clinical systems pharmacogenomics project is powered to identify key genes regulating response to fluoxetine in mice. Follow- up work will then examine these candidate genes in human clinical trial samples.
PUBLIC HEALTH RELEVANCE:
The goal of this study is to understand how fluoxetine (Prozac) works. While it is among the most highly prescribed psychiatric drugs, unfortunately fluoxetine works in only half of all patients. If it were possible to know ahead of time that a patient would not respond to fluoxetine,
say by a genetic test, the physician could prescribe a different drug. Since in humans it is very difficult to identify the key genes responsible for inter- individual differences in drug response,
this project aims to use mice where conditions can be more tightly regulated. We also propose to use a modern set of technologies to get an in-depth understanding.
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