Assessing miRNA expression in the Corticolimbic System of Major Depressive Disorder
评估重度抑郁症皮质边缘系统中的 miRNA 表达
基本信息
- 批准号:10810045
- 负责人:
- 金额:$ 45.13万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-04-01 至 2025-01-31
- 项目状态:未结题
- 来源:
- 关键词:AffectAmygdaloid structureAnteriorAutopsyBiologicalBiological AssayBiologyBrainBrain regionClinicalCodeCollaborationsCollectionDataData AnalysesData DiscoveryData SetDevelopmentDiagnosisDiseaseEtiologyFunctional disorderGene ExpressionGeneticGenetic RiskGenetic TranscriptionGenotypeGoalsInterventionKnowledgeLeadLinear RegressionsMajor Depressive DisorderMental disordersMessenger RNAMicroRNAsMolecularMorbidity - disease rateNational Institute of Mental HealthNerve DegenerationNeurobiologyPathologyPatientsPhenotypePlayProcessProteinsProtocols documentationRecording of previous eventsRecurrenceResearchResearch PersonnelResourcesRiskRisk FactorsRoleSample SizeSamplingSeriesSingle Nucleotide PolymorphismSubstance abuse problemSuicideSuicide attemptSystemTestingTimeTissuesUniversitiesVariantVirginiacingulate cortexdata integrationdisorder riskeffective therapyfallsgenetic associationgenetic variantgenome wide association studygenome-wideimprovedinsightmiRNA expression profilingneurobiological mechanismneurodevelopmentneuropathologyneuropsychiatric disordernon-geneticnovelpharmacologicprotein structurerisk variantsevere mental illnesssynaptogenesistraittranscriptometranscriptome sequencingvirtualwhole genome
项目摘要
Contact PD/PI: Vladimirov, Vladimir I
Abstract:
The objective of this proposal is to investigate the biological mechanisms by which microRNAs (miRNAs)
contribute to major depressive disorder (MDD). MDD is a severe mental disorder and the single most common
risk factor for suicide. Recent genome-wide association studies (GWAS) of large samples of MDD have for the
first time identified robust genetic associations with major depression. However, the biological mechanisms by
which these increase the risk for major depression are still unknown. Additionally, most genetic variants
associated with MDD fall outside of the protein coding transcriptome, suggesting that their functional impact is
likely to affect gene expression levels rather than protein structure. MiRNA are highly expressed in the brain and
were shown to play an important role in the pathology of psychiatric disorders including MDD and their canonical
functions are to control gene expression levels. Despite their importance, however, profiling of miRNA expression
in large postmortem brain samples for various neuropsychiatric disorders including MDD across different brain
regions currently do not yet exist.
Thus, in this application we propose to use miRNA sequencing to assess miRNA expression in one of the
largest postmortem brain samples of major depression in the world. The sample has been extensively
characterized clinically, genetically, and molecularly and provides a unique resource for examining the
neurobiological mechanisms by which genetic factors contribute to major depression directly in the primary
affected tissue. Our miRNA data will be integrated with an ongoing RNA sequencing data generated in the same
subjects to identify miRNA/mRNA pairs with important disease functions. Our aims are to: 1) carry out miRNA
sequencing of the subgenual anterior cingulate cortex (sACC) and amygdala in 200 recurrent MDD cases and
200 matched controls, 2) test whether genome-wide significant SNPs from GWAS of MDD are associated with
miRNA expression across these key regions of the brain, 3) identify miRNA whose expression is associated with
major depression and suicide and perform a series of univariate, multivariate (network), and data integration
analyses to further elucidate miRNA role in the neuropathology of MDD, 4) replicate our top miRNA (FDR ≤5%)
in an independently ascertain postmortem brain sample of 50 MDD cases and 50 matched controls. In aim 4 we
will also perform series of exploratory analyses to identify the cellular mechanism by which risk MDD variants
affect miRNA/mRNA interactions. We hypothesize that a major mechanism contributing to the etiology of major
depression is through the ability of risk MDD variants to affect miRNA expression and functions. By explicating
the mechanisms by which risk MDD variants lead to increase risk of major depression, we will provide novel
targets for intervention in the disease process and, therefore, a more rational basis for improved treatments.
联系人 PD/PI:Vladimirov、Vladimir I
抽象的:
该提案的目的是研究 microRNA (miRNA) 的生物学机制
导致重度抑郁症(MDD)。 MDD 是一种严重的精神障碍,也是最常见的一种
自杀的危险因素。最近对 MDD 大样本进行的全基因组关联研究 (GWAS)
首次发现与重度抑郁症有很强的遗传关联。然而,生物学机制
这些会增加患重度抑郁症的风险仍不清楚。此外,大多数基因变异
与MDD相关的蛋白质编码转录组之外,表明它们的功能影响是
可能影响基因表达水平而不是蛋白质结构。 miRNA 在大脑中高度表达
被证明在精神疾病的病理学中发挥着重要作用,包括 MDD 及其典型
功能是控制基因表达水平。然而,尽管 miRNA 表达谱很重要,
在大型死后大脑样本中研究各种神经精神疾病,包括不同大脑的MDD
区域目前尚不存在。
因此,在本应用中,我们建议使用 miRNA 测序来评估其中之一的 miRNA 表达。
世界上最大的重度抑郁症死后大脑样本。该样本已被广泛
临床、遗传和分子特征,并为检查提供了独特的资源
遗传因素直接导致原发性抑郁症的神经生物学机制
受影响的组织。我们的 miRNA 数据将与同一系统中生成的正在进行的 RNA 测序数据集成。
受试者识别具有重要疾病功能的 miRNA/mRNA 对。我们的目标是:1)进行miRNA
对 200 例复发性 MDD 病例的膝下前扣带皮层 (sACC) 和杏仁核进行测序
200 个匹配的对照,2) 测试来自 MDD GWAS 的全基因组显着 SNP 是否与 MDD 相关
大脑这些关键区域的 miRNA 表达,3) 识别其表达与以下因素相关的 miRNA:
重度抑郁症和自杀,并进行一系列单变量、多变量(网络)和数据整合
分析以进一步阐明 miRNA 在 MDD 神经病理学中的作用,4) 复制我们的顶级 miRNA (FDR ≤5%)
在一项独立确定的 50 名 MDD 病例和 50 名匹配对照的死后大脑样本中。在目标 4 中,我们
还将进行一系列探索性分析,以确定风险 MDD 变异的细胞机制
影响 miRNA/mRNA 相互作用。我们假设导致主要病因的一个主要机制
抑郁症是通过风险 MDD 变异影响 miRNA 表达和功能的能力来实现的。通过解释
风险 MDD 变异导致重度抑郁症风险增加的机制,我们将提供新的
疾病过程干预的目标,因此为改进治疗奠定了更合理的基础。
项目成果
期刊论文数量(0)
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Vladimir Ivanov Vladimirov其他文献
Vladimir Ivanov Vladimirov的其他文献
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{{ truncateString('Vladimir Ivanov Vladimirov', 18)}}的其他基金
Translational Approach to Studying miRNA functions in sACC and amygdala in patients with BPD
研究 BPD 患者 sACC 和杏仁核 miRNA 功能的转化方法
- 批准号:
10635583 - 财政年份:2023
- 资助金额:
$ 45.13万 - 项目类别:
Assessing miRNA expression in the Corticolimbic System of Major Depressive Disorder
评估重度抑郁症皮质边缘系统中的 miRNA 表达
- 批准号:
9904778 - 财政年份:2019
- 资助金额:
$ 45.13万 - 项目类别:
Assessing miRNA expression in the Corticolimbic System of Major Depressive Disorder
评估重度抑郁症皮质边缘系统中的 miRNA 表达
- 批准号:
9761634 - 财政年份:2019
- 资助金额:
$ 45.13万 - 项目类别: