Functional genomic resource and integrative model of dopaminergic circuitry associated with psychiatric disease

与精神疾病相关的多巴胺能回路的功能基因组资源和整合模型

• 批准号: 10579957
• 负责人: Schahram Akbarian
• 金额: $ 66.03万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579957
• 关键词: 3-Dimensional; ATAC-seq; Adult; Architecture; Autopsy; Bayesian Network; Bipolar Disorder; Brain; Brain Stem; Cell Culture Techniques; Cell Nucleus; Cerebral cortex; ChIP-seq; Chromatin; Chromosome Mapping; Cognitive; Collection; Communities; Complex; DNA; Data; Data Set; Diagnosis; Dimensions; Disease; Drug Addiction; Drug abuse; Enhancers; Etiology; Forebrain Development; Functional disorder; Gene Expression; Genes; Genetic; Genetic Diseases; Genetic Risk; Genetic Transcription; Genetic Variation; Genome; Genomics; Genotype; Goals; Greek; Guide RNA; Hi-C; Histone Acetylation; Human; Individual; Information Networks; Maps; Mediating; Mental disorders; Methods; Midbrain structure; Modeling; Molecular Conformation; Moods; NR4A2 gene; National Institute of Mental Health; Neurobiology; Neuroglia; Neurons; Nuclear RNA; Organoids; Pathology; Pathway interactions; Persons; Phenotype; Policies; Prefrontal Cortex; Prosencephalon; Psychoses; Public Health; Regulation; Research; Resources; Sampling; Schizophrenia; Source; Subgroup; Substance abuse problem; Substantia nigra structure; Suicide; System; Testing; Tissues; United States; Validation; Variant; Ventral Tegmental Area; Veterans; Work; cell type; clinical phenotype; cohort; comorbidity; cost; data sharing; differential expression; disease phenotype; dopaminergic neuron; epigenome; epigenome editing; epigenomic profiling; epigenomics; functional genomics; gene network; genetic risk factor; genome resource; genome sequencing; induced pluripotent stem cell; network models; neuropsychiatry; phenotypic data; prediction algorithm; predictive modeling; promoter; psychiatric genomics; reconstruction; recurrent depression; three dimensional structure; trait; transcriptome; transcriptome sequencing; whole genome

PROJECT SUMMARY Great progress has been made in mapping the transcriptome and some its epigenomic determinants of the adult and developing human cerebral cortex (including alterations in common psychiatric disease) through the efforts of the PsychENCODE consortium. However, next to nothing, or very little, is known about genomic regulation in brainstem monoaminergic neurons and their ascending projections into the forebrain, a circuitry critically involved in the pathophysiology of mood and psychosis spectrum disorders and substance abuse disorders, among others. The goal of our project is to construct transcriptome and epigenome (incl. 3D genome/chromosomal conformation) maps for midbrain dopaminergic neurons and for their surrounding non- neuronal cells, and to assess the relationship to known genetic risk factors for complex mental illness, including psychosis with substance abuse co-morbidity. We will apply integrative methods for functional analysis of risk genetic variation and networks, including but not limited to Bayesian network reconstruction and prediction algorithms of variant causality to identify key drivers of schizophrenia and bipolar disease pathology, and drug addiction co-morbidity. These methods will simultaneously integrate multiple different dimensions of data: DNA variation, RNA expression, chromatin accessibility, 3D structure of the genome, known pathway and gene network information in the context of clinical phenotype data. The fundamental source of data for the project comes from the current studies on human midbrain functional omics, and the CommonMinds and PsychENCODE consortia (whole genome sequencing and cortical functional omics data), the Psychiatric Genomics Consortium and the Million Veterans Project (genetic variation and disease phenotypes). The Million Veterans Project (MVP) has collected genotyping and phenotypic data from ~700,000 individuals, including a subgroup of 50,000 veterans diagnosed with SCZ and BD and a larger group of individuals diagnosed with other neuropsychiatric traits (recurrent depression, suicide and substance abuse). We will make our newly generated transcriptome and epigenome datasets from adult midbrain, as well as the network and predictive models, available to the research community in accordance with NIMH data sharing policies.

项目摘要 转录组及其表观基因组决定簇的定位已取得了很大进展， 成人和发育中的人类大脑皮层（包括常见精神病的改变）， PsychENCODE财团的努力。然而，对于基因组学， 调节脑干单胺能神经元及其向前脑的上行投射， 严重参与情绪和精神病谱系障碍和药物滥用的病理生理学 疾病，等等。我们的目标是构建转录组和表观基因组（包括。3D 基因组/染色体构象）图，用于中脑多巴胺能神经元和它们周围的非多巴胺能神经元。 神经元细胞，并评估与复杂精神疾病已知遗传风险因素的关系，包括 精神病与药物滥用共病我们将应用综合方法进行风险的功能分析 遗传变异和网络，包括但不限于贝叶斯网络重建和预测 变量因果关系的算法，以确定精神分裂症和双相情感障碍病理学的关键驱动因素，以及药物 成瘾共病这些方法将同时整合多个不同维度的数据：DNA 变异、RNA表达、染色质可及性、基因组的3D结构、已知途径和基因 在临床表型数据的背景下的网络信息。项目数据的基本来源 来自目前对人类中脑功能组学的研究，以及CommonMinds和 PsychENCODE联合会（全基因组测序和皮质功能组学数据），精神病学 基因组学联盟和百万退伍军人项目（遗传变异和疾病表型）。百万 退伍军人项目（MVP）收集了来自约70万人的基因分型和表型数据，包括一个 一个由50，000名被诊断患有SCZ和BD的退伍军人组成的亚组，以及一个更大的被诊断患有 其他神经精神特征（复发性抑郁症、自杀和药物滥用）。我们将使我们的新 从成人中脑生成的转录组和表观基因组数据集，以及网络和预测 模型，根据NIMH数据共享政策提供给研究界。