1/2 Large-scale, single-cell characterization of molecular and cellular networks of mood regulation circuitry in major depressive disorder

1/2 重度抑郁症情绪调节回路的分子和细胞网络的大规模单细胞表征

• 批准号: 10744931
• 负责人: Fernando Sampaio Goes
• 金额: $ 50.15万
• 依托单位: LIEBER INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10744931
• 关键词: Address; Affect; African American; African American population; Amygdaloid structure; Anterior; Antidepressive Agents; Area; Autopsy; Biological; Biological Assay; Bipolar Disorder; Brain; Brain region; Cell Nucleus; Cells; Chromosome Mapping; Cognitive; Data; Diagnostic; Disease; Disease remission; Emotional; Emotions; Etiology; European; Female; Functional disorder; Gatekeeping; Gene Expression; Gene set enrichment analysis; Genes; Genetic; Genetic Transcription; Genomics; Hippocampus; Human; Human Resources; Impairment; Individual; Investigation; Ligands; Link; Machine Learning; Major Depressive Disorder; Maps; Mediating; Mental Depression; Mental disorders; Metabolic; Modeling; Molecular; Mood Disorders; Moods; Morbidity - disease rate; Negative Valence; Network-based; Neurobiology; Neurosciences; Outcome; Pathway interactions; Play; Process; Protocols documentation; Psychopathology; Pyramidal Cells; Quantitative Genetics; Quantitative Trait Loci; Research; Research Domain Criteria; Resolution; Resources; Role; Sample Size; Sampling; Series; Sexual Dysfunction; Suicide; Techniques; Work; brain cell; burden of illness; caucasian American; cell type; cingulate cortex; cohort; convolutional neural network; deep learning; depressive symptoms; functional genomics; gene network; genome wide association study; genome-wide; genomic locus; innovation; mood regulation; multi-ethnic; negative mood; neural network; novel; personalized intervention; receptor; response; sex; single episode major depressive disorder; single nucleus RNA-sequencing; transcriptome; transcriptomics; treatment strategy

SUMMARY While there is strong evidence supporting the role of the anterior cingulate cortex, basolateral amygdala, and the hippocampus (ACC, BLA, HIPP) as a key neural network regulating mood, and therefore central to the pathophysiology of major depressive disorder (MDD), much remains unknown, including which gene pathways and which specific cell types play a primary causal role mediating alterations in this circuit, and what cell-type connections, within and between these regions, are particularly altered in depressive states. The overall objective of this application is to generate single-cell transcriptomic profiles to study molecular changes, including those specific to genetic ancestry and sex, associated with MDD in the mood regulation circuit. While disease burden is greater in African Americans, the impact of genetic ancestry remains unknown as most genomic studies in MDD so far have been limited to subjects of European descent. In addition, previous studies revealed that transcriptomic changes associated with MDD are sex-specific, and gene networks are differentially dysregulated between sexes. The applicants’ recent single-cell brain study revealed cell-specific contributions to transcriptomic changes associated with MDD. The proposed project is a large-scale, systematic investigation in the ACC, BLA, and HIPP to interrogate the transcriptome at single-nucleus resolution in an unprecedently large and representative sample of MDD. The specific aims are to: 1) Identify, at the single-cell level transcriptomic changes associated with MDD in 800 subjects across three linked brain regions: ACC, BLA, and HIPP; 1b) Study the impact of genetic ancestry and sex; 2) Define cell networks associated with mood regulation using machine learning approaches; and 3) Identify cell-specific expression Quantitative Trait Loci (eQTLs) colocalizing with genome-wide significant SNPs identified in MDD GWAS analyses. A large cohort (N=800) of human post-mortem samples obtained from subjects with MDD will be compared to psychiatrically-healthy controls. The sample (~20% African American and ~30% female) will allow for studying the impact of genetic ancestry and sex. Droplet-based single-nucleus RNA sequencing will be applied to generate transcriptomic profiles. Deep learning approaches will be used to identify and annotate the cell types and gene networks associated MDD. The latest GWAS data in MDD will be leveraged to fine map genetic loci with cellular and regional resolution. The proposed research is innovative because it is the first large-scale investigation of the ACC-BLA-HIPP circuit in humans and will represent the largest single-cell transcriptional resource of the human brain. It will identify gene and cellular networks associated with sex or genetic ancestry, and will also generate a vast amount of transcriptomic data on neurotypical brains. This research is significant because it will greatly advance our understanding of the cellular and molecular pathways involved in mood regulation and MDD. Through a better understanding of the mechanisms of depressive illness, we may be one step closer to developing novel treatment strategies and personalize interventions.

概括 虽然有强有力的证据支持前扣带皮层、基底外侧杏仁核和 海马体（ACC、BLA、HIPP）作为调节情绪的关键神经网络，因此对情绪至关重要 重度抑郁症 (MDD) 的病理生理学仍有很多未知之处，包括哪些基因途径 哪些特定的细胞类型在介导该回路的改变中起主要因果作用，以及哪些细胞类型 在抑郁状态下，这些区域内部和之间的联系尤其会发生变化。 该应用程序的目的是生成单细胞转录组图谱以研究分子变化， 包括那些与情绪调节回路中的 MDD 相关的遗传血统和性别特有的基因。 非裔美国人的疾病负担更大，遗传血统的影响仍然未知，因为大多数人 迄今为止，MDD 的基因组研究仅限于欧洲血统的受试者。 研究表明，与 MDD 相关的转录组变化具有性别特异性，并且基因网络也具有性别特异性。 申请人最近的单细胞大脑研究揭示了细胞特异性。 对与 MDD 相关的转录组变化的贡献 拟议的项目是一个大规模的、 在 ACC、BLA 和 HIPP 中进行系统研究以询问单核转录组 在前所未有的大规模且具有代表性的 MDD 样本中进行解决 具体目标是： 1) 识别，在。 在 800 名受试者的三个相连大脑中，与 MDD 相关的单细胞水平转录组变化 区域：ACC、BLA 和 HIPP；1b) 研究遗传血统和性别的影响；2) 定义细胞网络； 使用机器学习方法与情绪调节相关；3) 识别细胞特异性表达 数量性状位点 (eQTL) 与 MDD GWAS 中鉴定的全基因组重要 SNP 共定位 将对从 MDD 受试者获得的大量（N=800）人类尸检样本进行分析。 与精神健康的对照组相比，样本（约 20% 非洲裔美国人和约 30% 女性）将允许。 用于研究遗传血统和性别的影响。 用于生成转录组图谱的深度学习方法将用于识别和注释。 MDD 相关的细胞类型和基因网络将用于精细绘制 MDD 地图。 所提出的研究具有创新性，因为它是第一个。 对人类 ACC-BLA-HIPP 回路的大规模研究，将代表最大的单细胞 它将识别与性别或性别相关的基因和细胞网络。 遗传祖先，还将产生大量关于神经典型大脑的转录组数据。 研究意义重大，因为它将极大地增进我们对细胞和分子途径的理解 通过更好地了解抑郁症的机制，参与情绪调节和抑郁症。 如果我们患有疾病，我们可能会更接近开发新的治疗策略和个性化干预措施。