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 尽管有强有力的证据支持前扣带回皮质的作用，但 海马（ACC，BLA，HIPP）作为关键的神经网络调节情绪，因此是 重度抑郁症（MDD）的病理生理学，许多尚不清楚，包括哪些基因途径 哪种特定细胞类型在该电路中起主要因果角色介导的变化以及哪种细胞类型 这些区域内和之间的联系在抑郁状态下尤其改变。总体 该应用的目的是生成单细胞转录组谱以研究分子变化， 包括特定于遗传血统和性别的那些，与情绪调节电路中的MDD相关。尽管 伯恩在非洲裔美国人中较大，遗传血统的影响仍然未知 到目前为止，MDD中的基因组研究仅限于欧洲血统的受试者。另外，以前 研究表明，与MDD相关的转录组变化是性别特异性的，基因网络是 性别之间的不同失调。申请人最近的单细胞脑研究表明细胞特异性 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项目 对ACC，BLA和HIPP的系统投资以询问单核的转录组 在MDD的较大且代表性的样本中的分辨率。具体目的是：1）确定，在 三个链接的大脑的800名受试者中与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有关； 3）确定细胞特异性表达 定量性状基因座（EQTL）与MDD GWAS中鉴定的全基因组显着SNP共定位 分析。从具有MDD受试者获得的大量人类（n = 800）将 与精神健康的控制相比。样本（约20％的非裔美国人和约30％的女性）将允许 用于研究遗传血统和性别的影响。基于液滴的单核RNA测序将是 应用于生成转录组轮廓。深度学习方法将用于识别和注释 细胞类型和基因网络相关的MDD。 MDD中最新的GWAS数据将被利用到精细地图 具有细胞和区域分辨率的遗传基因座。拟议的研究具有创新性，因为它是第一个 对人类ACC-BLA-HIPP电路的大规模研究，将代表最大的单细胞 人脑的转录资源。它将确定与性别或性别相关的基因和细胞网络 遗传血统，还将在神经型大脑上产生大量的转录组数据。这 研究很重要，因为它将大大提高我们对细胞和分子途径的理解 参与情绪调节和MDD。通过更好地理解抑郁的机制 疾病，我们可能更接近制定新颖的治疗策略并个性化干预措施。