Computational analysis of single-nucleus sequencing data for studying the cell type-specific basis of opioid use disorders

单核测序数据的计算分析，用于研究阿片类药物使用障碍的细胞类型特异性基础

• 批准号: 10663815
• 负责人: Jessica Lu Zhou
• 金额: $ 2.64万
• 依托单位: SALK INSTITUTE FOR BIOLOGICAL STUDIES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10663815
• 关键词: ATAC-seq; Abstinence; Accounting; Acute; Addictive Behavior; Affect; Area; Atlases; Behavior; Binding Sites; Blood Cells; Brain; Brain region; COVID-19 pandemic; Cell Nucleus; Cells; Chromatin; Chronic; Computer Analysis; Data; Data Set; Dependence; Disease; Drug Exposure; Drug usage; Enhancers; Etiology; Exposure to; Fentanyl; Gene Expression; Gene Targeting; Genes; Genetic; Genetic Risk; Genetic Transcription; Genomic Segment; Goals; Grant; Heritability; Heroin; Individual; Intake; Knowledge; Lead; Learning; Linkage Disequilibrium; Long-Term Effects; Longitudinal Studies; Measures; Modeling; Molecular; National Institute of Drug Abuse; Neurobiology; Nucleus Accumbens; Opiate Addiction; Opioid; Oxycodone; Pattern; Peptide Initiation Factors; Persons; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacotherapy; Predisposition; Public Health; Publishing; Rattus; Regulation; Regulatory Element; Regulatory Pathway; Relapse; Research; Resistance; Resolution; Risk; Risk Factors; Role; Sampling; Self Administration; Severities; Signal Transduction; Statistical Models; Stress; System; Training; Treatment Efficacy; United States; Untranslated RNA; Variant; Work; addiction; advanced disease; behavior measurement; brain cell; cell type; clinically significant; convolutional neural network; cost; deep learning; differential expression; disorder prevention; drug of abuse; drug seeking behavior; economic impact; genetic variant; genome wide association study; improved; insight; neural circuit; neuroadaptation; new therapeutic target; novel; opioid epidemic; opioid use; opioid use disorder; overdose death; prescription opioid misuse; promoter; risk variant; single cell sequencing; single nucleus RNA-sequencing; substance use; synthetic opioid; therapeutic target; transcription factor

PROJECT SUMMARY The opioid epidemic is a public health crisis that affects almost two million people in the United States and costs billions of dollars annually. The chronic use of opioids can lead to tolerance, dependence, and in the most severe cases, addiction. Addiction is characterized by compulsive drug-seeking behavior despite negative consequences, as well as a propensity for relapse even after extended periods of abstinence. This suggests that compulsive drug use induces persistent changes in key brain regions which persist following cessation of drug use that give rise to addiction-related behaviors. Increasing evidence indicates that persistent changes in gene expression might be a critical mechanism by which drugs of abuse lead to changes in neural circuits associated to addictive behaviors. Exposure to addictive drugs causes widespread transcriptional changes across various brain cell types. However, the genes affected by drugs of abuse in distinct brain cell types and the regulatory pathways that drive these changes remain mostly unclear. Additionally, most genetic variants associated with addiction are found in noncoding genomic regions and frequently located in cell type-specific enhancers and promoters. These observations indicate that persistent changes in gene expression associated with opioid addiction and the transcriptional regulatory pathways that drive these changes are likely cell type-specific. However, existing knowledge in this area has largely been based on bulk sequencing heterogeneous samples from key brain regions, which cannot capture cell type-specific signals. Single-cell sequencing data is uniquely capable of detecting molecular differences across different cell types, but single-cell studies of opioid addiction have been limited to blood cells or acute drug treatment. This has impeded a higher resolution understanding of the mechanisms involved in long-term drug-induced neurobiological changes and susceptibility to addiction. This proposal will computationally analyze novel single-nucleus RNA-seq (snRNA-seq) and single- nucleus ATAC-seq (snATAC-seq) data generated from a validated rat model of extended access oxycodone self-administration to study the molecular basis of opioid use disorders (OUDs) at single cell resolution. Cell type-specific comparisons of gene expression and chromatin accessibility between rats selected as vulnerable versus resistant to behavioral measures of addiction will be conducted to reveal the long-term effects of compulsive opioid use in specific brain cell types and identify putative regulatory relationships. Statistical models and deep learning will also be used to develop a framework for identifying the functional effects of noncoding genetic variants and improve understanding of genetic risk in OUDs. This work is clinically significant and will contribute to a better understanding of OUDs and identify regulatory mechanisms as therapeutic targets to improve OUD treatment approaches.

项目概要 阿片类药物流行是一场公共卫生危机，影响了美国近 200 万人 并每年花费数十亿美元。长期使用阿片类药物会导致耐受性、依赖性，并导致 最严重的情况是成瘾。成瘾的特点是强迫性寻求药物的行为，尽管 负面后果，以及即使在长时间禁欲后也有复发的倾向。这 表明强迫性药物使用会引起关键大脑区域的持续变化，这种变化在服药后持续存在 停止使用导致成瘾相关行为的药物。 越来越多的证据表明基因表达的持续变化可能是一个关键 滥用药物导致与成瘾行为相关的神经回路发生变化的机制。 接触成瘾药物会导致各种脑细胞类型发生广泛的转录变化。 然而，不同脑细胞类型中受滥用药物影响的基因以及调节途径 推动这些变化的因素大多仍不清楚。此外，大多数与成瘾相关的基因变异是 存在于非编码基因组区域，并且经常位于细胞类型特异性增强子和启动子中。 这些观察结果表明，与阿片类药物成瘾相关的基因表达的持续变化和 驱动这些变化的转录调控途径可能是细胞类型特异性的。然而，现有的 该领域的知识很大程度上基于对关键大脑的异质样本进行批量测序 区域，无法捕获细胞类型特异性信号。单细胞测序数据具有独特的能力 检测不同细胞类型之间的分子差异，但阿片类药物成瘾的单细胞研究已经 仅限于血细胞或急性药物治疗。这阻碍了对这一现象的更高分辨率的理解 涉及长期药物引起的神经生物学变化和成瘾易感性的机制。 该提案将通过计算分析新型单核 RNA-seq (snRNA-seq) 和单核 RNA-seq 核 ATAC-seq (snATAC-seq) 数据由经过验证的扩展访问羟考酮大鼠模型生成 自我给药，以单细胞分辨率研究阿片类药物使用障碍 (OUD) 的分子基础。细胞 选择易感大鼠之间基因表达和染色质可及性的类型特异性比较 将进行与抵制成瘾行为措施的比较，以揭示成瘾的长期影响 特定脑细胞类型中强迫性阿片类药物的使用并确定假定的调节关系。统计 模型和深度学习也将用于开发一个框架来识别功能效果 非编码遗传变异并提高对 OUD 遗传风险的理解。这项工作是临床 意义重大，将有助于更好地理解 OUD 并确定监管机制： 改善 OUD 治疗方法的治疗目标。