LEVERAGING CONTEXT-SPECIFIC EPIGENOMIC REGULATORY NETWORKS (EPINETS) TO DISSECT THE GENETICS OF NEUROPSYCHIATRIC DISORDERS

IF 6.1 2区 医学 Q1 CLINICAL NEUROLOGY European Neuropsychopharmacology Pub Date : 2024-10-01 DOI:10.1016/j.euroneuro.2024.08.024
Ting-Ting Fu, Bhavana Kapalli, Jawahar Mahendran, Neha Rao, Lei Hou
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Abstract

Expression quantitative locus (eQTL) mapping provides deep insights into the function of disease-associated variants from Genome-wide association studies (GWAS). However, previous studies and our research reported misalignment between eQTLs and GWAS signals, likely due to bulk eQTLs mapped in non-pathology-relevant contexts and lack of cell-type resolution. Alternatively, unraveling the links between cis-regulatory elements (CREs) and genes in various cellular contexts offers an independent strategy to associate GWAS variants with their target genes beyond eQTL mapping. The state-of-the-art approaches, such as experimental assays (e.g., Promoter Capture Hi-C and macro-C) and computational models (e.g., ABC and EpiMap), provide linking resources based on different pieces of evidence, however, are each confined to limited brain cell types or cellular states.
Addressing this challenge, our study proposes a machine-learning approach to predict CRE-gene associations by combining protein-protein interactions and transcription factor (TF) binding predictions based on ATAC-seq, an assay measuring genomic accessibility. This computational approach facilitates the discovery of CRE-gene connections across different contexts (combinations of cell types and various conditions) whenever ATAC-seq data are available, enriching our understanding of the cis-regulatory networks between TF-CRE-gene.
We have amassed over 130 cell-sorted and single-cell ATAC-seq datasets encompassing a variety of brain cell types—excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte progenitor cells (OPCs), astrocytes, microglia, immune cells, and brain vascular cells—under a range of conditions including chemical perturbations, genetic modifications, infections, and disease status. Utilizing this extensive data collection and our integrative pipeline, we have constructed an atlas of TF-CRE-gene linking, namely cEpiNets. We finally employ the atlas to evaluate the enrichment of GWAS signals in CRE modules under various cellular contexts and to prioritize target genes and key drivers across a spectrum of neuropsychiatric disorders.
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利用特定情境表观基因组调控网络(epinets)剖析神经精神疾病的遗传学问题
表达定量基因座(eQTL)图谱能让人从全基因组关联研究(GWAS)中深入了解疾病相关变异的功能。然而,先前的研究和我们的研究都报告了 eQTL 与 GWAS 信号之间的不一致,这可能是由于大量 eQTL 映射在非病理学相关的环境中以及缺乏细胞类型分辨率。另外,揭示顺式调控元件(CRE)与各种细胞环境中基因之间的联系也是一种独立的策略,可将 GWAS 变异与其 eQTL 图谱之外的靶基因联系起来。最先进的方法,如实验检测(如 Promoter Capture Hi-C 和 macro-C)和计算模型(如 ABC 和 EpiMap),提供了基于不同证据的关联资源,但每种方法都局限于有限的脑细胞类型或细胞状态。为了应对这一挑战,我们的研究提出了一种机器学习方法,通过结合基于 ATAC-seq 的蛋白质-蛋白质相互作用和转录因子(TF)结合预测来预测 CRE 与基因的关联。只要有ATAC-seq数据,这种计算方法就能帮助发现不同情况下(细胞类型和各种条件的组合)的CRE-基因关联,从而丰富我们对TF-CRE-基因之间顺式调控网络的理解。我们已经积累了 130 多个细胞分选和单细胞 ATAC-seq 数据集,涵盖了各种脑细胞类型--兴奋性神经元、抑制性神经元、少突胶质细胞、少突胶质祖细胞 (OPC)、星形胶质细胞、小胶质细胞、免疫细胞和脑血管细胞--在一系列条件下,包括化学扰动、基因修饰、感染和疾病状态。利用这些广泛的数据收集和我们的整合管道,我们构建了一个 TF-CRE 基因连接图谱,即 cEpiNets。最后,我们利用该图集评估了各种细胞环境下 CRE 模块中 GWAS 信号的富集情况,并对一系列神经精神疾病的目标基因和关键驱动因素进行了优先排序。
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来源期刊
European Neuropsychopharmacology
European Neuropsychopharmacology 医学-精神病学
CiteScore
10.30
自引率
5.40%
发文量
730
审稿时长
41 days
期刊介绍: European Neuropsychopharmacology is the official publication of the European College of Neuropsychopharmacology (ECNP). In accordance with the mission of the College, the journal focuses on clinical and basic science contributions that advance our understanding of brain function and human behaviour and enable translation into improved treatments and enhanced public health impact in psychiatry. Recent years have been characterized by exciting advances in basic knowledge and available experimental techniques in neuroscience and genomics. However, clinical translation of these findings has not been as rapid. The journal aims to narrow this gap by promoting findings that are expected to have a major impact on both our understanding of the biological bases of mental disorders and the development and improvement of treatments, ideally paving the way for prevention and recovery.
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