Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA.
Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
Nat Med. 2023 Mar;29(3):667-678. doi: 10.1038/s41591-023-02238-2. Epub 2023 Mar 6.
Cerebral arachnoid cysts (ACs) are one of the most common and poorly understood types of developmental brain lesion. To begin to elucidate AC pathogenesis, we performed an integrated analysis of 617 patient-parent (trio) exomes, 152,898 human brain and mouse meningeal single-cell RNA sequencing transcriptomes and natural language processing data of patient medical records. We found that damaging de novo variants (DNVs) were highly enriched in patients with ACs compared with healthy individuals (P = 1.57 × 10). Seven genes harbored an exome-wide significant DNV burden. AC-associated genes were enriched for chromatin modifiers and converged in midgestational transcription networks essential for neural and meningeal development. Unsupervised clustering of patient phenotypes identified four AC subtypes and clinical severity correlated with the presence of a damaging DNV. These data provide insights into the coordinated regulation of brain and meningeal development and implicate epigenomic dysregulation due to DNVs in AC pathogenesis. Our results provide a preliminary indication that, in the appropriate clinical context, ACs may be considered radiographic harbingers of neurodevelopmental pathology warranting genetic testing and neurobehavioral follow-up. These data highlight the utility of a systems-level, multiomics approach to elucidate sporadic structural brain disease.
脑蛛网膜囊肿 (AC) 是最常见和了解最少的发育性脑病变之一。为了开始阐明 AC 的发病机制,我们对 617 位患者-父母(三核苷酸)外显子组、152898 个人类大脑和小鼠脑膜单细胞 RNA 测序转录组以及患者病历的自然语言处理数据进行了综合分析。我们发现与健康个体相比,AC 患者中存在大量新生破坏性变异 (DNV)(P=1.57×10)。有七个基因携带有全外显子显著的 DNV 负担。与 AC 相关的基因富含染色质修饰因子,并集中在中孕期转录网络中,这些网络对于神经和脑膜发育至关重要。对患者表型的无监督聚类确定了四个 AC 亚型,临床严重程度与存在破坏性 DNV 相关。这些数据提供了对脑和脑膜发育的协调调控的深入了解,并表明由于 DNV 导致的表观基因组失调在 AC 发病机制中起作用。我们的结果初步表明,在适当的临床情况下,AC 可能被视为神经发育病理学的影像学标志物,需要进行基因检测和神经行为随访。这些数据突出了系统水平、多组学方法在阐明散发性结构性脑疾病方面的应用价值。