美国加州大学洛杉矶分校Daniel H. Geschwind课题组揭示全脑和多区域的疾病易感性基础。相关论文于2021年7月22日在线发表在《自然—神经科学》杂志上。

研究人员利用代表12个脑区的864个样本的RNA测序，有力地确定了12个全脑、50个跨区域和114个区域的共表达模块。近40%的基因属于全脑模块，而25%的基因由反映区域生物学的区域特定模块组成，如下丘脑的催产素信号，或伏隔核的成瘾途径。精神分裂症和自闭症的遗传风险在全脑和多区域模块中富集，这表明其影响广泛；这些模块牵涉到神经元增殖和活动依赖过程，包括在疾病的病理生理学中的内吞作用和剪接。

研究人员发现，细胞特异性长非编码RNA和基因异构体对区域突触多样性有很大的贡献，而受限制的、不耐突变的基因主要在神经元中富集。研究人员利用这些数据，用一个全基因启发的网络框架来描述共表达和基因调控网络如何反映神经精神疾病风险，并支持多基因模型。

据悉，基因网络已经产生了许多神经生物学方面的见解，但还缺乏跨脑区的综合观点。

附：英文原文

Title: Coexpression network architecture reveals the brain-wide and multiregional basis of disease susceptibility

Author: Christopher L. Hartl, Gokul Ramaswami, William G. Pembroke, Sandrine Muller, Greta Pintacuda, Ashis Saha, Princy Parsana, Alexis Battle, Kasper Lage, Daniel H. Geschwind

Issue&Volume: 2021-07-22

Abstract: Gene networks have yielded numerous neurobiological insights, yet an integrated view across brain regions is lacking. We leverage RNA sequencing in 864 samples representing 12 brain regions to robustly identify 12 brain-wide, 50 cross-regional and 114 region-specific coexpression modules. Nearly 40% of genes fall into brain-wide modules, while 25% comprise region-specific modules reflecting regional biology, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens. Schizophrenia and autism genetic risk are enriched in brain-wide and multiregional modules, indicative of broad impact; these modules implicate neuronal proliferation and activity-dependent processes, including endocytosis and splicing, in disease pathophysiology. We find that cell-type-specific long noncoding RNA and gene isoforms contribute substantially to regional synaptic diversity and that constrained, mutation-intolerant genes are primarily enriched in neurons. We leverage these data using an omnigenic-inspired network framework to characterize how coexpression and gene regulatory networks reflect neuropsychiatric disease risk, supporting polygenic models. The authors construct brain-wide coexpression networks to characterize regional versus global features, determine if disease susceptibility maps onto regional or brain-wide processes and assess how these networks capture genetic models of disease risk.

DOI: 10.1038/s41593-021-00887-5

Source: https://www.nature.com/articles/s41593-021-00887-5