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联合分析转录组和功能连接组鉴定人类大脑皮层的年龄特异性基因特征。

Identifying age-specific gene signatures of the human cerebral cortex with joint analysis of transcriptomes and functional connectomes.

机构信息

Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China.

Department of Electronic and Information Engineering, Tongji University, China.

出版信息

Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa388.

Abstract

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method transcriptome-connectome correlation analysis (TCA), which integrates the brain functional magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value <2.00e-3) and myelination (P-value <1.00e-3), two key brain structural features that vary in accordance with brain development. In addition to the molecular underpinning of age-related brain functions, the ASC gene signatures allow delineation of the molecular mechanisms of neuropsychiatric disorders, such as the regulation between ARNT2 and its target gene ETF1 involved in Schizophrenia. We further validate the ASC gene signatures with published gene sets associated with the adult cortex, and confirm the robustness of TCA on other brain image datasets. Availability: All scripts are written in R. Scripts for the TCA method and related statistics result can be freely accessed at https://github.com/Soulnature/TCA. Additional data related to this paper may be requested from the authors.

摘要

人类大脑皮层在整个生命周期中经历深刻的结构和功能动态变化,但其潜在的分子机制尚不清楚。在这里,我们采用一种新的方法——转录组-连接组相关分析(TCA),将大脑功能磁共振图像和特定区域的转录组相结合,鉴定出青少年、成年早期和成年晚期特有的大脑皮层(ASC)基因特征。ASC 基因特征与皮质厚度(P 值<2.00e-3)和髓鞘化(P 值<1.00e-3)显著相关,这两个关键的大脑结构特征随大脑发育而变化。除了与年龄相关的大脑功能的分子基础外,ASC 基因特征还可以描绘神经精神疾病的分子机制,例如涉及精神分裂症的 ARNT2 与其靶基因 ETF1 之间的调节。我们使用与成人大脑相关的已发表基因集进一步验证了 ASC 基因特征,并在其他大脑图像数据集上验证了 TCA 的稳健性。可获取性:所有脚本均使用 R 编写。TCA 方法及相关统计结果的脚本可在 https://github.com/Soulnature/TCA 上免费获取。如需获取本文相关的其他数据,请与作者联系。

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