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人类大脑衰老的转录组景观、基因特征和调控特征。

Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain.

机构信息

Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain.

Cell Growth, Tissue Repair and Regeneration (CRRET), CNRS ERL 9215, Université Paris Est Créteil (UPEC), Créteil F-94000, France.

出版信息

Biochim Biophys Acta Gene Regul Mech. 2020 Jun;1863(6):194491. doi: 10.1016/j.bbagrm.2020.194491. Epub 2020 Feb 8.

Abstract

The molecular characteristics of aging that lead to increased disease susceptibility remain poorly understood. Here we present a transcriptomic profile of the human brain associated with age and aging, derived from a systematic integrative analysis of four independent cohorts of genome-wide expression data from 2202 brain samples (cortex, hippocampus and cerebellum) of individuals of different ages (from young infants, 5-10 years old, to elderly people, up to 100 years old) categorized in age stages by decades. The study provides a signature of 1148 genes detected in cortex, 874 genes in hippocampus and 657 genes in cerebellum, that present significant differential expression changes with age according to a robust gamma rank correlation profiling. The signatures show a significant large overlap of 258 genes between cortex and hippocampus, and 63 common genes between the three brain regions. Focusing on cortex, functional enrichment analysis and cell-type analysis provided biological insight about the aging signature. Response to stress and immune response were up-regulated functions. Synapse, neurotransmission and calcium signaling were down-regulated functions. Cell analysis, derived from single-cell data, disclosed an increase of neuronal activity in the young stages of life and a decline of such activity in the old stages. A regulatory analysis identified the transcription factors (TF) associated with the signature of 258 genes, common to cortex and hippocampus; revealing the role of MEF2(A,D), PDX1, FOSL(1,2) and RFX(5,1) as candidate regulators of the signature. Finally, a deep-learning neural network algorithm was used to build a biological age predictor based on the aging signature. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

摘要

衰老导致疾病易感性增加的分子特征仍知之甚少。在这里,我们展示了与年龄和衰老相关的人类大脑转录组特征,该特征源自对来自 2202 个不同年龄段(从婴儿期的 5-10 岁到 100 岁以上的老年人)的个体的全基因组表达数据的四个独立队列的系统综合分析。该研究提供了在皮质中检测到的 1148 个基因、海马体中 874 个基因和小脑体中 657 个基因的特征,这些基因根据稳健的伽马等级相关分析呈现出与年龄相关的显著差异表达变化。该特征显示皮质和海马体之间有 258 个基因的显著重叠,以及三个脑区之间有 63 个共同基因。重点关注皮质,功能富集分析和细胞类型分析为衰老特征提供了生物学见解。应激和免疫反应的上调功能。突触、神经递质和钙信号转导的下调功能。单细胞数据衍生的细胞分析揭示了生命早期神经元活动的增加和老年阶段神经元活动的下降。调控分析确定了与皮质和海马体共有的 258 个基因特征相关的转录因子 (TF);揭示了 MEF2(A,D)、PDX1、FOSL(1,2) 和 RFX(5,1) 作为特征签名候选调控因子的作用。最后,使用深度学习神经网络算法基于衰老特征构建了生物年龄预测器。本文是由 Federico Manuel Giorgi 博士和 Shaun Mahony 博士编辑的题为“转录谱和调控基因网络”的特刊的一部分。

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