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

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.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.The publication costs for this article were funded by the research grant PI18/00591, from the Instituto de Salud Carlos III (ISCiii) co-funded by the Fondo Europeo de Desarrollo Regional (FEDER). This project has also received direct funding from the European Commission EU Horizon 2020 FET OPEN RIA Programme (call H2020-FETOPEN-1-2016-2017) under grant number 737390 (Project ArrestAD). We acknowledge the funding provided to JDLR research group by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) with grants of the ISCiii co-funded by FEDER (references AC14/00024 and PI18/00591). We also acknowledge the funding received from the European Commission EU Horizon 2020 FET OPEN RIA Programme (call H2020-FETOPEN-1-2016-2017) with grant number 737390: Project ArrestAD. A PhD research grant to OGV (from the Program: Ayudas a la Contratación de Personal Investigador) provided by the Junta de Castilla y Leon (JCyL) with the support of the Fondo Social Europeo (FSE) is also acknowledged.Peer reviewe