Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain
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 expressi...
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description | 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.
•Transcriptomic analysis of 2202 human brain samples from young to elderly across age stages to identify aging signature•Human brain aging signatures discovered: 1148 altered genes in cortex, 874 in hippocampus (258 common), 657 in cerebellum•Functions affected by aging process: up response to stress, immune response; down synapse, neurotransmission, Ca signaling•Gene regulation and Transcription Factors of the 258 common genes of cortex and hippocampus found altered by aging•Biological-age predictor model using Deep-Learning Neural Networks based on the aging gene signature of human brain cortex |
doi_str_mv | 10.1016/j.bbagrm.2020.194491 |
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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.
•Transcriptomic analysis of 2202 human brain samples from young to elderly across age stages to identify aging signature•Human brain aging signatures discovered: 1148 altered genes in cortex, 874 in hippocampus (258 common), 657 in cerebellum•Functions affected by aging process: up response to stress, immune response; down synapse, neurotransmission, Ca signaling•Gene regulation and Transcription Factors of the 258 common genes of cortex and hippocampus found altered by aging•Biological-age predictor model using Deep-Learning Neural Networks based on the aging gene signature of human brain cortex</description><identifier>ISSN: 1874-9399</identifier><identifier>EISSN: 1876-4320</identifier><identifier>DOI: 10.1016/j.bbagrm.2020.194491</identifier><identifier>PMID: 32006715</identifier><language>eng</language><publisher>AMSTERDAM: Elsevier B.V</publisher><subject>Aging ; Biochemistry & Molecular Biology ; Biological age ; Biophysics ; Brain ; Cerebral cortex ; Life Sciences & Biomedicine ; Machine learning ; Science & Technology ; Transcriptomics</subject><ispartof>Biochimica et biophysica acta. Gene regulatory mechanisms, 2020-06, Vol.1863 (6), p.194491-194491, Article 194491</ispartof><rights>2020 The Authors</rights><rights>Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>25</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000528165200014</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c408t-5007d20af14770a1d4e0428a4dce6083b3c68027392cfcf215a293594eec2b273</citedby><cites>FETCH-LOGICAL-c408t-5007d20af14770a1d4e0428a4dce6083b3c68027392cfcf215a293594eec2b273</cites><orcidid>0000-0002-5054-8635 ; 0000-0002-0984-9946</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.bbagrm.2020.194491$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>315,782,786,3552,27931,27932,28255,46002</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32006715$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>González-Velasco, Oscar</creatorcontrib><creatorcontrib>Papy-García, Dulce</creatorcontrib><creatorcontrib>Le Douaron, Gael</creatorcontrib><creatorcontrib>Sánchez-Santos, José M.</creatorcontrib><creatorcontrib>De Las Rivas, Javier</creatorcontrib><title>Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain</title><title>Biochimica et biophysica acta. Gene regulatory mechanisms</title><addtitle>BBA-GENE REGUL MECH</addtitle><addtitle>Biochim Biophys Acta Gene Regul Mech</addtitle><description>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.
•Transcriptomic analysis of 2202 human brain samples from young to elderly across age stages to identify aging signature•Human brain aging signatures discovered: 1148 altered genes in cortex, 874 in hippocampus (258 common), 657 in cerebellum•Functions affected by aging process: up response to stress, immune response; down synapse, neurotransmission, Ca signaling•Gene regulation and Transcription Factors of the 258 common genes of cortex and hippocampus found altered by aging•Biological-age predictor model using Deep-Learning Neural Networks based on the aging gene signature of human brain cortex</description><subject>Aging</subject><subject>Biochemistry & Molecular Biology</subject><subject>Biological age</subject><subject>Biophysics</subject><subject>Brain</subject><subject>Cerebral cortex</subject><subject>Life Sciences & Biomedicine</subject><subject>Machine learning</subject><subject>Science & Technology</subject><subject>Transcriptomics</subject><issn>1874-9399</issn><issn>1876-4320</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><recordid>eNqNkE1r3DAQhkVJadK0_yAEHQOJtyNZ_tAlEJZ-QaCX9FYQsjx2tNjSRpIT8u-r1NscS08SM8-r0TyEnDHYMGD1p92m6_QY5g0HnktSCMnekBPWNnUhSg5Hf-6ikKWUx-R9jDuAmnGAd-Q4t6FuWHVCft0F7aIJdp_8bA2dtOuj0Xu8oiM6pNGOTqclYKS5QwOOy6STD890H_xgJ6R-oHq0bqTW0XSP9H6ZtaNd0NZ9IG8HPUX8eDhPyc8vn--234rbH1-_b29uCyOgTUUF0PQc9MBE04BmvUAQvNWiN1hDW3alqVvgTSm5GczAWaW5LCspEA3vcv2UXKzv5j89LBiTmm00OOVl0C9R8TKPaJumlhkVK2qCjzHgoPbBzjo8KwbqxavaqdWrevGqVq85dn6YsHQz9q-hvyIzcLkCT9j5IRqLzuArBgAVb1ldZRyYyHT7__TWJp2sd1u_uJSj12sUs9BHi0Ed4r0NaJLqvf33Kr8BgJKrTQ</recordid><startdate>202006</startdate><enddate>202006</enddate><creator>González-Velasco, Oscar</creator><creator>Papy-García, Dulce</creator><creator>Le Douaron, Gael</creator><creator>Sánchez-Santos, José M.</creator><creator>De Las Rivas, Javier</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5054-8635</orcidid><orcidid>https://orcid.org/0000-0002-0984-9946</orcidid></search><sort><creationdate>202006</creationdate><title>Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain</title><author>González-Velasco, Oscar ; Papy-García, Dulce ; Le Douaron, Gael ; Sánchez-Santos, José M. ; De Las Rivas, Javier</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-5007d20af14770a1d4e0428a4dce6083b3c68027392cfcf215a293594eec2b273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aging</topic><topic>Biochemistry & Molecular Biology</topic><topic>Biological age</topic><topic>Biophysics</topic><topic>Brain</topic><topic>Cerebral cortex</topic><topic>Life Sciences & Biomedicine</topic><topic>Machine learning</topic><topic>Science & Technology</topic><topic>Transcriptomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>González-Velasco, Oscar</creatorcontrib><creatorcontrib>Papy-García, Dulce</creatorcontrib><creatorcontrib>Le Douaron, Gael</creatorcontrib><creatorcontrib>Sánchez-Santos, José M.</creatorcontrib><creatorcontrib>De Las Rivas, Javier</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Biochimica et biophysica acta. Gene regulatory mechanisms</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>González-Velasco, Oscar</au><au>Papy-García, Dulce</au><au>Le Douaron, Gael</au><au>Sánchez-Santos, José M.</au><au>De Las Rivas, Javier</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain</atitle><jtitle>Biochimica et biophysica acta. Gene regulatory mechanisms</jtitle><stitle>BBA-GENE REGUL MECH</stitle><addtitle>Biochim Biophys Acta Gene Regul Mech</addtitle><date>2020-06</date><risdate>2020</risdate><volume>1863</volume><issue>6</issue><spage>194491</spage><epage>194491</epage><pages>194491-194491</pages><artnum>194491</artnum><issn>1874-9399</issn><eissn>1876-4320</eissn><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.
•Transcriptomic analysis of 2202 human brain samples from young to elderly across age stages to identify aging signature•Human brain aging signatures discovered: 1148 altered genes in cortex, 874 in hippocampus (258 common), 657 in cerebellum•Functions affected by aging process: up response to stress, immune response; down synapse, neurotransmission, Ca signaling•Gene regulation and Transcription Factors of the 258 common genes of cortex and hippocampus found altered by aging•Biological-age predictor model using Deep-Learning Neural Networks based on the aging gene signature of human brain cortex</abstract><cop>AMSTERDAM</cop><pub>Elsevier B.V</pub><pmid>32006715</pmid><doi>10.1016/j.bbagrm.2020.194491</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-5054-8635</orcidid><orcidid>https://orcid.org/0000-0002-0984-9946</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Aging Biochemistry & Molecular Biology Biological age Biophysics Brain Cerebral cortex Life Sciences & Biomedicine Machine learning Science & Technology Transcriptomics |
title | Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain |
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