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Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data

Aging is an inevitable progressive decline in every physiological function and serves as a primary risk factor for cognitive decline and Alzheimer’s disease. Thus, age-dependent impairments in cognitive function must be understood in association with general aging processes with an integrative appro...

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Autores principales: Bae, Sang-Hun, Kim, Han Wool, Shin, SeoJeong, Kim, Joopyung, Jeong, Yun-Hwa, Moon, Jisook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938059/
https://www.ncbi.nlm.nih.gov/pubmed/29651153
http://dx.doi.org/10.1038/s12276-018-0057-6
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author Bae, Sang-Hun
Kim, Han Wool
Shin, SeoJeong
Kim, Joopyung
Jeong, Yun-Hwa
Moon, Jisook
author_facet Bae, Sang-Hun
Kim, Han Wool
Shin, SeoJeong
Kim, Joopyung
Jeong, Yun-Hwa
Moon, Jisook
author_sort Bae, Sang-Hun
collection PubMed
description Aging is an inevitable progressive decline in every physiological function and serves as a primary risk factor for cognitive decline and Alzheimer’s disease. Thus, age-dependent impairments in cognitive function must be understood in association with general aging processes with an integrative approach in a systemic manner. An integrative aging gene network was constructed based on mutual molecular interactions using literature-curated interactome data and separated into functionally distinct modules. To investigate key surrogate biomarkers of the aging brain in the context of the general aging process, co-expression networks were built on post-mortem and Alzheimer’s brain transcriptome data. In both the normal aging brain and the brain affected by Alzheimer’s disease, the immune-related co-expression module was positively correlated with advancing age, whereas the synaptic transmission-related co-expression module was decreased with age. Importantly, the network topology-based analysis indicated that complement system genes were prioritized as a surrogate biomarker in evaluating the process of brain aging. Our public data-centered analysis coupled with experimental validation revealed that the complement system is likely to be a master regulator in initiating and regulating the immune system in the aging brain and could serve as reliable and surrogate biomarkers for the diagnosis of cognitive dysfunction.
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spelling pubmed-59380592018-05-15 Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data Bae, Sang-Hun Kim, Han Wool Shin, SeoJeong Kim, Joopyung Jeong, Yun-Hwa Moon, Jisook Exp Mol Med Article Aging is an inevitable progressive decline in every physiological function and serves as a primary risk factor for cognitive decline and Alzheimer’s disease. Thus, age-dependent impairments in cognitive function must be understood in association with general aging processes with an integrative approach in a systemic manner. An integrative aging gene network was constructed based on mutual molecular interactions using literature-curated interactome data and separated into functionally distinct modules. To investigate key surrogate biomarkers of the aging brain in the context of the general aging process, co-expression networks were built on post-mortem and Alzheimer’s brain transcriptome data. In both the normal aging brain and the brain affected by Alzheimer’s disease, the immune-related co-expression module was positively correlated with advancing age, whereas the synaptic transmission-related co-expression module was decreased with age. Importantly, the network topology-based analysis indicated that complement system genes were prioritized as a surrogate biomarker in evaluating the process of brain aging. Our public data-centered analysis coupled with experimental validation revealed that the complement system is likely to be a master regulator in initiating and regulating the immune system in the aging brain and could serve as reliable and surrogate biomarkers for the diagnosis of cognitive dysfunction. Nature Publishing Group UK 2018-04-13 /pmc/articles/PMC5938059/ /pubmed/29651153 http://dx.doi.org/10.1038/s12276-018-0057-6 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, and provide a link to the Creative Commons license. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, http://creativecommons.org/licenses/by-nc-nd/4.0/.
spellingShingle Article
Bae, Sang-Hun
Kim, Han Wool
Shin, SeoJeong
Kim, Joopyung
Jeong, Yun-Hwa
Moon, Jisook
Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data
title Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data
title_full Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data
title_fullStr Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data
title_full_unstemmed Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data
title_short Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data
title_sort decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938059/
https://www.ncbi.nlm.nih.gov/pubmed/29651153
http://dx.doi.org/10.1038/s12276-018-0057-6
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