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Discover the network mechanisms underlying the connections between aging and age-related diseases
Although our knowledge of aging has greatly expanded in the past decades, it remains elusive why and how aging contributes to the development of age-related diseases (ARDs). In particular, a global mechanistic understanding of the connections between aging and ARDs is yet to be established. We rely...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007654/ https://www.ncbi.nlm.nih.gov/pubmed/27582315 http://dx.doi.org/10.1038/srep32566 |
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author | Yang, Jialiang Huang, Tao Song, Won-min Petralia, Francesca Mobbs, Charles V. Zhang, Bin Zhao, Yong Schadt, Eric E. Zhu, Jun Tu, Zhidong |
author_facet | Yang, Jialiang Huang, Tao Song, Won-min Petralia, Francesca Mobbs, Charles V. Zhang, Bin Zhao, Yong Schadt, Eric E. Zhu, Jun Tu, Zhidong |
author_sort | Yang, Jialiang |
collection | PubMed |
description | Although our knowledge of aging has greatly expanded in the past decades, it remains elusive why and how aging contributes to the development of age-related diseases (ARDs). In particular, a global mechanistic understanding of the connections between aging and ARDs is yet to be established. We rely on a network modelling named “GeroNet” to study the connections between aging and more than a hundred diseases. By evaluating topological connections between aging genes and disease genes in over three thousand subnetworks corresponding to various biological processes, we show that aging has stronger connections with ARD genes compared to non-ARD genes in subnetworks corresponding to “response to decreased oxygen levels”, “insulin signalling pathway”, “cell cycle”, etc. Based on subnetwork connectivity, we can correctly “predict” if a disease is age-related and prioritize the biological processes that are involved in connecting to multiple ARDs. Using Alzheimer’s disease (AD) as an example, GeroNet identifies meaningful genes that may play key roles in connecting aging and ARDs. The top modules identified by GeroNet in AD significantly overlap with modules identified from a large scale AD brain gene expression experiment, supporting that GeroNet indeed reveals the underlying biological processes involved in the disease. |
format | Online Article Text |
id | pubmed-5007654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50076542016-09-08 Discover the network mechanisms underlying the connections between aging and age-related diseases Yang, Jialiang Huang, Tao Song, Won-min Petralia, Francesca Mobbs, Charles V. Zhang, Bin Zhao, Yong Schadt, Eric E. Zhu, Jun Tu, Zhidong Sci Rep Article Although our knowledge of aging has greatly expanded in the past decades, it remains elusive why and how aging contributes to the development of age-related diseases (ARDs). In particular, a global mechanistic understanding of the connections between aging and ARDs is yet to be established. We rely on a network modelling named “GeroNet” to study the connections between aging and more than a hundred diseases. By evaluating topological connections between aging genes and disease genes in over three thousand subnetworks corresponding to various biological processes, we show that aging has stronger connections with ARD genes compared to non-ARD genes in subnetworks corresponding to “response to decreased oxygen levels”, “insulin signalling pathway”, “cell cycle”, etc. Based on subnetwork connectivity, we can correctly “predict” if a disease is age-related and prioritize the biological processes that are involved in connecting to multiple ARDs. Using Alzheimer’s disease (AD) as an example, GeroNet identifies meaningful genes that may play key roles in connecting aging and ARDs. The top modules identified by GeroNet in AD significantly overlap with modules identified from a large scale AD brain gene expression experiment, supporting that GeroNet indeed reveals the underlying biological processes involved in the disease. Nature Publishing Group 2016-09-01 /pmc/articles/PMC5007654/ /pubmed/27582315 http://dx.doi.org/10.1038/srep32566 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Yang, Jialiang Huang, Tao Song, Won-min Petralia, Francesca Mobbs, Charles V. Zhang, Bin Zhao, Yong Schadt, Eric E. Zhu, Jun Tu, Zhidong Discover the network mechanisms underlying the connections between aging and age-related diseases |
title | Discover the network mechanisms underlying the connections between aging and age-related diseases |
title_full | Discover the network mechanisms underlying the connections between aging and age-related diseases |
title_fullStr | Discover the network mechanisms underlying the connections between aging and age-related diseases |
title_full_unstemmed | Discover the network mechanisms underlying the connections between aging and age-related diseases |
title_short | Discover the network mechanisms underlying the connections between aging and age-related diseases |
title_sort | discover the network mechanisms underlying the connections between aging and age-related diseases |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007654/ https://www.ncbi.nlm.nih.gov/pubmed/27582315 http://dx.doi.org/10.1038/srep32566 |
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