Volume 1, 2018
|Number of page(s)||15|
|Section||Life Sciences - Medicine|
|Published online||22 August 2018|
Visual gene network analysis of aging-specific gene co-expression in human indicates overlaps with immuno-pathological regulations
Post-Graduate Department of Biosciences and Biotechnology, School of Biotechnology, Fakir Mohan University,
2 Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem 27157, NC, USA
3 Central University of Jharkhand, Brambe, Ratu-Lohardaga Road, Ranchi 835205, Jharkhand, India
4 Khallikote University, Berhampur 760001, Odisha, India
* e-mail: email@example.com
Accepted: 4 July 2018
Introduction: Aging is a complex biological process that brings about a gradual decline of physiological and metabolic machineries as a result of maturity. Also, aging is irreversible and leads ultimately to death in biological organisms.
Methods: We intend to characterize aging at the gene expression level using publicly available human gene expression arrays obtained from gene expression omnibus (GEO) and ArrayExpress. Candidate genes were identified by rigorous screening using filtered data sets, i.e., GSE11882, GSE47881, and GSE32719. Using Aroma and Limma packages, we selected the top 200 genes showing up and down regulation (p < 0.05 and fold change >2.5) out of which 185 were chosen for further comparative analysis.
Results: This investigation enabled identification of candidate genes involved in aging that are associated with several signaling cascades demonstrating strong correlation with ATP binding and protease functions.
Conclusion: A majority of these gene encoded proteins function extracellularly, and also provide insights into the immunopathological basis of aging.
Key words: Aging / Human / Microarray analysis / Gene expression / Gene networks / Skin / Brain / Mitochondria
© B.P. Parida et al., Published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.