Literature DB >> 33309863

Tissue-specific Gene Expression Changes Are Associated with Aging in Mice.

Akash Srivastava1, Emanuel Barth2, Maria A Ermolaeva3, Madlen Guenther1, Christiane Frahm1, Manja Marz4, Otto W Witte5.   

Abstract

Aging is a complex process that can be characterized by functional and cognitive decline in an individual. Aging can be assessed based on the functional capacity of vital organs and their intricate interactions with one another. Thus, the nature of aging can be described by focusing on a specific organ and an individual itself. However, to fully understand the complexity of aging, one must investigate not only a single tissue or biological process but also its complex interplay and interdependencies with other biological processes. Here, using RNA-seq, we monitored changes in the transcriptome during aging in four tissues (including brain, blood, skin and liver) in mice at 9 months, 15 months, and 24 months, with a final evaluation at the very old age of 30 months. We identified several genes and processes that were differentially regulated during aging in both tissue-dependent and tissue-independent manners. Most importantly, we found that the electron transport chain (ETC) of mitochondria was similarly affected at the transcriptome level in the four tissues during the aging process. We also identified the liver as the tissue showing the largest variety of differentially expressed genes (DEGs) over time. Lcn2 (Lipocalin-2) was found to be similarly regulated among all tissues, and its effect on longevity and survival was validated using its orthologue in Caenorhabditis elegans. Our study demonstrated that the molecular processes of aging are relatively subtle in their progress, and the aging process of every tissue depends on the tissue's specialized function and environment. Hence, individual gene or process alone cannot be described as the key of aging in the whole organism.
Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Aging; Electron transport chain; Inflammaging; RNA-seq analysis; Tissue aging

Year:  2020        PMID: 33309863     DOI: 10.1016/j.gpb.2020.12.001

Source DB:  PubMed          Journal:  Genomics Proteomics Bioinformatics        ISSN: 1672-0229            Impact factor:   7.691


  7 in total

1.  Quantification of tissue-specific protein translation in whole C. elegans using O-propargyl-puromycin labeling and fluorescence microscopy.

Authors:  Hannah M Somers; Jeremy H Fuqua; Frédéric X A Bonnet; Jarod A Rollins
Journal:  Cell Rep Methods       Date:  2022-04-25

2.  Age-dependent expression changes of circadian system-related genes reveal a potentially conserved link to aging.

Authors:  Emanuel Barth; Akash Srivastava; Diane Wengerodt; Milan Stojiljkovic; Hubertus Axer; Otto W Witte; Alexandra Kretz; Manja Marz
Journal:  Aging (Albany NY)       Date:  2021-12-19       Impact factor: 5.682

3.  Upregulation of a nuclear factor-kappa B-interacting immune gene network in mice cochleae with age-related hearing loss.

Authors:  Kensuke Uraguchi; Yukihide Maeda; Junko Takahara; Ryotaro Omichi; Shohei Fujimoto; Shin Kariya; Kazunori Nishizaki; Mizuo Ando
Journal:  PLoS One       Date:  2021-10-22       Impact factor: 3.240

4.  Human Galectin-7 Gene LGALS7 Promoter Sequence Polymorphisms and Risk of Spontaneous Intracerebral Hemorrhage: A Prospective Study.

Authors:  Ming-Dong Wang; Jing Tian; John H Zhang; Shun-Ying Zhao; Ming-Jing Song; Zhan-Xiang Wang
Journal:  Front Mol Neurosci       Date:  2022-03-23       Impact factor: 5.639

5.  Single-cell transcriptomics reveals age-resistant maintenance of cell identities, stem cell compartments and differentiation trajectories in long-lived naked mole-rats skin.

Authors:  Aleksandra Savina; Thierry Jaffredo; Frederic Saldmann; Chris G Faulkes; Philippe Moguelet; Christine Leroy; Delphine Del Marmol; Patrice Codogno; Lucy Foucher; Antoine Zalc; Mélanie Viltard; Gérard Friedlander; Selim Aractingi; Romain H Fontaine
Journal:  Aging (Albany NY)       Date:  2022-05-04       Impact factor: 5.955

6.  Phosphoproteome profiling of mouse liver during normal aging.

Authors:  Jiang-Feng Liu; Yue Wu; Ye-Hong Yang; Song-Feng Wu; Shu Liu; Ping Xu; Jun-Tao Yang
Journal:  Proteome Sci       Date:  2022-08-05       Impact factor: 2.882

7.  The aging mouse lens transcriptome.

Authors:  Adam P Faranda; Mahbubul H Shihan; Yan Wang; Melinda K Duncan
Journal:  Exp Eye Res       Date:  2021-06-11       Impact factor: 3.770

  7 in total

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