Author
Listed:
- Jacob M Zahn
- Suresh Poosala
- Art B Owen
- Donald K Ingram
- Ana Lustig
- Arnell Carter
- Ashani T Weeraratna
- Dennis D Taub
- Myriam Gorospe
- Krystyna Mazan-Mamczarz
- Edward G Lakatta
- Kenneth R Boheler
- Xiangru Xu
- Mark P Mattson
- Geppino Falco
- Minoru S H Ko
- David Schlessinger
- Jeffrey Firman
- Sarah K Kummerfeld
- William H Wood III
- Alan B Zonderman
- Stuart K Kim
- Kevin G Becker
Abstract
We present the AGEMAP (Atlas of Gene Expression in Mouse Aging Project) gene expression database, which is a resource that catalogs changes in gene expression as a function of age in mice. The AGEMAP database includes expression changes for 8,932 genes in 16 tissues as a function of age. We found great heterogeneity in the amount of transcriptional changes with age in different tissues. Some tissues displayed large transcriptional differences in old mice, suggesting that these tissues may contribute strongly to organismal decline. Other tissues showed few or no changes in expression with age, indicating strong levels of homeostasis throughout life. Based on the pattern of age-related transcriptional changes, we found that tissues could be classified into one of three aging processes: (1) a pattern common to neural tissues, (2) a pattern for vascular tissues, and (3) a pattern for steroid-responsive tissues. We observed that different tissues age in a coordinated fashion in individual mice, such that certain mice exhibit rapid aging, whereas others exhibit slow aging for multiple tissues. Finally, we compared the transcriptional profiles for aging in mice to those from humans, flies, and worms. We found that genes involved in the electron transport chain show common age regulation in all four species, indicating that these genes may be exceptionally good markers of aging. However, we saw no overall correlation of age regulation between mice and humans, suggesting that aging processes in mice and humans may be fundamentally different.: This work studies the aging process in mice using DNA microarrays to identify genes that change expression during aging. The entire set of age-regulated genes constitutes a transcriptional profile that can be used to measure ages of different individuals. Furthermore, the aging expression profile highlights genetic and metabolic pathways that change with age, providing key insights about possible molecular changes that may contribute to cell senescence and physiological decline. This aging study is massive in scope, involving gene expression measurements from 16 different tissues at four different ages. Expression data for genes of interest can be queried over the web by researchers interested in aging. Different tissues were found to have strikingly different levels of age-related change, and could be divided into three groups based on their patterns of aging: a neural group, a vascular group, and a steroid-responsive group. The electron transport chain pathway stands out because it is the only genetic pathway that shows a similar pattern of age-related change in mice, humans, worms, and flies. However, there is little overall similarity between changes in gene expression during aging of humans and mice, consistent with evolutionary theories suggesting that aging lies outside the force of natural selection.
Suggested Citation
Jacob M Zahn & Suresh Poosala & Art B Owen & Donald K Ingram & Ana Lustig & Arnell Carter & Ashani T Weeraratna & Dennis D Taub & Myriam Gorospe & Krystyna Mazan-Mamczarz & Edward G Lakatta & Kenneth , 2007.
"AGEMAP: A Gene Expression Database for Aging in Mice,"
PLOS Genetics, Public Library of Science, vol. 3(11), pages 1-12, November.
Handle:
RePEc:plo:pgen00:0030201
DOI: 10.1371/journal.pgen.0030201
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Citations
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Cited by:
- Veronika Ročková & Edward I. George, 2016.
"Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1608-1622, October.
- Niu, Lu & Liu, Xiumin & Zhao, Junlong, 2020.
"Robust estimator of the correlation matrix with sparse Kronecker structure for a high-dimensional matrix-variate,"
Journal of Multivariate Analysis, Elsevier, vol. 177(C).
- Vu, Duy & Aitkin, Murray, 2015.
"Variational algorithms for biclustering models,"
Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 12-24.
- David H. Rehkopf & Luis Rosero-Bixby & William H. Dow, 2016.
"A cross-national comparison of 12 biomarkers finds no universal biomarkers of aging among individuals aged 60 and older,"
Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 14(1), pages 255-277.
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