Meta-analysis of Age-related Gene Expression Profiles Identifies Common Signatures of Aging

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2009 Feb 5

PMID 19189975

Citations 414

Authors

Joao Pedro de Magalhaes

Joao Curado

George M Church

Affiliations

Soon will be listed here.

Abstract

Motivation: Numerous microarray studies of aging have been conducted, yet given the noisy nature of gene expression changes with age, elucidating the transcriptional features of aging and how these relate to physiological, biochemical and pathological changes remains a critical problem.

Results: We performed a meta-analysis of age-related gene expression profiles using 27 datasets from mice, rats and humans. Our results reveal several common signatures of aging, including 56 genes consistently overexpressed with age, the most significant of which was APOD, and 17 genes underexpressed with age. We characterized the biological processes associated with these signatures and found that age-related gene expression changes most notably involve an overexpression of inflammation and immune response genes and of genes associated with the lysosome. An underexpression of collagen genes and of genes associated with energy metabolism, particularly mitochondrial genes, as well as alterations in the expression of genes related to apoptosis, cell cycle and cellular senescence biomarkers, were also observed. By employing a new method that emphasizes sensitivity, our work further reveals previously unknown transcriptional changes with age in many genes, processes and functions. We suggest these molecular signatures reflect a combination of degenerative processes but also transcriptional responses to the process of aging. Overall, our results help to understand how transcriptional changes relate to the process of aging and could serve as targets for future studies.

Availability: http://genomics.senescence.info/uarrays/signatures.html.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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