Large Scale Analysis of Gene Expression Changes in Aging

Fight Aging!

Do you want to live a longer life in good health? Simple practices can make some difference, such as exercise or calorie restriction. But over the long haul all that really matters is progress in medicine: building new classes of therapy to repair and reverse the known root causes of aging. The sooner these treatments arrive, the more lives will be saved. Find out how to help »

Over at Ouroboros, you'll find a look at what emerges from a very large comparative analysis of gene expression changes with aging. By finding the most important differences, and tracing back to the biological mechanisms associated with these genes, we should learn something about the validity of various theories of aging, and the importance of various potential strategies for slowing or reversing aging.

The core of the thing:

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.

If you've been reading Fight Aging! for a while, you'll have seen most of these processes and systems mentioned in connection to the damage of aging. The failing immune system, the role of chronic inflammation, the biochemical junk cluttering the lysosome, mitochondrial DNA damage, senescent cells, and so forth.

As they point out at Ouroboros:

While this approach will likely fail to identify those genes that are age-regulated only in a single tissue, the advantage is that those genes that do come out of this analysis are likely to be the really interesting ones - components of a common aging program that operates in multiple tissues.