STANFORD, Calif. – For the first time, researchers at Stanford University School of Medicine have examined how kidneys change at a molecular level with the passage of time. What they found suggests that all human cells age in a similar way, supporting one theory about how cells grow old.
“Until now we really didn’t know what happens when people get old,” said Stuart Kim, PhD, professor of developmental biology and genetics, who led the study that is to be published in the November 30 issue of Public Library of Science Biology. “Our work suggests that there’s a common way for all tissues to get old.”
These findings are contrary to one model for how cells age. This theory holds that because organs have different groups of molecules, they follow different pathways as they age. If this were the case then the aging kidney would look quite different on the molecular level from an aging liver.
Instead the study findings support another model, which suggests that all cells in an animal peter out in the same way. If this were true then researchers would find the same molecular differences between old and young cells from all organs.
In the study, Kim and his group compared which genes are active in kidney cells from 74 people ranging in age from 27 to 92 years. They found 742 genes that become more active as the kidney ages and 243 genes that become less active.
They then did the same experiment using different types of kidney tissue, with one sample from the outer kidney, called the cortex, and the other from the inner kidney, called the medulla. Although these two tissues are both from the kidney, they are as different in function as cells from entirely different organs. The researchers found exactly the same genes varied in old and young samples from these two tissues.
The next obvious experiment would be to repeat this study with tissues from other organs and see if the same genes have changing levels of activity as tissue ages. Do some organs age at more rapid rates? Does this happen for everyone? One might expect some variability between humans due to genetic variants that accelerate aging in particular organs and also due to dietary habits and other habits that impose larger harm on certain organs (e.g. smoking on lungs or drinking on livers or fried meats on intestines).
Note that gene microarrays have gotten so powerful that these researchers were able to check the expression levels of all the known genes in a human cell. Chronological age is not always the same as age as measured by molecular genetic expression profile.
Kim and colleagues then isolated RNA transcripts from the samples to determine the activity of every gene, broken down by age and kidney section, through microarray analysis. Looking for differences in gene expression across the genome, they identified genes that showed a statistically significant change in expression as a function of age. Of 33,000 known human genes on the microarray, 985 showed age-related changes, most showing increased activity. These changes are truly age-regulated, the authors conclude, since none of the medical factors impacted the observed changes in gene expression.
Although cortex and medulla have different cell types and perform different functions, their genetic aging profile was very similar, suggesting a common aging mechanism operates in both structures. In fact, these mechanisms may function broadly, as most of the age-regulated kidney genes were also active in a wide range of human tissues. Other organisms appear to lack these changes, however, prompting the authors to argue that understanding aging in humans will require human subjects.
Most importantly, the genetic profile of the tissue samples correlated with the physiological and morphological decline of an aging kidney. An 81-year-old patient with an unusually healthy kidney had a molecular profile typical of someone much younger, while a 78-year-old with a damaged kidney had the profile of a much older person. Using the power of functional genomics, this study has identified a set of genes that can serve as molecular markers for various stages of a deteriorating kidney and predict the relative health of a patient compared to their age group. These gene sets can also serve as probes to shed light on the molecular pathways at work in the aging kidney, and possibly on the process of aging itself.
I'd love to see a longitudinal study where tissues are taken from a number of elderly people and assayed for gene expression to see if onset of diseases and mortality can be predicted from how far along the cells in a person seem to have aged according to gene expression levels.
One public policy implication of a developed ability to more accurately predict life expectancy is that government-funded old age retirement funds could (not saying they would) offer different ages of eligibility for retirement based on how old a person is measured to be at the molecular level. A highly aged 60 year old could be offered early retirement based on a short life expectancy and a diminished ability to work. At the same time a highly health and functional 70 year old could be told they have to keep working or live off their own savings because all molecular indications are that they have years of healthy vibrant life still left in their bodies. Given that governments face massive unfunded liabilities for old age entitlements programs the ability to distinguish between aged elderly and relatively more youthful elderly could provide governments with a way to lighten their fiscal burdens.
You can read the full research paper free of charge.
|Share |||Randall Parker, 2004 December 14 03:22 PM Aging Studies|