Scientists find genetic signatures of accelerated ageing by analysing skin cells

Ever wonder why some of us appear to be considerably younger or older than our actual chronological age? Scientists from Salk Institute for Biological Studies have discovered genetic signatures that may help explain this.

The age-associated genetic patterns were found by analysing skin cells from people of various ages, according to a study from the San Diego, California-based research facility.

Researchers then applied the results to detect genetic signs of accelerated ageing in people with progeria, a fatal disease that causes children to age far more quickly than normal.

With further research, the machine-learning method could be broadened to detect when people are ageing faster than their chronological age, says Saket Navlakha, a senior author of the study. This could be used to treat people at risk for age-related conditions, or advise them to change lifestyle habits before diseases occur, he says.

Such clinical applications could be ready in as little as five years, Navlakha says. To help other researchers, the scientists have made the machine learning algorithms and underlying data public.

The study was published last week in the journal Genome Biology.

Navlakha says the project began as a conversation in the Salk Institute courtyard between him and Martin Hetzer, the other senior author, who suggested they discuss some of the new data they were collecting on ageing.

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“Martin has been working in the ageing field for a while, and I’m a computer scientist interested in developing machine learning algorithms to analyse biological data sets.”

The two brought in another Salk scientist, Jason Fleischer. They set up the project to examine the data without presuppositions about where signs of ageing might be found, Navlakha says.

The outcome was a “black box” prediction for age. It found the age-related genetic changes but without explaining why they were important.

They did this by examining all the RNA molecules in the cells. RNA is produced or transcribed from DNA; genes that are inactive do not make RNA. So by noting which RNA molecules are present or absent, the status of the corresponding genes can be deduced.

They performed this process on cells taken from 133 people, aged from one to 94. Researchers used connective tissue cells called fibroblasts, which are easy to collect.

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Putting the data into a machine learning algorithm extracted patterns of gene activity that lined up with increasing age. Using these biomarkers, the team was able to predict a person’s age with a median error of four years.

To verify that the molecular signature was real and not an artefact or coincidence, researchers then applied the machine learning process to the genomes of 10 children with progeria, aged two to eight. These were predicted to be about 10 years older than their chronological age.

Next, researchers plan to examine the age-related genetic activity of other cells. They also plan to look into the black box machine learning algorithm, to determine how the age-related changes line up with biological processes.