Epigenetic ‘Clocks’ Predict Animals’ True Biological Age – Quanta Magazine

His clocks are based on analyses of the chemical tags called methyl groups that hang on DNA like charms on a bracelet and help control gene activity. They are products of epigenetics (literally, “above genetics”), the field that studies heritable information not written in the genetic code. A dozen years ago, Horvath and his colleagues began applying their know-how to building the clocks, first to assess the age of DNA from saliva, and later to determine the age of blood, liver and other individual tissues.

Many biologists were skeptical at first because the clocks were rooted in statistics rather than an understanding of biomolecular mechanisms. Yet the accuracy of the clocks stood up to tests and sent ripples through the biomedical community. Scientists began using Horvath clocks in their research to measure the aging of cells because the clocks were better arbiters of the state of the body and the risk of disease than chronological age. “Epigenetic clocks are closer to the actual process of aging than any other biomarkers,” said Vadim Gladyshev, a biochemist at Brigham and Women’s Hospital and Harvard Medical School who studies cancer and aging. Now the clocks are leading some scientists to rethink their ideas about what aging is, as well as its connection to diseases.

“I now have collaborators that work a lot in breast cancer and [are] starting to think about, ‘If you have advanced biological aging, is that also informative for breast cancer?’” said Sara Hägg, a molecular epidemiologist at the Karolinska Institute in Stockholm, Sweden. If the clocks can usefully illuminate how to stop the aging process from triggering age-related disorders, she added, “we could prevent not just one disease but many.”

Seeing a Signal

Time and again in past decades, biological researchers thought a clock for aging was within reach. For example, they learned in the early 1960s that cells growing in culture aren’t immortal but instead die after only 40-60 rounds of replication, which suggested that cells harbor a kind of aging clock. In 1982, researchers thought they might have found the clock’s mechanism when they isolated telomeres, DNA-protein complexes at the ends of chromosomes that shorten each time a cell divides; when telomeres become critically short, cells die.

But telomeres did not pan out as an aging clock. The correlation of telomere length with age and mortality is weak in humans and nonexistent in some other species. “Telomere [length] does not actually track age. It just tracks cell proliferation,” said Ken Raj, a principal investigator at Altos Labs.

As an alternative to telomere length, in 2009 Horvath began working on a clock based on the RNA transcripts of a cell’s active genes, the templates for the proteins that define a cell and allow it to function. For the next two years he tried to make that approach work, to no avail: The transcription data was just too noisy.

But in 2010, Horvath answered a request for help from a colleague at UCLA. To study possible connections between sexual orientation and epigenetics, the researcher was collecting saliva from identical twins who differed in sexual orientation, with the hypothesis that the DNA in their saliva cells might reveal some consistent differences in methylation patterns. Horvath’s twin brother is gay; Horvath is heterosexual. They supplied their spit.

The study’s analysis looked at sites in the DNA where cytosine bases are located and checked which of them were methylated. (Cytosines are the only bases to which methyl groups attach.) A recently introduced lab-on-a chip technology made it easy to test tens of thousands of cytosine sites in each cell’s DNA. When the colleague needed a statistician to analyze the data, Horvath volunteered his services.

He did not find what they were looking for. “There was no signal whatsoever for homosexuality,” Horvath said. “But because the data were on my computer, I said, let me look at aging effects,” since the ages of the twins in the study spanned decades.

Until then, Horvath had steered clear of epigenetic data in his own research. The relationship of methylation patterns to gene expression is messy and indirect, and it had seemed unlikely to show much useful connection to aging. But now that he had this windfall of epigenetic data at his disposal, there seemed no harm in looking.

Horvath started matching the methylation patterns with the ages of the twins. In any one saliva sample — or any sample from any tissue — not all the cells will show the same methylation pattern. But the proportion of cells that are methylated at a given cytosine in DNA can be measured. In one sample, for instance, 40% of cells might be methylated at a certain position; in another, that proportion might be 45% or 60%.

To his surprise, Horvath found a strong correlation between age and the proportion of cells with methylation, even when he looked at just one site in the DNA. Looking at more locations boosted the accuracy.

“This changed everything for me,” he said. “Once I looked at the signal for aging, it blew me away.”

Horvath built a model that predicted a person’s age from the methylation status of about 300 cytosines across millions of cells in a saliva sample. “You spit in a cup, and we can measure your age,” he said.

Soon he was building epigenetic clock models for evaluating the biological ages of blood, liver, brain and various other tissues. First, he measured the proportions of cells in each sample that showed methylation at specific sites. From that data, he created profiles of the tissues that described the proportions of cells methylated at each site.

To build a clock, he fed a computer thousands of epigenetic profiles along with the age of each tissue profiled. Through machine learning, the computer linked ages to methylation patterns. It also narrowed down the number of sites needed to predict age. The computer then weighted the significance of each site’s methylation in its calculations to create the best predictive formula for age, which Horvath tested on a separate set of samples of known ages.

Within two years, he had combined their separate tissue aging clocks into one formula for a “pan-tissue” clock, published in 2013. The pan-tissue clock was “the game changer,” said Daniel Belsky, an epidemiologist at the Columbia Mailman School of Public Health. The formula applied to any and all human cells containing DNA. And anyone could use it — Horvath put the software on the internet. By uploading their own methylation data, biologists could find out how much of a toll time had taken on cells in their samples.

Quantifying Decline

Horvath’s pan-tissue clock was miraculously accurate at predicting chronological age. It also seemed to reflect important underlying differences between chronological and biological age. Researchers discovered that when the epigenetic clock estimated that someone’s age was greater than their chronological age, the person faced a higher risk of disease and death. When the clock estimated that someone was younger, their risk went down. Even though the epigenetic clock was derived from chronological age data, its algorithm predicted mortality better than age did.

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