Computer Algorithm Uses Epigenetics to Identify “Aging Genes”

Restricting calorie consumption without malnutrition has been shown to prolong lifespan in various species, including yeast, worms, flies, monkeys, and humans. Although the underlying mechanism is not yet known, an advanced computer algorithm sheds light on the concept and potential causes.

At Tel Aviv University’s Blavatnik School of Computer Science, Keren Yizhak and her colleagues developed something called a genome-scale metabolic model (GSMM), creating a computer algorithm that predicts which genes can be “turned off” to produce the same anti-aging effect as calorie restriction. The study in Nature Communications provides results that could lead to the development of new anti-aging drugs.

The team of scientists in Israel produced an algorithm that is the first of its kind in the field – an algorithm that looks for drug targets to transform cells from a diseased state into a healthy one, as opposed to most which “try to find drug targets that kill cells to treat cancer or bacterial infection,” according to Yizhak.

Professor Ruppin’s lab at Tel Aviv University is a leader in the field of GSMM, which has gained enormous momentum recently. Using mathematical equations, GSMMs reconstruct the metabolic network of living cells by integrating biochemical metabolic pathways with genome sequences. The individual models can then be used as digital laboratories, conducting typically time-consuming experiments in an instant. The research team’s novel algorithm, called metabolic transformation algorithm (MTA), “takes information about any two metabolic states and predicts the environmental or genetic changes required to go from one state to the other.”

Yizhak first confirmed previous laboratory findings with the newly-designed algorithm before she used it to figure out which genes, when turned off, could make old yeast look like that of young yeast. Some genes that MTA identified were already known to lengthen the lifespan of yeast when turned off, but Yizhak sent seven other genes she found to a lab at Bar-Ilan University for further testing on actual, non-digital yeast. Researchers there found that turning off two genes, GRE3 and ADH2, significantly extends the yeast’s lifespan.

Due to the systemic view of cell metabolism it provides, MTA also gives us insight into how these genes contribute to changes in genetic expression. For instance, they found that turning off GRE3 and ADH2 genes increased oxidative stress levels in yeast, possibly inducing a mild stress similar to that produced by calorie restriction.

When MTA was applied to human metabolic information, it was able to “identify a set of genes that can transform 40 to 70 percent of the differences between the old and young information from four different studies.” While there is no way to verify the results in humans yet, many of the genes detected by MTA are known to extend lifespan in yeast, worms, and mice. Yizhak’s future direction involves testing whether or not turning off the genes identified by MTA would extend the lifespan of genetically engineered mice.

Someday, MTA could target specific genes in humans, possibly leading to the development of drugs that prolong the human lifespan or even drugs which can cure metabolically related disorders, including obesity, diabetes, neurodegenerative disorders, and cancer.

Source: Learn all about it and read more about their findings here: Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Yizhak, K., Gabay O., Cohen, H., Ruppin, E.

References: American Friends of Tel Aviv University. Turning off the “Aging Genes.” 2014.