Turning off the ‘aging genes’
January 3, 2014
Tel Aviv University researchers have developed a computer algorithm that predicts which genes can be “turned off” to create the same anti-aging effect as calorie restriction*. The findings, reported in Nature Communications, could lead to the development of new drugs to treat aging.
“Most algorithms try to find drug targets that kill cells to treat cancer or bacterial infections,” says Keren Yizhak, a doctoral student in Prof. Eytan Ruppin’s laboratory. “Our algorithm is the first in our field to look for drug targets not to kill cells, but to transform them from a diseased state into a healthy one.”
Ruppin’s lab is a leader in the growing field of genome-scale metabolic modeling or GSMMs. Using mathematical equations and computers, GSMMs describe the metabolism, or life-sustaining, processes of living cells. Yizhak’s algorithm, which she calls a “metabolic transformation algorithm,” or MTA, can take information about any two metabolic states and predict the environmental or genetic changes required to go from one state to the other.
“Gene expression” is the measurement of the expression level of individual genes in a cell, and genes can be “turned off” in various ways to prevent them from being expressed in the cell. In the study, Yizhak applied MTA to the genetics of aging. After using her custom-designed MTA to confirm previous laboratory findings, she used it to predict genes that can be turned off to make the gene expression of old yeast look like that of young yeast. Yeast is the most widely used genetic model because much of its DNA is preserved in humans.
Some of the genes that the MTA identified were already known to extend the lifespan of yeast when turned off. Of the other genes she found, Yizhak sent seven to be tested at a Bar-Ilan University laboratory. Researchers there found that turning off two of the genes, GRE3 and ADH2, in actual (non-digital) yeast significantly extends the yeast’s lifespan.
“You would expect about three percent of yeast’s genes to be lifespan-extending,” said Yizhak. “So achieving a 10-fold increase over this expected frequency, as we did, is very encouraging.”
Hope for humans
Since MTA provides a systemic view of cell metabolism, it can also shed light on how the genes it identifies contribute to changes in genetic expression. In the case of GRE3 and ADH2, MTA showed that turning off the genes increased oxidative stress levels in yeast, thus possibly inducing a mild stress similar to that produced by calorie restriction.
As a final test, Yizhak applied MTA to human metabolic information. MTA 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 currently there is no way to verify the results in humans, many of these genes are known to extend lifespan in yeast, worms, and mice.
Next, Yizhak will study whether turning off the genes predicted by MTA prolongs the lifespan of genetically engineered mice.
One day, drugs could be developed to target genes in humans, potentially allowing us to live longer. MTA could also be applied to finding drug targets for disorders where metabolism plays a role, including obesity, diabetes, neurodegenerative disorders, and cancer.
* Restricting calorie consumption is one of the few proven ways to combat aging. Though the underlying mechanism is unknown, calorie restriction has been shown to prolong lifespan in yeast, worms, flies, monkeys, and, in some studies, humans.
Abstract of Nature Communications paper
The growing availability of ‘omics’ data and high-quality in silico genome-scale metabolic models (GSMMs) provide a golden opportunity for the systematic identification of new metabolic drug targets. Extant GSMM-based methods aim at identifying drug targets that would kill the target cell, focusing on antibiotics or cancer treatments. However, normal human metabolism is altered in many diseases and the therapeutic goal is fundamentally different—to retrieve the healthy state. Here we present a generic metabolic transformation algorithm (MTA) addressing this issue. First, the prediction accuracy of MTA is comprehensively validated using data sets of known perturbations. Second, two predicted yeast lifespan-extending genes, GRE3 and ADH2, are experimentally validated, together with their associated hormetic effect. Third, we show that MTA predicts new drug targets for human ageing that are enriched with orthologs of known lifespan-extending genes and with genes downregulated following caloric restriction mimetic treatments. MTA offers a promising new approach for the identification of drug targets in metabolically related disorders.