Silencing ‘Aging Gene’ Leads To 10-Fold Increase In Lifespan For Yeast Population
Instead of scrambling for a dry desk job with a solid 401k and a lavish health care plan, why not just silence the aging gene and work at a microbrewery, chocolate factory, or petting zoo until you call it a day at 85? In a new study from Tel Aviv University, researchers describe a new computer algorithm that can pinpoint the genes that drive the aging process. Aside from helping seniors look just as good as their grandchildren, the model may help reduce the incidence of Alzheimer’s disease, Parkinson’s disease, and other age-related neurodegenerative disorders.
Prevailing research suggests that, if you want to look younger in old age, your best bet is to eat right. This strategy appears to be universally applicable: calorie restriction has been shown to reduce aging in a range of organisms, including yeast, worms, flies, and monkeys. That said, the biological mechanism remains obscure, and trials involving humans have yielded conflicting results.
The current study, which is published in the journal Nature Communications, sought to revive this anti-aging strategy by illuminating its genetic basis. In other words, the authors wanted to understand why reining in your calorie intake stops cells from working themselves to death. A reliable map of these genes could theoretically allow society to approximate the anti-aging effect of calorie restriction without actually restricting calories.
To investigate, the team relied on the growing scientific world of “omics” — a neologism derived from booming fields like genomics, proteomics, and metabolomics. In formal terms, this type of inquiry concerns the totality of a particular system. For a scientist, such data is extremely useful, as it allows her to infer specific phenomena from fundamental, system-wide rules. In this particular case, high-quality genomic data makes it possible to hunt specific genes with mathematical models rather than expensive and time-consuming lab efforts.
Keren Yizhak, a doctoral student at Tel Aviv University and lead author of the study, told reporters that this “digital laboratory” is the first of its kind. “Most algorithms try to find drug targets that kill cells to treat cancer or bacterial infections,” she said. “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.”
In an experiment with yeast cultures, these sophisticated mathematical models allowed the team to identify the genes GRE2 and ADH2 as factors driving the aging process. When these genes were silenced, the population’s lifespan increased significantly. “You would expect about three percent of yeast’s genes to be lifespan-extending,” Yizhak explained. “So achieving a 10-fold increase over this expected frequency, as we did, is very encouraging.”
Although the algorithm can theoretically lead to the same results in humans, a number of factors problematize any actual experimentation. The most glaring issue is perhaps the time it would take to gather any hard results. Human lifespans are already too long for a single research team to study.
That said, the method could come to benefit patients at risk of developing age-related disorders. Slowing the aging process could significantly delay the onset of obesity, diabetes, cancer, and neurodegenerative disorders. In this sense, the algorithm could eventually help alleviate a heavy burden on society.
Source: Yizhak K, Gabay O, Cohen H, Ruppin E. “Model-based identification of drug targets that revert disrupted metabolism and its application to ageing.” Nature Communications 4. 2013.