Machine learning reveals secrets of aging in flies and humans

Discoveries that impact lifespan and healthspan in fruit flies are usually tested in mice before being considered potentially relevant in humans, a process that is expensive and time intensive. A pioneering approach taken at the Buck Institute leapfrogs over that standard methodology.

Utilizing cutting-edge machine learning and systems biology, researchers analyzed and correlated huge data sets from flies and humans to identify key metabolites that impact lifespan in both species. Results published online in Nature Communications suggest that one of the metabolites, threonine, may hold promise as a potential therapeutic for aging interventions.

"These results would not have been possible without this pioneering approach," says Buck professor Pankaj Kapahi, PhD, senior author of the paper.

Threonine has been shown to protect against diabetes in mice. The essential amino acid plays an important role in collagen and elastin production and is also involved in blood clotting, fat metabolism and immune function.

The method – simplified

Vikram Narayan, PhD, a postdoctoral fellow then cross-referenced findings with human data from the massive UK Biobank. "Using the human data allowed us to focus on interesting metabolites to those that are conserved in both species. It also allowed us to uncover the impact of those metabolites in humans," he says. Importantly, the team then brought those relevant metabolites back into the fly to validate results.

The results

Larger implications

Kapahi hopes the larger research community will begin employing this method. "So many times we find things that work in worms and flies and then we don't have the resources to move the basic science forward. This approach allows us to say with a lot more certainty that discoveries are going to be relevant in humans." Kapahi says this method may reduce the need for studies in mice, something he welcomes.

Source:

Buck Institute for Research on Aging

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