Drug Perks up Old Muscles and Aging Brains
News May 14, 2015
Researchers at the University of California, Berkeley, have discovered that a small-molecule drug simultaneously perks up old stem cells in the brains and muscles of mice, a finding that could lead to drug interventions for humans that would make aging tissues throughout the body act young again.
“We established that you can use a single small molecule to rescue essential function in not only aged brain tissue but aged muscle,” said co-author David Schaffer, director of the Berkeley Stem Cell Center and a professor of chemical and biomolecular engineering. “That is good news, because if every tissue had a different molecular mechanism for aging, we wouldn’t be able to have a single intervention that rescues the function of multiple tissues.”
The drug interferes with the activity of a growth factor, transforming growth factor beta 1 (TGF-beta1), that Schaffer’s UC Berkeley colleague Irina Conboy showed over the past 10 years depresses the ability of various types of stem cells to renew tissue.
“Based on our earlier papers, the TGF-beta1 pathway seemed to be one of the main culprits in multi-tissue aging,” said Conboy, an associate professor of bioengineering. “That one protein, when upregulated, ages multiple stem cells in distinct organs, such as the brain, pancreas, heart and muscle. This is really the first demonstration that we can find a drug that makes the key TGF-beta1 pathway, which is elevated by aging, behave younger, thereby rejuvenating multiple organ systems.”
Aging is ascribed, in part, to the failure of adult stem cells to generate replacements for damaged cells and thus repair the body’s tissues. Researchers have shown that this decreased stem cell activity is largely a result of inhibitory chemicals in the environment around the stem cell, some of them dumped there by the immune system as a result of chronic, low-level inflammation that is also a hallmark of aging.
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.