USEFUL 'JUNK'
Michael Snyder, professor and chair of genetics at the School of Medicine, was one of the leaders of a massive, five-year effort to discern the function of the more than 90 percent of the human genome that doesn't code for cellular proteins—so-called "junk DNA." The project, known as the Encyclopedia of DNA Elements, or ENCODE, concluded earlier this year. In September scientists working in 32 labs around the world simultaneously published 30 papers in the journals Nature, Genome Biology and Genome Research.
In them, the researchers report that some 80 percent of noncoding DNA has a biological function—such as determining where and when genes are expressed. While nearly every cell in the body contains the recipe, in the form of DNA, for making all 20,000 possible proteins encoded by the human genome, no one cell does. Whether a cell becomes bone or muscle or nerve depends on protein production being switched on and off in the proper sequence.
By integrating ENCODE with other databases of genetic variations, "we now have the beginnings of a regulatory network, or wiring diagram, for a human being," Snyder says. "This global overview will help us understand how changes in the genome cause disease, and also to see how an individual's unique genetic code may affect his or her health in meaningful ways."
Snyder, who also directs the Stanford Center for Genomics and Personalized Medicine, made national headlines earlier this year for using his own whole-genome sequence and other biological measurements to track his development of type-2 diabetes in real time.
DIAGNOSIS ON A CHIP
Researchers at Intel Corp. and the School of Medicine collaborated to synthesize and study a silicon wafer—the same kind used in computer microprocessors—dotted with short segments of biological proteins. They then used the chip, dubbed the Intel array, to simultaneously analyze thousands of protein-protein interactions, in this case to identify patients with a particularly severe form of lupus.
By enabling researchers to observe how proteins interact in the body, the technology has the potential to improve diagnosis for a plethora of diseases. "When I see patients in the clinic right now," says associate professor of medicine Paul "P.J." Utz, co-senior author of the research, "I may know they have arthritis, but I don't know which of the 20 or 30 types of the disease they have." Narrowing down the possibilities using current methods can take days or even weeks. Now, says Utz, "we can measure thousands of protein interactions at a time, integrate this information to diagnose the disease and even determine how severe it may be."
According to Utz's co-senior author Madoo Varma, director of life science research for Intel's Integrated Biosystems Laboratory, the eventual goal is to integrate a semiconductor circuit with the array to create a minicomputer that could diagnose disease, assess therapeutic efficacy and even help design better drugs.
