Even an organism as seemingly simple as the worm is surprisingly complex.
The DNA of the worm C. elegans comprises more than 20,000 genes. Humans have more than 30,000. So the old way of looking at genes, one at a time, simply isn't productive when it comes to medical and scientific discovery, according to a new study.
The journal Science this month features a study from the Huntsman Cancer Institute at the University of Utah and a collaborator at the University of California at Santa Cruz that may change the way genetic research is done. The researchers have come up with a "unique" computational approach to look for gene expression, examining thousands of genes at a time.
With the sequencing of the genome of various organisms, from humans to fruit flies and worms, scientists have gotten good at predicting genes, said Susan Mango, associate investigator at HCI and research team leader. They are, however, "much less good at predicting regulatory things" like what will trigger expression of a certain protein or the effect of environmental input. "It's hard to look at a gene and say 'I predict a gene will be on when it's sunny and off when it's raining,' but there's a huge interest in being able to do that."
The study offers a way to start predicting such regulation, she said.
Since the organisms are all complex, it's too cumbersome to search a little at a time for information — genetic discovery would slog along without much progress. But the ability to look at many genes at a time changes the pace of research and discovery.
The method uses microarray technology with computational approaches in order to predict where in the genome a specific regulatory sequence might be found. The research team, which also included Wanyuan Ao, Jeb Gaudet, James Kent and Srikanth Mattumu, scoured the worm's genome to find code that might serve as regulatory sequences responsible for the growth and development of the worm's foregut, or pharynx, an area where much of Mango's research has centered. Of the seven they identified, testing proved five were actually regulatory sequences.
Follow-up tests validated their predictions, and in one case, they found the protein binding that element.
The Improbizer algorithm they use, developed by Kent, picked up regulatory sequences both quickly and accurately, Mango said.
The next job for Mango is answering questions about regulation in cell metabolism and cell differentiation, both areas of great importance in cancer research.
"We are taking it in a number of directions," she said. "We are trying to find pathways, regulatory modules, and what they do and how they have such dramatic effects."