It's not often I get to compare biological interactions to dancing.
Oh, who am I kidding? I love to imagine the complex 'choreography' of molecules as they interact in myriad ways to power our cells and just get on with the business of living. A quick scan shows I've made the comparison in at least five published articles; I'm surprised my editor hasn't banned the term yet. (Shhh!)
But my most recent release has my favorite dance analogy yet. It's about work just published in Cell by Stanford geneticist Michael Snyder, PhD. In the study, he and his colleagues outline an entirely new way to determine how proteins that govern gene expression interact with DNA and with one another. The technique allows them to track the interactions of hundreds of factors simultaneously and track how a cell responds to complex environmental and developmental cues. Previously researchers have been restricted to following just two or three molecules at a time. As I explain in our release:
The challenge resembled trying to figure out interactions in a crowded mosh pit by studying a few waltzing couples in an otherwise empty ballroom, and it has severely limited what could be learned about the dynamics of gene expression.
Or, as Snyder distills the work:
At a very basic level, we are learning who likes to work with whom to regulate around 20,000 human genes. If you had to look through all possible interactions pair-wise, it would be ridiculously impossible. Here we can look at thousands of combinations in an unbiased manner and pull out important and powerful information. It gives us an unprecedented level of understanding.
Postdoctoral scholars Dan Xie, PhD; Alan Boyle, PhD; and Linfeng Wu, PhD, share first author credits on the paper. Together, they combined information from an international project called ENCODE with their own experiments to come up with their findings:
In this study, the researchers combined data from genomics (a field devoted to the study of genes) and proteomics (which focuses on proteins and their interactions). They studied 128 proteins, called trans-acting factors, which are known to regulate gene expression by binding to regulatory regions within the genome. Some of the regions control the expression of nearby genes; others affect the expression of genes great distances away.
The researchers used 238 data sets generated by the ENCODE project to study the specific DNA sequences bound by each of the 128 trans-acting factors. But these factors aren’t monogamous; they bind many different sequences in a variety of protein-DNA combinations. Xie, Boyle and Snyder designed a machine-learning algorithm to analyze all the data and identify which trans-acting factors tend to be seen together and which DNA sequences they prefer.
Their technique allows researchers to track the dynamic interactions in living cells under a variety of conditions and discover new patterns, or (dare I say it?) new steps to this ongoing molecular dance and get us ever closer to the goal of personalized medicine for all:
“We’d like to understand how these interactions work together to make different cell types and how they gain their unique identities in development,” Snyder said. “Furthermore, diseased cells will have a very different type of wiring diagram. We hope to understand how these cells go astray.”
Previously: 'Omics' profiling coming soon to a doctor's office near you?, Ask Stanford Med: Genetics chair answers your questions on genomics and personalized medicine and Stanford researchers work to translate genetic discoveries into personalized medicine
Photo by Paul Heaberlin
