Understanding how DNA is folded, wound up, and packaged inside nuclei provides an additional layer of biological information to what’s written in the base pairs sequences. F1000 Faculty Member and National Cancer Institute cell biologist, Tom Misteli, discusses a paper that presents a high-resolution map of the yeast genome and illustrates the importance of building such maps (Nature, 465:363-67, 2010).
The Scientist: Now that the genomes of hundreds of species, including yeast and humans, have been sequenced, why is it important to be able to visualize the three-dimensional shape DNA takes in cells?
Tom Misteli: It’s a fundamental property of the genome to be organized, to be folded in some way inside the nucleus. Now it’s becoming clear...
TS: Chromosomes are generally depicted as tightly wound X and Y shapes—just as they appear when cells are dividing. But what do scientists know about what they look like the rest of the time?
TM: If you have cycling cells, then chromosomes only look like [Xs and Ys] for about 20 minutes every 24 hours, when the cells divide. So the real question is: What do real chromosomes look like in their natural habitat? That’s what these methods are trying to get at. We’ve known what they look like from microscopy experiments, which have shown that a chromosome exists in what’s called a chromosome territory. The DNA that belongs to one particular chromosome is not randomly dispersed in the nucleus, but is nicely bunched together in a corner of the nucleus, and then the next chromosome sits next to it, and so on.
TS: A team of researchers from the University of Washington, headed by William Noble, describes the yeast’s genome as a water lily “with 32 chromosome arms jutting out from a base of clustered centromeres.” Did their model yield any surprises?
TM: It’s not a revolutionary model or a model that nobody had thought about. There are some details that are now put on a stronger footing. The significance of this paper is really the method. It’s a very high-resolution picture of the genome. The same method had been applied a few months earlier to the human genome. But because the human genome is much larger, the resolution of that picture was a lot lower. Here they’re talking about one-kilobase resolution. So that gets down to the scale of individual genes.
TS: And how were they able to do this?
TM: You chemically cross-link the DNA inside the nucleus. You’ve now established essentially who touches whom—what part of the genome touches what other part of the genome. You extract the DNA and you cut, sort of randomly, your entire genome. Then you can isolate those pieces, and you re-ligate them and get them in a circle of DNA. Then you find out what the sequence is in all these circles that you’ve isolated. Because you can then go back to the genome, you can find out what pieces of the genome touch each other, and that builds up a map of interactions.
TS: The genomes of a whole population of yeast cells were used to build this map. What is the drawback of employing this statistical approach?
TM: It could be that there is a very important interaction between chromosome 5 and 6, but it only happens in 5 percent of all cells. But those 5 percent may drive the population; or those 5 percent might be your stem cells of the population, or your cancer cells, and you will likely not pick it up as a very strong interaction. So you’re only picking things up which really dominate the population.
TS: How can these individual maps lead to a better understanding of genome structure and function?
TM: As these methods become more routine, people will map these sorts of things in more cell types, under more physiological conditions, during differentiation, during development, etc. And so you’re essentially building up a library of genome maps. The real value of this comes in trying to figure out how these interactions are important for function.
Misteli’s lab researches how the spatial organization of the genome relates to function and how it is important for cancer and aging. You can access his review of the paper here.