With this newly documented diversity, the scientists were able to explore more thoroughly the genetic networks that underlie these complex traits—almost as if they went from hunting in the dark with a flashlight to hunting in a room full of overhead lamps. For instance, Beverly Paigen, Ken’s wife, has spent the past few years studying genes that influence levels of “good” cholesterol (high density lipoprotein cholesterol, or HDL). One strain of mice has high levels, and another has very low levels. Paigen bred the two strains together and then raised their pups. Once she found genetic markers strongly linked to high or low levels of HDL, she searched around the markers for the genes that were at work. (The hunt has become much faster now that other researchers have sequenced the entire mouse genome.)
Beverly Paigen expected that she would simply build on the enormous amount of work already done in this area, finding a mix of new genes and genes that had already been identified in other experiments. “I thought we would find maybe half and half,” she says. But she was wrong. There were far more novel genes than anyone had expected: “We’re finding mostly new stuff.”
Coming up with a list of genes is a good starting point for mapping a gene network, but it is only a starting point. Imagine if you were an alien trying to understand people on Earth solely by studying satellite pictures. You notice that they put up umbrellas whenever it is cloudy, yet the clouds themselves do not directly cause the umbrellas to go up. Unless you understand that there is rain sprinkling down, the umbrellas will remain a mystery. This is the quandary that geneticists like Beverly Paigen face when they discover genes belonging to the same network. A gene may influence a trait directly, or it may influence another trait that influences the trait in question. Or—to make the puzzle truly migraine inducing—the gene may have different effects on both traits. Unless you understand how those genes work together, the list of genetic markers will remain a mystery.
Gary Churchill is leading Jackson Laboratory’s efforts to cut through that mystery. He is a statistician by training, and he is an expert at recognizing patterns in what may seem like random data. He looks for many markers that tend to turn up together in mice with a single trait, and he looks at whether each gene belongs to other groups that are associated with other traits, using his own statistical tricks to tease all of this apart. Once Churchill has identified potential links among the genes, he creates a mathematical model of the entire network and uses it to predict what sort of mice will be produced from various combinations of the genes. If his predictions hold, he knows he has made a successful model. If not, he can go back and fine-tune it.
Recently Churchill decided to go after the gene network that controls body weight. The map they came up with looks like a flowchart from hell
Recently Churchill and his Jackson Laboratory colleagues decided to go after some big genetic game, the gene network that controls body weight. With 300 million people now suffering from obesity worldwide, fat has become a global epidemic. For years geneticists have searched for the genes that determine whether people gain weight easily or not, but it has been a frustrating experience. In the 1990s studies on a strain of obese mice developed at Jackson Laboratory guided Rockefeller University scientists to a protein they dubbed leptin. When they injected leptin into the obese mice, the mice lost weight. A major biotechnology corporation, Amgen, seized on the discovery, hoping to create a weight-loss drug. But they could not replicate the effect in humans.
Rather than focus on a single gene, Churchill and his colleagues decided to explore the entire weight-control network. They selected a big, lean strain of mice and mated them with small, fat ones. The offspring of this union grew to many different sizes and weights. Churchill and his team then measured how large the animals grew and how much of their body weight was fat versus muscle. They also measured how the fat was spread out on each mouse. Like us, mice tend to accumulate fat in certain places, like their haunches and their bellies. Finally, the scientists scanned the genome of each mouse for hundreds of markers to see which ones were linked tightly to each trait.
The map they came up with looks like a flowchart from hell. Churchill’s group identified a dozen sites in the mouse genome where genes are influencing the body weight of mice. But the genes have different effects. Some make mice large-bodied, and being big makes mice more likely to get fat. But they also found genes that had separate effects on both body size and fat levels. In some cases, the same gene could make a mouse both big and lean. Other genes influenced only how fat the mice were, with no effect on their body size. Still other genes determined the size of different fat pads. One region of mouse DNA appears to make mice fat overall while actually making the fat pads on their haunches smaller.
While networks like the one that controls body weight may be complicated, Churchill takes some comfort in the fact that they are not so complicated as to be incomprehensible. “The good news is that it doesn’t seem that everything interacts with everything else,” he says. The networks are small enough that it may be possible to understand all their parts, a depth of knowledge that should point to new treatments for disease.
In fact, Beverly Paigen argues that in some cases treating gene networks may be easier than trying to treat single genes. “Suppose you find only one gene, and that’s the cause of the disease,” she says. “Suppose you can’t get a drug to it. Suppose it’s just in a place in the body where it’s intractable. Now suppose the gene is in a network and there are other things in the network that interact with it. You might be able to use a drug target at another piece of the network.”
Despite the recent advances, the mice from the Phenome Room are just barely beginning to divulge their secrets. Jackson Laboratory scientists are still struggling to distinguish the effects of closely spaced genes that are linked to the same genetic markers, for example. The problem, Churchill explains to me, is that 40 strains of mice are just not enough: “If you want to go forward, you’ve got to go big.”
Instead of a Bar Harbor’s worth of mice, he needs the equivalent of a large city. He and other mouse experts around the country have therefore launched a project, the Collaborative Cross, to produce new inbred strains from lab-grown mice, each with its own unique combinations of alleles. So far, the collaboration has started 500 inbred strains. Churchill hopes to have 1,000 by the time they are finished in 2010. For the first time in history, he predicts, we will finally be able to see in crisp, sharp detail the genetic networks that allow us to live and cause us to die.
“We spent a hundred years trying to figure out what the parts are,” Churchill says. “We now have the part list. We can start to ask how the parts are assembled.”
