While surfing the internet not long ago, a Harvard biologist stumbled upon a pile of research data including unpublished leftovers from an unresolved genetic study. It wasn't unusual: Data like that can be found all over the web.
33 Year-old Indian-American professor Vamsi Mootha using a unique computational method, mined the data and indentified a gene underlying a rare but fatal pediatric disorder called "Leigh Syndrome, French-Canadian" variant or LSFC. Astonishingly, he did it in a single weekend.
For the diabetes study, Mootha applied computational approaches similar to those used on the pediatric study to both found reaserch and data his team generated independently. As a result, he located a set of three genes that revs up the energy-producing ability of muscle cells and might lessen diabetes' harmful effects. Again, the finding was notable not only because of its potential consequence, but also because Mootha has found a way to sort the hay in the genetic haystack to discover the proverbial needle.
Each human cell contains all of the body's approximately 23,000 genes. But not every gene in every cell is active; some are silent. It's the repertoire of active genes that makes a muscle cell different than a liver cell or skin cell. In a diseased cell, the program is altered. The correct genes are activated too much or not enough.
A relatively new technology, called microarrays, enables interrogation of every gene to determine how active it is. Rather than just look at a slice of diseased cell tissue under a microscope, scientists can see how many of the 23,000 genes are switched on or off and to what degree. Multiply all that data by all the patients in research studies such as Mootha encountered, and the result is an intimidating mass of numbers to crunch and assess.
"Vamsi got a hold of the data from the internet, but he said you can't compare gene by gene. You'd be doing so many comparisons, you aren't going to find anything statistically significant," said Alan Attie, a University of Wisconsin biochemistry professor.
"Vamsi re-curated the list of genes, making about 120 categories by functional group," such as genes that make fat or carbohydrates, or control respiration, etc., Attie said. "It turned out that the mitochondrial respiration group showed a big difference. But looking at the individual gene level, there would have been only modest differences."
Mootha and collaborators had found that the master regulator of gene expression for genes in the mitochondria were different between diabetics and non-diabetics. Though Mootha, whose undergraduate degree is in mathematics, calls his approach "relatively simple computation," National Institutes of Health research physiologist Robert Balaban disagrees.
"Vamsi is not attempting to reduce the problem to its simplest elements, but to accept the complexity of biology and develop the tools we will use over the next several decades to unravel the interactions that naturally occur," he said.
One of those tools is a software program that a graduate student is developing, based on Mootha's algorithms, which other scientific researchers can use to profile diseases.
Mootha's diabetes discovery was significant for both the disease and other researchers trying to mine masses of data, but his passion is investigating mitochondrial mutations linked to rarer diseases such as LSFC (.pdf).
That's how he envisions employing his windfall. "$500,000 is really not enough money to fund a large, modern genomics lab, but it might be enough to jump-start a research program focused on developing therapies for rare mitochondrial disorders," Mootha said.
"Big pharma will not develop drugs to combat these disorders anytime soon since the market is so small, so the onus is on private institutions and academic labs to develop new therapeutics."
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