National Geographic Emerging Explorer and computational geneticist Pardis Sabeti is on a mission to combat infectious disease. Her weapon of choice? Complex algorithms.
Pardis Sabeti is a computational biologist using medical and evolutionary genetics to better understand the origins of our acquired traits as well as to help prevent the spread of infectious diseases. At Harvard University she is an associate professor at the Center for Systems Biology and senior associate member of the Broad Institute.
Can math cure malaria? The algorithms Pardis Sabeti invents and wields are helping crack genetic codes of how such infectious diseases adapt, spread, and may one day be prevented. "Humans and virulent microbes are both governed by genetic codes that tell them how to evolve," she explains. "There's a constant evolutionary arms race going on between them. Humans develop genetic resistance to particular diseases, while microbes develop resistance to antibiotics and our immune defenses. Unlocking the genetic codes of humans and pathogens can help us understand how to intervene."
In 2001, Sabeti developed a breakthrough algorithm that allows geneticists to scan for genes that reveal natural selection at work. Tracing the genetics behind natural selection is crucial to unraveling when and how certain mutations increase humanity's odds of survival. Her algorithm, now a key evolutionary detective tool, is rooted in the fact that mutations that enhance our ability to survive or reproduce are more likely to be passed on to future generations. By searching a genome for mutations that have become very common very fast, telltale signatures such as resistance to malaria can be identified.
The challenge is finding which change was the actual driver in a genetic mutation. "It's like looking at a huge crater and not being able to locate the precise center or tell what caused it," Sabeti describes. "So we developed another algorithm that allows us to narrow down a region of 10,000 mutations to perhaps ten that may be biologically meaningful and worth testing."
One of her genome scans detected a signature that has become central to her lab's efforts. "We found evidence for recent adaptation in a human gene critical for infection with Lassa virus, an infectious agent which, if it spread, would be catastrophic. It is known that if you knocked out this gene, the virus couldn't get at the cells. Probing the genomes of individuals around the world led us to a signal of natural selection in a population in Nigeria where the deadly virus was first discovered."
Although 21 percent of that population has been exposed, 50 to 90 percent of those infected (depending on the region) show mild symptoms or none at all. "We believe that natural selection has driven resistance to the virus in populations affected," Sabeti says.
In collaboration with the lab her group helped to establish in Nigeria, she and her team have now sequenced the Lassa virus in so many individuals they are able to design improved diagnostics to quickly test for exposure, understand which strains are most viral, and explore how certain people develop resistance. "Working with a deadly virus in rural Africa has its challenges," she acknowledges, "but your impact can be major and immediate."
Sabeti has also long been on the trail of another killer. Malaria claims up to two million lives each year. The pathogen's enormous genetic diversity allows it to rapidly adapt and evade treatment. Sabeti is characterizing this diversity in order to better understand malaria's drug resistance, help eliminate disease epicenters, track its transmission, and ultimately outsmart the deadly parasite with more effective drug strategies. Another methodology she recently helped develop potentially sheds new light on typhoid. Her computations help pinpoint mutations driven by selection and reveal immune gene receptors important in human response to the infection.
One of Sabeti's newest findings is extremely old. Her methodology uncovered a mutation that emerged in China 30,000 years ago and migrated to Native American populations. Among other changes, this single gene mutation increased the number of active sweat glands. Sabeti's group demonstrated the finding by isolating and incorporating the gene into mice. "This mutation is clearly an important adaptive human evolutionary trait," she notes, "perhaps beneficial in regulating body temperature during childbirth, running, and other essential activities."
"The process of discovery in my field is very incremental. But there are moments when you realize you know something about the world nobody else knows. That's extraordinarily exhilarating," she says. "So much of the physical world has been explored. But the deluge of data I get to investigate really lets me chart new territory. Genetic data from people living today forms an archaeological record of what happened to their ancestors 10,000 years ago. Most of my work may happen at a computer, but it's still a new and very exciting frontier."