As a child, Arya Rao often awoke with her parents and accompanied them when they received a late-night call from the hospital in the small farming town of Big Rapids, Michigan. She watched from her perch at the nurse’s station as her mother, a pediatrician, sat with a sick child on a ventilator or as her father, an orthopedic surgeon, was called in to perform an emergency procedure.
She greeted Amish neighbors from their rural community who arrived by horse and buggy with wounds for her father to stitch and dress. Rao’s parents’ dedication to the well-being of their neighbors left a deep impression on their daughter.
Rao’s parents, who had immigrated from India, were “the only people of color trying to establish a medical practice” in their town, said Rao, a Paul and Daisy Soros Fellow, Harvard/MIT MD-PhD program student, and Harvard Kenneth C. Griffin Graduate School of Arts and Sciences doctoral candidate in bioinformatics and integrative genomics at Harvard Medical School.
“It was hard for them, but they focused on building a home, forming connections, and caring for families in a place that did not have — and still doesn’t have — much specialized medical care,” she added. “They only planned to be there a few years, but it became home, and our family has now been part of that community for 30 years.”
Rao hopes to make a career as a physician-scientist, focused on caring and healing as well as on improving the treatments and therapies available to patients. She conducts research at the Broad Institute of MIT and Harvard that is deeply integrated with the work she plans to do as a clinician.
She draws inspiration from natural selection and leverages artificial intelligence to engineer new gene therapies that could make medicine more precise and personalized.
Tracking the path of natural selection
Life on Earth ranges from bacteria that live at the bottom of the ocean and survive in subzero temperatures to organisms that dwell in volcanoes, with human beings somewhere in the middle.
At the heart of this astonishing diversity are mathematics and code: the letters A, C, T, and G — which represent adenine, thymine, cytosine, and guanine — the four chemical building blocks of DNA. Arranged in base pairs, these four letters can be combined in an almost infinite number of ways, yet only a small fraction of those combinations account for the genetic code of all living organisms on Earth.
This raises a question: If such diversity results from a relatively small amount of manipulation of that code, what else might be possible?
“That is the question I have wanted to answer in my PhD research,” said Rao, whose co-advisor is Pardis Sabeti, professor of organismic and evolutionary biology in the Faculty of Arts and Sciences at Harvard University and a core institute member of the Broad Institute.
“There is so much more we could do in this space that evolution has not yet explored. But we can use the principles evolution has uncovered through thousands and thousands of years of experimentation to navigate that space in a more informed way.”
Traditionally, statistical methods have been used to identify genes under selection. But statistical approaches often can’t identify the specific mutation that gives a population a greater capacity to adapt to its environment, such as a cold climate or high altitude.
But using artificial intelligence, Rao can track where natural selection has taken place in the genome — and investigate whether those changes could inform new strategies to treat disease.
“We can train AI models to learn the genomic signatures of natural selection,” she said. “Our goal is to go beyond identifying where selection has occurred and predict which specific mutation actually drove that adaptation. That lets us ask what the variant does biologically and whether the mechanism it points to might be therapeutically useful.”
Rao and her other co-advisor, Sangeeta Bhatia, visiting professor of the Wyss Institute for Biologically Inspired Engineering, are putting these principles into practice.
They are studying host-directed prophylaxis against malaria, the infectious disease responsible for 610,000 human deaths and 282 million cases worldwide in 2024. Scientists cannot test every possible human genetic variant in a wet lab to see how each affects susceptibility, Rao said, but by using AI and population genetics, they can access that experiment indirectly.
Their work uses small interfering RNAs, which help prevent the production of proteins that malaria uses to enter cells or facilitate infection. The same approach could inform progress in diseases beyond malaria, Rao said.
Flipping the switch for precision medicine
The second half of Rao’s dissertation research, part of the Ladders to Cures Scientific Accelerator at the Broad Institute, looks to design the regulatory elements used in gene therapies.
A major challenge in gene therapy is controlling where in the body a therapeutic gene is active and how strongly it is expressed, Rao said. “I’m developing synthetic regulatory DNA — short sequences that function like switches — to control gene activity in different cell types.”
Working with Sabeti and Bhatia, Rao builds and trains machine-learning models using data from thousands of DNA sequences tested across a wide range of cell types, including skeletal muscle and cardiac cells.
“It’s become possible to test thousands of DNA sequences at once, but it’s impossible to generate that amount of data for every cell type in the body,” Rao said. “Our deep-learning approach asks whether we can learn the underlying rules from the data we do have and use them to design sequences that work in new settings.”
The team’s models learn the DNA patterns that control gene activity in different cell types, then use those rules to generate new sequences designed to activate gene expression only in specific tissues.
Using these models, Rao’s group has been able to design genetic switches that are far more compact and targetable than anything used before — an important step toward making gene therapy more effective and viable.
“If I have a gene therapy and want it expressed only in muscle or the liver, we can train an AI model to learn the optimal path toward that new synthetic function,” she said.
A key application of Rao’s work is in Duchenne muscular dystrophy, a progressive muscular weakness that most often affects children and results from a deficiency or malformation of the protein dystrophin. Researchers have been trying to develop gene therapies to restore dystrophin function, but thus far clinical trials have not gone as hoped and include risk of serious toxicities.
“More compact and precise regulatory elements could help researchers better control where and how strongly a therapy is expressed while taking up less of the limited space available in the vector,” said Rao.
No career without care
While one aspect of Rao’s endeavor to improve precision medicine is embodied by deep molecular research, another involves thinking about health systems, big data, and artificial intelligence.
To this end, Rao also works with Sabeti at Sentinel, a pandemic-prevention initiative supported by the MacArthur Foundation that strives to strengthen outbreak detection and response in partner locations, including through new AI-enabled tools.
“I think a lot about how new technologies actually reach the people and places that could benefit from them,” said Rao. “It’s important that the work is grounded in what our partners need and can use.”
As she looks ahead, Rao hopes to become a reconstructive surgeon, caring for children with musculoskeletal disorders and practicing pediatric plastic surgery.
The possibilities for precision surgery are “really interesting,” she said, such as the idea of making therapeutic molecular modifications to tissue before performing a tissue graft or transplant.
“Patient care is what drives me,” she said. “I love the science, and I’m interested in how we can make interventions more precise at every scale. But at the end of the day, I want to be a physician who can bring that work back to the person in front of me.”
Adapted from a Harvard Griffin GSAS news story.
