We have a winner!
A team of scientists at East Carolina University who are challenging dogma that says once heart cells are deprived of oxygen, they cannot recover, has emerged as the crowd favorite of STAT Madness 2018.
The team, led by Jitka Virag, associate professor of physiology, beat a group from Children’s National Health System in Washington that created a facial analysis technology that matches a child’s features to patterns found in genetic syndromes, accurately detecting differences among diverse populations.
Virag said she was thrilled with the news.
“This has been quite a journey! I’m so grateful and humbled by the incredible support from Brody and ECU faculty, students, and staff as well as the Greenville community,” she said.
East Carolina beat out 63 other entries to win the crowd vote in STAT Madness, STAT’s yearly celebration of biomedical science. The contest, in its second year, drew some 150 entries from dozens of universities, colleges, and research institutions from all over the United States. Sixty-four of those entries competed in a single-elimination bracket that ran through nearly five weeks of voting. By the end of the contest, there were more than 372,000 votes cast for entries that spanned a range of subjects including cancer, CRISPR, mental health, diagnostics, and neuroscience.
Virag said she was thrilled that the contest drew attention to her work and that of the university, in Greenville, N.C. “The benefits to all of us in terms of the research we have going on here and the evolution of this institution are immeasurable and exciting!”
Bringing heart cells back to life
In Virag’s part of North Carolina, she thinks about people who have not just heart disease, but also obesity and diabetes, and whether one day her work could help them.
“When you tell people who are not scientists about this, of course they get really excited,” Virag said about her research. “Everyone knows somebody who has suffered some form of heart disease, so that’s why I so badly want to help these people.”
She began her work a dozen years ago studying a family of molecules called ephrins. Heart muscle cells, called cardiomyocytes, are not renewable, she said. The ones you are born with are all you get. In a heart attack, the oxygen-carrying blood supply to the heart is cut off, and those cells begin to die 40 minutes later. The prevailing dogma says once a cardiomyocyte dies, there is no way to bring that cell back to life.
Virag and her colleagues would disagree.
They have found that injecting a particular kind of ephrin — ephrin A1 — into a mouse’s heart after the equivalent of a heart attack can cut in half the damage to those heart muscle cells.
“We were shocked,” Virag recalled. “Nobody believed us.”
To try to figure out what was going on, they looked at whether ephrins were reversing heart attack damage by building new blood vessels. That hypothesis didn’t hold up in experiments.
Looking more closely at the cascade of cellular signals that ramp up after injury, the scientists saw that signals promoting cell survival were soaring. But that was just one clue. They also looked at inflammation, which causes tissue damage. And they studied the role of autophagy, a sort of cellular house cleaning vital to non-renewing cells.
“We’re trying to figure out what ephrin’s doing to these cells that makes them resistant to injury,” she said.
The paper that the scientists entered into STAT Madness concluded that all three signaling pathways — cell survival, autophagy, and inflammation — protect heart cells. “Collectively, these results defy conventional beliefs regarding the fragility of … cardiomyocytes,” they wrote.
Teasing out which mechanism — or mechanisms — in ephrin’s repertoire can save heart muscle cells will occupy Virag’s lab for some time to come. She’s now looking at ways to more closely mimic in mice not only what happens when a heart attack strikes but also what ensues in the aftermath, when blood rushes back into the heart, causing even more damage.
A world of difference for genetic disease
The distance between the eyes. The flatness of the face. The length of the nose.
Doctors trained to detect specific facial patterns that appear in certain genetic syndromes will look at a child to see if any features fit what experience or textbooks have taught them to look for, but that expertise has its limits. When the criteria for discerning facial features are based on children with overwhelmingly European ancestry, important differences may be overlooked in children whose faces reflect the rest of the world.
A team led by scientists at the Children’s National Health System hopes their tool can overcome those gaps in knowledge. Working with the National Human Genome Research Institute, they’ve created facial analysis technology that includes more diverse faces, based on photos of children with various ethnicities in the U.S. and in 20 countries around the world.
“We showed that our facial analysis technology becomes more accurate if we take into consideration the ancestry of the child,“ said Marius Linguraru, a computer scientists and principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National. “The technology also provides geneticists and clinicians all around the world who read our paper a list of the most relevant and important features for diagnosing these syndromes in their populations.”
When Linguraru spoke to STAT last week, he had just come from the Congo, where he met with local hospitals and special-needs schools to discuss collaborating with Children’s National.
The tool is based on artificial intelligence and quantitative imaging, translating into algorithms what clinicians do to interpret faces. Still in the research and development stage, the technology has already illuminated how various populations — Caucasian, African, Asian, and of Latin American descent — share some facial traits but diverge in others that signal Down syndrome, Noonan syndrome, and DiGeorge syndrome.
These syndromes are often underdiagnosed, and come with a panoply of physical and intellectual problems that can be monitored, and possibly treated, with early diagnosis.
The scientists have tested it in children from around the world who have already been diagnosed with genetic testing from blood samples. Compared to those tests, the tool has correctly identified more than 96 percent of the children. Just as important, it also revealed which facial features are different in which populations.
“One of the main aims I have is to collect as much data and as much diverse data as possible to train the technology to detect genetic conditions in children from everywhere,” he said.