Algorithm helps sort genetic data to improve our understanding of congenital heart disease

Researchers at Gladstone Institutes developed a new algorithm method for identifying genetic variant combinations that are likely to play important roles in congenital heart disease. This opens up opportunities to accelerate research into this serious condition
     
Nearly 1 percent of all children are born with congenital heart disease. This includes a range of potentially life-threatening problems with the structure and function of their hearts. For most children, the precise causes of these frightening defects are unknown, but new genetic research hopes to open the door for better understanding and risk assessments for congenital heart defects.

The paper, "Transcription Factor Protein Interactomes Reveal Genetic Determinants in Heart Disease," was published by the journal Cell in February.[1]

The culprits appear to be abnormal variants of genes that are involved in the formation of the heart in the womb. But much remains to be learned about exactly which genes contribute to congenital heart disease and how they interact with each other.

Researchers at Gladstone Institutes, an independent state-of-the-art biomedical research institution, say this opens up opportunities to accelerate research into this serious condition. The study was led by Gladstone President and Senior Investigator Deepak Srivastava, MD, and Katie Pollard, PhD, director of the Gladstone Institute of Data Science and Biotechnology.

The new strategy, which combines techniques from genetics, computational biology, stem cell biology and proteomics, could also be applied to study numerous other diseases with complex genetic causes.

"Previous methods have generated long lists of variants detected in patients, but many actually turned out to be inconsequential, so a major challenge in the field has been identifying which variants are most important," explained Srivastava, who is also a pediatric cardiologist and a professor in the Department of Pediatrics at UC San Francisco (UCSF). "Our approach pinpoints variants that are most likely to be involved in disease, allowing us to focus on those variants, deepen understanding of the underlying biology of the disease, and, we hope, move more rapidly toward new treatments."

Leveraging interactions between proteins to better understand congenital heart disease

Rather than looking at variants in isolation, the novel strategy considers the interactions between proteins to zero in on which might variants be causing disease—in this case, congenital heart disease.

The proteins GATA4 and TBX5 were already known to be required for healthy human heart formation, and to collaborate with a network of additional proteins to help grow a heart. Mutations in the other proteins in the network could, in theory, contribute to heart malformation.

To identify these potential culprit genes, the researchers carefully mapped out the entire network of interactions between the GATA4 and TBX5 proteins using precursor heart cells grown from human induced pluripotent stem cells. Next, they cross-referenced this 273-protein network with DNA sequencing data from over 3,000 children with congenital heart disease and their parents, developed by a National Institutes of Health–funded consortium.

Several dozen variants in the children's sequencing data matched specific proteins also found in the GATA4-TBX5 network, far more than expected, pinpointing them as candidates that may contribute to congenital heart disease.

"We first identified important protein networks in the types of cells affected in congenital heart disease, and then integrated large-scale, protein-coding sequencing data," said Bárbara González Terán, PhD, lead author of the study and a postdoctoral scholar in Srivastava's lab. "Many scientists had speculated this approach was possible, but to our knowledge, this is the first time it has actually been done, for any disease."