A computational systems biology approach identified a distinct functional neighborhood for Long QT syndrome, enabling the prediction of new gene variants and adverse drug effects.
A computational systems biology approach mapping the LQTS functional neighborhood can predict novel genetic variants and identify drugs with a high risk of adverse QT effects.
Long QT syndrome (LQTS) is a congenital or drug-induced change in electrical activity of the heart that can lead to fatal arrhythmias. Mutations in 12 genes encoding ion channels and associated proteins are linked with congenital LQTS. With a computational systems biology approach, we found that gene products involved in LQTS formed a distinct functional neighborhood within the human interactome. Other diseases form similarly selective neighborhoods, and comparison of the LQTS neighborhood with other disease-centered neighborhoods suggested a molecular basis for associations between seemingly unrelated diseases that have increased risk of cardiac complications. By combining the LQTS neighborhood with published genome-wide association study data, we identified previously unknown single-nucleotide polymorphisms likely to affect the QT interval. We found that targets of U.S. Food and Drug Administration (FDA)-approved drugs that cause LQTS as an adverse event were enriched in the LQTS neighborhood. With the LQTS neighborhood as a classifier, we predicted drugs likely to have risks for QT effects and we validated these predictions with the FDA's Adverse Events Reporting System, illustrating how network analysis can enhance the detection of adverse drug effects associated with drugs in clinical use. Thus, the identification of disease-selective neighborhoods within the human interactome can be useful for predicting new gene variants involved in disease, explaining the complexity underlying adverse drug side effects, and predicting adverse event susceptibility for new drugs.
Berger et al. (Tue,) conducted a other in Long QT syndrome (LQTS). Computational systems biology approach was evaluated on Identification of disease-selective neighborhoods and prediction of drugs with QT effect risks. A computational systems biology approach identified a distinct functional neighborhood for Long QT syndrome, enabling the prediction of new gene variants and adverse drug effects.
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