4.5 Article

Systems Pharmacology of Arrhythmias

Journal

SCIENCE SIGNALING
Volume 3, Issue 118, Pages -

Publisher

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/scisignal.2000723

Keywords

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Funding

  1. NCRR NIH HHS [UL1 RR029887-01, UL1RR029887] Funding Source: Medline
  2. NIDDK NIH HHS [DK038761, R01 DK038761-10] Funding Source: Medline
  3. NIGMS NIH HHS [P50 GM071558-020007, P50 GM071558-04, GM062754, P50GM07558, P50 GM071558-030007, P50 GM071558-01A20007, T32 GM062754-01, P50 GM071558-01A2] Funding Source: Medline

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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.

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