Systems Pharmacology of Arrhythmias

Abstract

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 singlenucleotide 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.

Physiological genomics identifies genetic modifiers of long QT syndrome type 2 severity

“…REM2 encodes a member of the Ras superfamily, which are well-known modulators of voltage-gated calcium channels. The framework for the identification of this variant was originally suggested by a computational network analysis of novel genes or "nodes" related to known congenital LQTS genes (22). In this previous analysis, a member of the Ras superfamily known as REM was identified as a candidate gene in what was termed the LQTS neighborhood.…”

“…Special attention was paid to variants in cardiac-expressed ion channel genes. Next, we filtered for genes identified either by genome-wide association studies (GWAS) seeking modulators of QT interval duration (18)(19)(20)(21) or by the computational network analysis approach used by Berger and colleagues (22) that grouped genes and nodes into a repository known as the LQTS neighborhood (Supplemental Figure 2). Implementing this approach, we identified 12 SNPs and 4 insertion/deletions that could potentially contribute to genotype-phenotype discordance (Supplemental Table 5) but specifically pursued 2 gene variants: one found in the mildly affected mutation-positive cohort and one found in the severely affected mutation-positive subjects.…”

Systems‐based understanding of pharmacological responses with combinations of multidisciplinary methodologies

“…Another example was reported by Berger et al, who used graph theory to identify the molecular pathways involved in changes in the duration of cardiac action potentials, which cause long-QT syndrome (LQTS) [43]. They built a pathway map showing the global protein-protein interaction network and, by extracting LQTS-related proteins using graph theory, they prepared a sub-network map.…”

“…They built a pathway map showing the global protein-protein interaction network and, by extracting LQTS-related proteins using graph theory, they prepared a sub-network map. This sub-network included proteins that bind drugs such as amitriptyline, orphenadrine, loperamide and dasatinib [43]. Although some of these drugs were not expected to prolong the QT interval, the possibility that they may cause LQTS has been suggested by clinical case reports submitted to the adverse events reporting system within the Food and Drug Administration.…”

Taming Rare Variation With Known Biology in Long QT Syndrome

“…The disadvantage of this approach is that if the causative variant resides in a gene from a completely novel pathway with no previous suspicion of involvement, then it would likely be filtered out. CACNA1C was fortunately included in the list of 1629 genes from the LQTS interactome, 12 which was used in the next filtering step. It is notable, however, that this list was generated from a protein-protein interaction network based on the set of genes that cause LQT1-12, which includes CACNA1C.…”