Identifying and Analyzing Novel Epilepsy-Related Genes Using Random Walk with Restart Algorithm

Abstract

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases.

Cystatin B is essential for proliferation and interneuron migration in individuals with EPM 1 epilepsy

“…It is also interesting to outline that almost half of the proteins that are differentially expressed in EPM1‐derived organoids and enriched in radial glial cells are involved in different types of human epilepsies. For instance, Collagen COL11A1 mutated in the Stickler syndrome, caused by mutations in COL2A1 or COL11A1, is associated with epileptic phenotypes (Savasta et al , ; Zhou et al , ); IFITM3 (interferon‐induced transmembrane protein 3) has been identified among genes that are target of epilepsy‐associated miRNA in animal models and human tissues (Cava et al , ); heterozygous mutation in GLUD1 gene has been reported in patients affected by hyperinsulinism, commonly associated with epileptic seizures (Aka et al , ); RRAS (Guo et al , ) and MGST1 (Shang et al , ; Ercegovac et al , ) are associated with epilepsy risk.…”

Multiplex gene and phenotype network to characterize shared genetic pathways of epilepsy and autism

“…To validate the novel predictions, we performed literature review to identify evidence supporting the potential role of these genes in epilepsy. ANK2 , which encodes for the ankyrin-B protein, a member of the ankyrins family, was predicted as a novel epilepsy-related gene by a recent network-based study using a random walk with restart algorithm 32 . A recent study showed that an ANK2 variant is associated with seizure possibly through its interactions with the voltage-gated Ca V 2.1 calcium channel 33 .…”

INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species

“…Given a target species, a set of proteins of the target species, and additional species to be compared (the top panel of Figure 1 ), INTERSPIA searches for additional proteins associated with the input proteins using the STRING database ( 5 ) by the random walk with restart algorithm ( 27 , 28 ), and the interactions among extended proteins in other species are also identified using the STRING and orthoDB ( 29 ) database (middle panel of Figure 1 ). Finally, the dynamics of the PPIs among multiple species are visualized in an easy-to-use web interface with several features (bottom panel of Figure 1 ).…”

“…Given user input data, the following three main steps are performed by the server-side pipeline of INTERSPIA (middle panel in Figure 1 ). First, additional proteins who have a potential for functional association with the input proteins are discovered by the random walk with restart (RWR) algorithm ( 27 , 28 ) using the PPI information of the target species in the STRING database. Starting from input proteins, the RWR algorithm randomly explores a given protein interaction network of a target species by moving to a neighbouring protein or going back to the input proteins based on a given restarting probability at each step of exploration.…”