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


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          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.

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          Interaction of 14-3-3 with signaling proteins is mediated by the recognition of phosphoserine.

          The highly conserved and ubiquitously expressed 14-3-3 family of proteins bind to a variety of proteins involved in signal transduction and cell cycle regulation. The nature and specificity of 14-3-3 binding is, however, not known. Here we show that 14-3-3 is a specific phosphoserine-binding protein. Using a panel of phosphorylated peptides based on Raf-1, we have defined the 14-3-3 binding motif and show that most of the known 14-3-3 binding proteins contain the motif. Peptides containing the motif could disrupt 14-3-3 complexes and inhibit maturation of Xenopus laevis oocytes. These results suggest that the interactions of 14-3-3 with signaling proteins are critical for the activation of signaling proteins. Our findings also suggest novel roles for serine/threonine phosphorylation in the assembly of protein-protein complexes.
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            Predicting disease genes using protein-protein interactions.

            The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes. To investigate whether protein-protein interactions can predict genes for genetically heterogeneous diseases. 72,940 protein-protein interactions between 10,894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes. Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci. Exploiting protein-protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.
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              Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes.

              Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.

                Author and article information

                Biomed Res Int
                Biomed Res Int
                BioMed Research International
                Hindawi Publishing Corporation
                1 February 2017
                : 2017
                : 6132436
                1Department of Outpatient, China-Japan Union Hospital of Jilin University, Changchun 130033, China
                2Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
                3Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou 510507, China
                4Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
                5School of Life Sciences, Shanghai University, Shanghai 200444, China
                Author notes

                Academic Editor: Ansgar Poetsch

                Author information
                Copyright © 2017 Wei Guo et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 23 October 2016
                : 15 January 2017
                Funded by: Norman Bethune Program of Jilin University
                Award ID: 2015218
                Funded by: Science and Technology Department of Jilin Province
                Award ID: 20160414007GH
                Award ID: 20160414047GH
                Funded by: Education Department of Jilin Province
                Award ID: 2015509
                Award ID: 2016449
                Funded by: Development and Reform Commission of Jilin Province
                Award ID: 2015Y032
                Research Article


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