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Title: Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.
Austin Authors: Oliver, Karen L;Lukic, Vesna;Thorne, Natalie P;Berkovic, Samuel F ;Scheffer, Ingrid E ;Bahlo, Melanie
Affiliation: Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Melbourne, Victoria, Australia
Department of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
Florey Institute, Melbourne, Victoria, Australia
Epilepsy Research Center, Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
Issue Date: 9-Jul-2014
Publication information: PLoS One 2014; 9(7): e102079
Abstract: We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.
Gov't Doc #: 25014031
DOI: 10.1371/journal.pone.0102079
Type: Journal Article
Appears in Collections:Journal articles

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