Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes

PLoS One. 2014 Jul 9;9(7):e102079. doi: 10.1371/journal.pone.0102079. eCollection 2014.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Biomarkers / metabolism*
  • Computational Biology*
  • Databases, Genetic*
  • Epilepsy / genetics*
  • Gene Expression Profiling*
  • Gene Expression Regulation*
  • Gene Regulatory Networks*
  • Humans
  • Oligonucleotide Array Sequence Analysis

Substances

  • Biomarkers

Grants and funding

MB, NT and VL were supported by Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS funding to their institute. MB was also supported by an Australian Research Council Future Fellowship (FT100100764). SFB and IES were supported by a National Health and Medical Research Council of Australia Program Grant 628952 and IES was supported by Practitioner Fellowship 1006110. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.