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Validation of reference genes for quantitative gene expression analysis in experimental epilepsy
Author(s) -
Sadangi Chinmaya,
Rosenow Felix,
Norwood Braxton A.
Publication year - 2017
Publication title -
journal of neuroscience research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.72
H-Index - 160
eISSN - 1097-4547
pISSN - 0360-4012
DOI - 10.1002/jnr.24089
Subject(s) - epileptogenesis , reference genes , epilepsy , gene , computational biology , gene expression , biology , neuroscience , bioinformatics , genetics
To grasp the molecular mechanisms and pathophysiology underlying epilepsy development (epileptogenesis) and epilepsy itself, it is important to understand the gene expression changes that occur during these phases. Quantitative real‐time polymerase chain reaction (qPCR) is a technique that rapidly and accurately determines gene expression changes. It is crucial, however, that stable reference genes are selected for each experimental condition to ensure that accurate values are obtained for genes of interest. If reference genes are unstably expressed, this can lead to inaccurate data and erroneous conclusions. To date, epilepsy studies have used mostly single, nonvalidated reference genes. This is the first study to systematically evaluate reference genes in male Sprague–Dawley rat models of epilepsy. We assessed 15 potential reference genes in hippocampal tissue obtained from 2 different models during epileptogenesis, 1 model during chronic epilepsy, and a model of noninjurious seizures. Reference gene ranking varied between models and also differed between epileptogenesis and chronic epilepsy time points. There was also some variance between the four mathematical models used to rank reference genes. Notably, we found novel reference genes to be more stably expressed than those most often used in experimental epilepsy studies. The consequence of these findings is that reference genes suitable for one epilepsy model may not be appropriate for others and that reference genes can change over time. It is, therefore, critically important to validate potential reference genes before using them as normalizing factors in expression analysis in order to ensure accurate, valid results.

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