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Validation of reference genes for quantitative gene expression analysis in experimental epilepsy.
Chinmaya Sadangi1, Felix Rosenow1,2, Braxton A Norwood1,2,3
1Department of Neurology, Philipps University, Marburg, Germany.
Journal of Neuroscience Research
|June 3, 2017
Summary
Selecting appropriate reference genes is crucial for accurate gene expression analysis in epilepsy research. This study reveals that commonly used reference genes are often unstable, necessitating validation for reliable results in epilepsy models.
Area of Science:
- Neuroscience
- Molecular Biology
- Genetics
Background:
- Understanding gene expression changes is vital for elucidating epilepsy pathophysiology and epileptogenesis.
- Quantitative real-time polymerase chain reaction (qPCR) is a key method for measuring gene expression.
- Accurate qPCR analysis relies on the selection of stably expressed reference genes.
Purpose of the Study:
- To systematically evaluate the stability of 15 potential reference genes in rat epilepsy models.
- To identify reliable reference genes for accurate gene expression analysis during epileptogenesis and chronic epilepsy.
Main Methods:
- Assessment of 15 candidate reference genes in hippocampal tissue from male Sprague-Dawley rats.
- Analysis across different epilepsy models: two during epileptogenesis, one chronic epilepsy, and one non-injurious seizure model.
- Utilized four distinct mathematical models for reference gene stability ranking.
Main Results:
- Reference gene stability rankings differed significantly between epilepsy models and time points (epileptogenesis vs. chronic epilepsy).
- Variance was observed in rankings across the four mathematical models used.
- Novel reference genes demonstrated superior stability compared to commonly used genes in epilepsy studies.
Conclusions:
- Reference genes validated for one epilepsy model may not be suitable for others or different disease stages.
- Gene reference stability can change over time within epilepsy models.
- Systematic validation of reference genes is critically important for ensuring accurate and reliable gene expression data in epilepsy research.
