Time- and Region-Specific Selection of Reference Genes in the Rat Brain in the Lithium-Pilocarpine Model of Acquired Temporal Lobe Epilepsy.
Alexander P SchwarzMaria V ZakharovaAnna A KovalenkoAlexandra V DyominaOlga E ZubarevaAleksey V ZaitsevPublished in: Biomedicines (2024)
Reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR) is a commonly used tool for gene expression analysis. The selection of stably expressed reference genes is required for accurate normalization. The aim of this study was to identify the optimal reference genes for RT-qPCR normalization in various brain regions of rats at different stages of the lithium-pilocarpine model of acquired epilepsy. We tested the expression stability of nine housekeeping genes commonly used as reference genes in brain research: Actb , Gapdh , B2m , Rpl13a , Sdha , Ppia , Hprt1 , Pgk1 , and Ywhaz . Based on four standard algorithms (geNorm, NormFinder, BestKeeper, and comparative delta-Ct), we found that after pilocarpine-induced status epilepticus, the stability of the tested reference genes varied significantly between brain regions and depended on time after epileptogenesis induction (3 and 7 days in the latent phase, and 2 months in the chronic phase of the model). Pgk1 and Ywhaz were the most stable, while Actb , Sdha , and B2m demonstrated the lowest stability in the analyzed brain areas. We revealed time- and region-specific changes in the mRNA expression of the housekeeping genes B2m , Actb , Sdha , Rpl13a , Gapdh , Hprt1 , and Sdha. These changes were more pronounced in the hippocampal region during the latent phase of the model and are thought to be related to epileptogenesis. Thus, RT-qPCR analysis of mRNA expression in acquired epilepsy models requires careful selection of reference genes depending on the brain region and time of analysis. For the time course study of epileptogenesis in the rat lithium-pilocarpine model, we recommend the use of the Pgk1 and Ywhaz genes.
Keyphrases
- genome wide identification
- genome wide
- bioinformatics analysis
- temporal lobe epilepsy
- transcription factor
- genome wide analysis
- white matter
- resting state
- magnetic resonance imaging
- computed tomography
- dna methylation
- deep learning
- copy number
- long non coding rna
- solid state
- brain injury
- high glucose
- binding protein
- image quality