Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 20, 2010
Publication Date: January 11, 2011
Repository URL: http://hdl.handle.net/10113/47566
Citation: Toutges, M.J., Hartzer, K.L., Lord, J.C., Oppert, B.S. 2011. Evaluation of reference genes for quantitative polymerase chain reaction across life-cycle stages and tissue types of Tribolium castaneum. Journal of Agricultural and Food Chemistry. 58(16):8948-8951. Interpretive Summary: The ability to compare RNA levels among experimental treatments is employed in many areas of agricultural research including gene expression studies. We evaluated the appropriateness of specific genes for their use as controls (normalizers) in gene expression studies by evaluating nine potential normalizers across both developmental stages and tissue types of insects. We found several normalize, genes that are suitable for broad scale analysis of gene expression, and we also demonstrated that it is essential to validate normalizers for the experimental conditions tested and also for the specific instruments and/or methods employed in a study.
Technical Abstract: The genome of the genetic model for coleopteran insects, Tribolium castaneum, is now available for downstream applications. To facilitate gene expression studies in T. castaneum, genes were evaluated for suitability in comparisons across tissues and/or developmental stages. In less diverse samples, such as comparisons within developmental stages or tissue only, normalizers for mRNA were more stable and consistent. Overall, the genes for ribosomal proteins rps6, rpl13a, rps3, and rps18 were the most stable normalizers for broad scale gene expression analysis in T. castaneum gene expression. However their stability ranking was dependent upon the instrument as well as the analysis program. These data emphasize the need to optimize normalizers used in all real time PCR experiments specifically for the experimental conditions and thermocycler, and to carefully evaluate data generated by computational algorithms.