Evaluation of Reference Genes for Quantitative Polymerase Chain Reaction across Life Cycle Stages and Tissue Types of Tribolium castaneum

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 as normalizers in comparisons across tissues and/or developmental stages. In less di...

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Bibliographic Details
Published in:Journal of agricultural and food chemistry 2010-08, Vol.58 (16), p.8948-8951
Main Authors: Toutges, Michelle J, Hartzer, Kris, Lord, Jeffrey, Oppert, Brenda
Format: Article
Language:English
Subjects:
algorithms
Analytical Methods
animal tissues
Animals
Base Sequence
Biological and medical sciences
DNA Primers
Food industries
Fundamental and applied biological sciences. Psychology
gene expression
Genes, Insect
genome
laboratory equipment
life cycle (organisms)
Life Cycle Stages
messenger RNA
normalizer genes
optimization
polymerase chain reaction
Polymerase Chain Reaction - methods
quantitative genetics
reference genes
reference standards
ribosomal proteins
storage insects
Tribolium - genetics
Tribolium - physiology
Tribolium castaneum
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Summary: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 as normalizers 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. 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 polymerase chain reaction experiments specifically for the experimental conditions and thermocycler and to carefully evaluate data generated by computational algorithms.
ISSN:0021-8561
1520-5118
DOI:10.1021/jf101603j