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Identification of condition-specific reference genes from microarray data for locusts exposed to hypobaric hypoxia

Real-time quantitative polymerase chain reaction (qPCR) is a routine and robust approach for measuring gene expression. The stability of reference genes in qPCR is crucial for the accurate quantification of gene expression. To provide reliable reference genes for studying the transcriptional respons...

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Detalles Bibliográficos
Autores principales: Zhao, De-Jian, Guo, Kun, Kang, Le
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3642161/
https://www.ncbi.nlm.nih.gov/pubmed/23650605
http://dx.doi.org/10.1016/j.fob.2012.08.001
Descripción
Sumario:Real-time quantitative polymerase chain reaction (qPCR) is a routine and robust approach for measuring gene expression. The stability of reference genes in qPCR is crucial for the accurate quantification of gene expression. To provide reliable reference genes for studying the transcriptional responses of locust muscles to hypobaric hypoxia, we first examined the gene expression stability of the frequently used housekeeping genes 18S, GAPDH, and β-actin. However, the expression of these three housekeeping genes was influenced by hypobaric hypoxia. Consequently, we identified five novel candidate reference genes from the locust microarray data. The gene expression stability of the five candidates, together with the three classical housekeeping genes, were evaluated using two distinct algorithms implemented in geNorm and NormFinder. GeNorm identified Ach (acetyl-CoA hydrolase) and Pgp (phosphoglycolate phosphatase-like) as the most stable genes and NormFinder further distinguished Ach as the most stable one. The validity of Ach as a reference gene was confirmed through comparison with 18S. This study exemplifies the necessity of validating reference genes before their application and the feasibility of identifying condition-specific reference genes from large-scale gene expression data.