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Validation of suitable genes for normalization of diurnal gene expression studies in Chenopodium quinoa
Quinoa depicts high nutritional quality and abiotic stress resistance, attracting strong interest in the last years. To unravel the function of candidate genes for agronomically relevant traits, studying their transcriptional activities by RT-qPCR is an important experimental approach. The accuracy...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951847/ https://www.ncbi.nlm.nih.gov/pubmed/33705394 http://dx.doi.org/10.1371/journal.pone.0233821 |
Sumario: | Quinoa depicts high nutritional quality and abiotic stress resistance, attracting strong interest in the last years. To unravel the function of candidate genes for agronomically relevant traits, studying their transcriptional activities by RT-qPCR is an important experimental approach. The accuracy of such experiments strongly depends on precise data normalization. To date, validation of potential candidate genes for normalization of diurnal expression studies has not been performed in C. quinoa. We selected eight candidate genes based on transcriptome data and literature survey, including conventionally used reference genes. We used three statistical algorithms (BestKeeper, geNorm and NormFinder) to test their stability and added further validation by a simulation-based strategy. We demonstrated that using different reference genes, including those top ranked by stability, causes significant differences among the resulting diurnal expression patterns. Our results show that isocitrate dehydrogenase enzyme (IDH-A) and polypyrimidine tract-binding protein (PTB) are suitable genes to normalize diurnal expression data of two different quinoa accessions. Moreover, we validated our reference genes by normalizing two known diurnally regulated genes, BTC1 and BBX19. The validated reference genes obtained in this study will improve the accuracy of RT-qPCR data normalization and facilitate gene expression studies in quinoa. |
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