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Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions.

ABSTRACT: Quantitative real-time reverse transcription polymerase chain reaction (qPCR), a sensitive technique for gene expression analysis, depends on the stability of the reference genes used for data normalization. Caragana intermedia, a native desert shrub with strong drought-resistance, sand-fixing capacity and high forage value that is widespread in the desert land of west and northwest China, has not been investigated regarding the identification of reference genes suitable for the normalization of qPCR data. In this study, 10 candidate reference genes were analyzed in C. intermedia subjected to different abiotic (osmotic, salt, cold and heat) stresses, in two distinct plant organs (roots and leaves). The expression stability of these genes was assessed using geNorm, NormFinder and BestKeeper algorithms. The best-ranked reference genes differed across the different sets of samples, but UNK2, PP2A and SAND were the most stable across all tested samples. UNK2 and SAND would be appropriate for normalizing gene expression data for salt-treated roots, whereas the combination of UNK2, SAND and EF-1? would be appropriate for salt-treated leaves. UNK1, UNK2 and PP2A would be appropriate for PEG-treated (osmotic) roots, whereas the combination of TIP41 and PP2A was the most suitable for PEG-treated leaves. SAND, PP2A and TIP41 exhibited the most stable expression in heat-treated leaves. In cold-treated leaves, SAND and EF-1? were the most stably expressed. To further validate the suitability of the reference genes identified in this study, the expression levels of DREB1 and DREB2 (homologs of AtDREB1 and AtDREB2) were studied in parallel. This study is the first systematic analysis for the selection of superior reference genes for qPCR in C. intermedia under different abiotic stress conditions, and will benefit future studies on gene expression in C. intermedia and other species of the leguminous genus Caragana.

SUBMITTER: Zhu J

PROVIDER: S-EPMC3534648 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions.

Zhu Jianfeng J Zhang Lifeng L Li Wanfeng W Han Suying S Yang Wenhua W Qi Liwang L

PloS one 20130102 1

Quantitative real-time reverse transcription polymerase chain reaction (qPCR), a sensitive technique for gene expression analysis, depends on the stability of the reference genes used for data normalization. Caragana intermedia, a native desert shrub with strong drought-resistance, sand-fixing capacity and high forage value that is widespread in the desert land of west and northwest China, has not been investigated regarding the identification of reference genes suitable for the normalization of ...[more]

PMID: 23301042

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Project description:Quantitative real-time polymerase chain reaction (RT-qPCR) using a stable reference gene is widely used for gene expression research. Barnyard millet (Echinochloa spp.) is an ancient crop in Asia and Africa that is widely cultivated for food and fodder. It thrives well under drought, salinity, cold, and heat environmental conditions, besides adapting to any soil type. To date, there are no gene expression studies performed to identify the potential candidate gene responsible for stress response in barnyard millet, due to lack of reference gene. Here, 10 candidate reference genes, Actin (ACT), α-tubulin (α-TUB), β-tubulin (β-TUB), RNA pol II (RP II), elongation factor-1 alpha (EF-1α), adenine phosphoribosyltransferase (APRT), TATA-binding protein-like factor (TLF), ubiquitin-conjugating enzyme 2 (UBC2), ubiquitin-conjugating enzyme E2L5 (UBC5) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), were selected from mRNA sequences of E. crus-galli and E. colona var frumentacea. Five statistical algorithms (geNorm, NormFinder, BestKeeper, ΔCt, and RefFinder) were applied to determine the expression stabilities of these genes in barnyard millet grown under four different abiotic stress (drought, salinity, cold and heat) exposed at different time points. The UBC5 and ɑ-TUB in drought, GAPDH in salinity, GAPDH and APRT in cold, and EF-1α and RP II in heat were the most stable reference genes, whereas ß-TUB was the least stable irrespective of stress conditions applied. Further Vn/Vn + 1 analysis revealed two reference genes were sufficient to normalize gene expression across all sample sets. The suitability of identified reference genes was validated with Cu-ZnSOD (SOD1) in the plants exposed to different abiotic stress conditions. The results revealed that the relative quantification of the SOD1 gene varied according to reference genes and the number of reference genes used, thus highlighting the importance of the choice of a reference gene in such experiments. This study provides a foundational framework for standardizing RT-qPCR analyses, enabling accurate gene expression profiling in barnyard millet.

Project description:BackgroundNormalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.ResultsWe propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.ConclusionsThe proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.

Dataset Information

Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions.

Publications

Reference gene selection for quantitative real-time PCR normalization in Caragana intermedia under different abiotic stress conditions.

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