Dataset Information

Comprehensive selection of reference genes for gene expression normalization in sugarcane by real time quantitative rt-PCR.

ABSTRACT: The increasingly used real time quantitative reverse transcription-PCR (qRT-PCR) method for gene expression analysis requires one or several reference gene(s) acting as normalization factor(s). In order to facilitate gene expression studies in sugarcane (Saccharum officinarum), a non-model plant with limited genome information, the stability of 13 candidate reference genes was evaluated. The geNorm, NormFinder and deltaCt methods were used for selecting stably expressed internal controls across different tissues and under various experimental treatments. These results revealed that, among these 13 candidate reference genes, GAPDH, eEF-1a and eIF-4? were the most stable and suitable for use as normalization factors across all various experimental samples. In addition, APRT could be a candidate for examining the relationship between gene copy number and transcript levels in sugarcane tissue samples. According to the results evaluated by geNorm, combining CUL and eEF-1? in hormone treatment experiments; CAC and CUL in abiotic stress tests; GAPDH, eEF-1a and CUL in all treatment samples plus CAC, CUL, APRT and TIPS-41 in cultivar tissues as groups for normalization would lead to more accurate and reliable expression quantification in sugarcane. This is the first systematic validation of reference genes for quantification of transcript expression profiles in sugarcane. This study should provide useful information for selecting reference genes for more accurate quantification of gene expression in sugarcane and other plant species.

SUBMITTER: Ling H

PROVIDER: S-EPMC4019594 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Comprehensive selection of reference genes for gene expression normalization in sugarcane by real time quantitative rt-PCR.

Ling Hui H Wu Qibin Q Guo Jinlong J Xu Liping L Que Youxiong Y

PloS one 20140513 5

The increasingly used real time quantitative reverse transcription-PCR (qRT-PCR) method for gene expression analysis requires one or several reference gene(s) acting as normalization factor(s). In order to facilitate gene expression studies in sugarcane (Saccharum officinarum), a non-model plant with limited genome information, the stability of 13 candidate reference genes was evaluated. The geNorm, NormFinder and deltaCt methods were used for selecting stably expressed internal controls across ...[more]

PMID: 24823940

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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.

Project description:BackgroundReal-time quantitative reverse transcription PCR (RT-qPCR) data needs to be normalized for its proper interpretation. Housekeeping genes are routinely employed for this purpose, but their expression level cannot be assumed to remain constant under all possible experimental conditions. Thus, a systematic validation of reference genes is required to ensure proper normalization. For soybean, only a small number of validated reference genes are available to date.ResultsA systematic comparison of 14 potential reference genes for soybean is presented. These included seven commonly used (ACT2, ACT11, TUB4, TUA5, CYP, UBQ10, EF1b) and seven new candidates (SKIP16, MTP, PEPKR1, HDC, TIP41, UKN1, UKN2). Expression stability was examined by RT-qPCR across 116 biological samples, representing tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars. Expression of all 14 genes was variable to some extent, but that of SKIP16, UKN1 and UKN2 was overall the most stable. A combination of ACT11, UKN1 and UKN2 would be appropriate as a reference panel for normalizing gene expression data among different tissues, whereas the combination SKIP16, UKN1 and MTP was most suitable for developmental stages. ACT11, TUA5 and TIP41 were the most stably expressed when the photoperiod was altered, and TIP41, UKN1 and UKN2 when the light quality was changed. For six different cultivars in long day (LD) and short day (SD), their expression stability did not vary significantly with ACT11, UKN2 and TUB4 being the most stable genes. The relative gene expression level of GmFTL3, an ortholog of Arabidopsis FT (FLOWERING LOCUS T) was detected to validate the reference genes selected in this study.ConclusionNone of the candidate reference genes was uniformly expressed across all experimental conditions, and the most suitable reference genes are conditional-, tissue-specific-, developmental-, and cultivar-dependent. Most of the new reference genes performed better than the conventional housekeeping genes. These results should guide the selection of reference genes for gene expression studies in soybean.

Dataset Information

Comprehensive selection of reference genes for gene expression normalization in sugarcane by real time quantitative rt-PCR.

Publications

Comprehensive selection of reference genes for gene expression normalization in sugarcane by real time quantitative rt-PCR.

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