Dataset Information

Genome-Wide Identification and Evaluation of Reference Genes for Quantitative RT-PCR Analysis during Tomato Fruit Development.

ABSTRACT: Gene expression analysis in tomato fruit has drawn increasing attention nowadays. Quantitative real-time PCR (qPCR) is a routine technique for gene expression analysis. In qPCR operation, reliability of results largely depends on the choice of appropriate reference genes (RGs). Although tomato is a model for fruit biology study, few RGs for qPCR analysis in tomato fruit had yet been developed. In this study, we initially identified 38 most stably expressed genes based on tomato transcriptome data set, and their expression stabilities were further determined in a set of tomato fruit samples of four different fruit developmental stages (Immature, mature green, breaker, mature red) using qPCR analysis. Two statistical algorithms, geNorm and Normfinder, concordantly determined the superiority of these identified putative RGs. Notably, SlFRG05 (Solyc01g104170), SlFRG12 (Solyc04g009770), SlFRG16 (Solyc10g081190), SlFRG27 (Solyc06g007510), and SlFRG37 (Solyc11g005330) were proved to be suitable RGs for tomato fruit development study. Further analysis using geNorm indicate that the combined use of SlFRG03 (Solyc02g063070) and SlFRG27 would provide more reliable normalization results in qPCR experiments. The identified RGs in this study will be beneficial for future qPCR analysis of tomato fruit developmental study, as well as for the potential identification of optimal normalization controls in other plant species.

SUBMITTER: Cheng Y

PROVIDER: S-EPMC5581943 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Genome-Wide Identification and Evaluation of Reference Genes for Quantitative RT-PCR Analysis during Tomato Fruit Development.

Cheng Yuan Y Bian Wuying W Pang Xin X Yu Jiahong J Ahammed Golam J GJ Zhou Guozhi G Wang Rongqing R Ruan Meiying M Li Zhimiao Z Ye Qingjing Q Yao Zhuping Z Yang Yuejian Y Wan Hongjian H

Frontiers in plant science 20170829

Gene expression analysis in tomato fruit has drawn increasing attention nowadays. Quantitative real-time PCR (qPCR) is a routine technique for gene expression analysis. In qPCR operation, reliability of results largely depends on the choice of appropriate reference genes (RGs). Although tomato is a model for fruit biology study, few RGs for qPCR analysis in tomato fruit had yet been developed. In this study, we initially identified 38 most stably expressed genes based on tomato transcriptome dat ...[more]

PMID: 28900431

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Project description:BACKGROUND: Usually the reference genes used in gene expression analysis have been chosen for their known or suspected housekeeping roles, however the variation observed in most of them hinders their effective use. The assessed lack of validated reference genes emphasizes the importance of a systematic study for their identification. For selecting candidate reference genes we have developed a simple in silico method based on the data publicly available in the wheat databases Unigene and TIGR. RESULTS: The expression stability of 32 genes was assessed by qRT-PCR using a set of cDNAs from 24 different plant samples, which included different tissues, developmental stages and temperature stresses. The selected sequences included 12 well-known HKGs representing different functional classes and 20 genes novel with reference to the normalization issue. The expression stability of the 32 candidate genes was tested by the computer programs geNorm and NormFinder using five different data-sets. Some discrepancies were detected in the ranking of the candidate reference genes, but there was substantial agreement between the groups of genes with the most and least stable expression. Three new identified reference genes appear more effective than the well-known and frequently used HKGs to normalize gene expression in wheat. Finally, the expression study of a gene encoding a PDI-like protein showed that its correct evaluation relies on the adoption of suitable normalization genes and can be negatively affected by the use of traditional HKGs with unstable expression, such as actin and alpha-tubulin. CONCLUSION: The present research represents the first wide screening aimed to the identification of reference genes and of the corresponding primer pairs specifically designed for gene expression studies in wheat, in particular for qRT-PCR analyses. Several of the new identified reference genes outperformed the traditional HKGs in terms of expression stability under all the tested conditions. The new reference genes will enable more accurate normalization and quantification of gene expression in wheat and will be helpful for designing primer pairs targeting orthologous genes in other plant species.

Project description:BackgroundWith the availability of genome sequences, gene expression analysis of jute has drawn considerable attention for understanding the regulatory mechanisms of fiber development and improving fiber quality. Gene expression profiles of a target gene can provide valuable clues towards the understanding of its biological function. Reverse transcription quantitative real-time PCR (qRT-PCR) is the best method for targeted gene expression analysis due to its sensitivity and reproducibility. However, calculating relative expression requires reference genes, which must be stable across various biological conditions. For this purposes, 11 prospective genes namely, 28S RNA, ACT7, CYP, EF1A, EF2, ETIF3E, GAPDH, PP2Ac, PTB, UBC2 and UBI1 were evaluated for their potential use as reference genes in jute.ResultsThe expression stabilities of eleven prospective genes were analyzed in various jute plant tissues, such as the root, stick, bark, leaf, flower, seed and fiber, as well as under abiotic (waterlogged, drought and salinity) and biotic stress (infestation with Macrophomina phaseolina) conditions with different time points. All 11 genes were variably expressed in different tissues and stress conditions. To find suitable reference genes in different sample sets, a comprehensive approach based on four statistical algorithms such as GeNorm, BestKeeper, NormFinder the ?Ct was used. The PP2Ac and EF2 genes were the most stably expressed across the different tissues. ACT7 and UBC2 were suitable reference genes under drought stress, and CYP and PP2Ac were the most appropriate after inoculation with Macrophomina phaseolina. Under salinity stress, PP2Ac and UBC2 were the best genes, and ACT7 and PP2Ac were the most suitable under waterlogged conditions.ConclusionExpression stability of reference genes from jute varied in different tissues and selected experimental conditions. Our results provide a valuable resource for the accurate normalization of gene expression experiments in fiber research for important bast fiber crops.

Project description:BackgroundNormalizing through reference genes, or housekeeping genes, can make more accurate and reliable results from reverse transcription real-time quantitative polymerase chain reaction (qPCR). Recent studies have shown that no single housekeeping gene is universal for all experiments. Thus, suitable reference genes should be the first step of any qPCR analysis. Only a few studies on the identification of housekeeping gene have been carried on plants. Therefore qPCR studies on important crops such as cotton has been hampered by the lack of suitable reference genes.ResultsBy the use of two distinct algorithms, implemented by geNorm and NormFinder, we have assessed the gene expression of nine candidate reference genes in cotton: GhACT4, GhEF1alpha5, GhFBX6, GhPP2A1, GhMZA, GhPTB, GhGAPC2, GhbetaTUB3 and GhUBQ14. The candidate reference genes were evaluated in 23 experimental samples consisting of six distinct plant organs, eight stages of flower development, four stages of fruit development and in flower verticils. The expression of GhPP2A1 and GhUBQ14 genes were the most stable across all samples and also when distinct plants organs are examined. GhACT4 and GhUBQ14 present more stable expression during flower development, GhACT4 and GhFBX6 in the floral verticils and GhMZA and GhPTB during fruit development. Our analysis provided the most suitable combination of reference genes for each experimental set tested as internal control for reliable qPCR data normalization. In addition, to illustrate the use of cotton reference genes we checked the expression of two cotton MADS-box genes in distinct plant and floral organs and also during flower development.ConclusionWe have tested the expression stabilities of nine candidate genes in a set of 23 tissue samples from cotton plants divided into five different experimental sets. As a result of this evaluation, we recommend the use of GhUBQ14 and GhPP2A1 housekeeping genes as superior references for normalization of gene expression measures in different cotton plant organs; GhACT4 and GhUBQ14 for flower development, GhACT4 and GhFBX6 for the floral organs and GhMZA and GhPTB for fruit development. We also provide the primer sequences whose performance in qPCR experiments is demonstrated. These genes will enable more accurate and reliable normalization of qPCR results for gene expression studies in this important crop, the major source of natural fiber and also an important source of edible oil. The use of bona fide reference genes allowed a detailed and accurate characterization of the temporal and spatial expression pattern of two MADS-box genes in cotton.

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

Genome-Wide Identification and Evaluation of Reference Genes for Quantitative RT-PCR Analysis during Tomato Fruit Development.

Publications

Genome-Wide Identification and Evaluation of Reference Genes for Quantitative RT-PCR Analysis during Tomato Fruit Development.

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