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MicroRNAs as reference genes for quantitative PCR in cotton.

ABSTRACT: Cotton (Gossypium hirsutum) is the most important non-food plant in the world. Studies concerning the fiber quality and plant fitness of cotton at molecular level depend on high sensitive and reproducible gene-expression assays. However, only a few reports have described genes suitable for normalizing gene expression data. In this study, we report for the first time that microRNAs (miRNAs) are reliable reference genes (RGs) for cotton gene expression normalization in quantitative real-time reverse transcription (RT)-PCR. The stability of cotton miRNAs was assayed in root, stem, leaf and flower samples from three different cultivars [FiberMax (FM966), Delta Opal (DO) and Cedro] and under conditions of biotic stress caused by infection with Cotton leafroll dwarf virus (CLRDV). The stability of mRNAs already described as reference genes in cotton was also assessed. The geNorm, NormFinder, BestKeeper and ?Ct algorithms were used to select the best reference genes. In 8 of the 12 sets tested, miRNAs (miR172, 168 and 390) were found to be the best RGs. To validate the best selected RGs, miR159, miR164, miR2118, miR2910, miR3476, GhDCL2 and GhDCL4 expression levels were evaluated under biotic stress conditions, and miR164 and a putative myo-inositol oxigenase gene (GhMIOX) were assessed in leaves and flowers. The RGs selected in this work proved to be excellent reference genes in the two cases studied. Our results support the use of miRNAs as reference genes for miRNA and protein-coding genes.

SUBMITTER: Fausto AKS

PROVIDER: S-EPMC5393557 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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microRNAs as reference genes for quantitative PCR in cotton.

Fausto Anna Karoline Silva AKS Silva Tatiane da Franca TDF Romanel Elisson E Vaslin Maite F S MFS

PloS one 20170417 4

Cotton (Gossypium hirsutum) is the most important non-food plant in the world. Studies concerning the fiber quality and plant fitness of cotton at molecular level depend on high sensitive and reproducible gene-expression assays. However, only a few reports have described genes suitable for normalizing gene expression data. In this study, we report for the first time that microRNAs (miRNAs) are reliable reference genes (RGs) for cotton gene expression normalization in quantitative real-time rever ...[more]

PMID: 28414734

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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: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.

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MicroRNAs as reference genes for quantitative PCR in cotton.

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microRNAs as reference genes for quantitative PCR in cotton.

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