Dataset Information

Selection of the reference genes for quantitative gene expression by RT-qPCR in the desert plant Stipagrostis pennata.

ABSTRACT: The desert pioneer plant Stipagrostis pennata plays an important role in sand fixation, wind prevention, and desert ecosystem recovery. An absence of reference genes greatly limits investigations into the regulatory mechanism by which S. pennata adapts to adverse desert environments at the molecular and genetic levels. In this study, eight candidate reference genes were identified from rhizosheath development transcriptome data from S. pennata, and their expression stability in the rhizosheaths at different development stages, in a variety of plant tissues, and under drought stress was evaluated using four procedures, including geNorm, NormFinder, BestKeeper, and RefFinder. The results showed that GAPDH and elF were the most stable reference genes under drought stress and in rhizosheath development, and ARP6 and ALDH were relatively stable in all plant tissues. In addition, elF was the most suitable reference gene for all treatments. Analysis of the consistency between the reverse transcription-quantitative PCR (RT-qPCR) and RNA sequencing data showed that the identified elF and GAPDH reference genes were stable during rhizosheath development. These results provide reliable reference genes for assuring the accuracy of RT-qPCR and offer a foundation for further investigations into the genetic responses of S. pennata to abiotic stress.

SUBMITTER: Li R

PROVIDER: S-EPMC8571334 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Selection of the reference genes for quantitative gene expression by RT-qPCR in the desert plant Stipagrostis pennata.

Li Rong R Cui Kaiwen K Xie Quanliang Q Xie Shuangquan S Chen Xifeng X Zhuo Lu L Cao Aiping A Shen Haitao H Jin Xiang X Wang Fei F Li Hongbin H

Scientific reports 20211105 1

The desert pioneer plant Stipagrostis pennata plays an important role in sand fixation, wind prevention, and desert ecosystem recovery. An absence of reference genes greatly limits investigations into the regulatory mechanism by which S. pennata adapts to adverse desert environments at the molecular and genetic levels. In this study, eight candidate reference genes were identified from rhizosheath development transcriptome data from S. pennata, and their expression stability in the rhizosheaths ...[more]

PMID: 34741052

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Project description:ObjectiveTo underline the necessity of adequate reference genes for real-time quantitative polymerase chain reaction (RT-qPCR) and evaluate a novel tool for condition-specific reference gene selection.BackgroundRT-qPCR is a commonly used experimental technique that allows for highly sensitive analysis of gene transcription. Moreover, the use of internal reference genes as a means for relative quantification has rendered RT-qPCR a straightforward method for a variety of sciences, including dentistry. However, the expressional stability of internal reference genes must be evaluated for every assay in order to account for possible quantification bias.Materials and methodsHerein, we used the software tool RefGenes to identify putatively stable reference genes with the help of microarray datasets and evaluated them. Additionally, we propose an evidence-based workflow for adequate normalization of thusly identified genes. Human gingival fibroblasts (HGF-hTert), human acute leukemia-derived monocytes (THP-1), and telomerase immortalized gingival keratinocytes (TIGKs) were subjected to set-ups simulating various glycemic conditions and lipopolysaccharide challenges. Five common housekeeping genes (HKGs) and five genes from RefGenes were selected as targets and RT-qPCR was performed subsequently. Then, normalization algorithms Bestkeeper, Normfinder, and geNorm were used for further analysis of the putative reference gene stability.ResultsRefGenes-derived targets exhibited the highest stability values in THP-1 and TIGK cell lines. Moreover, unacceptable standard variations were observed for some common HKG like β-actin. However, common HKG exhibited good stability values in HGF-hTert cells.ConclusionThe results indicate that microarray-based preselection of putative reference genes is a valuable refinement for RT-qPCR studies. Accordingly, the present study proposes a straightforward workflow for evidence-based preselection and validation of internal reference genes.

Project description:Selection of reference genes has become an integral step in any real time quantitative PCR (RT-qPCR) based expression studies. The importance of this study stems from the fact that riverine buffaloes are major dairy species of Indian sub-continent and the information generated here will be of great interest to the investigators engaged in functional genomic studies of this important livestock species. In this study, an effort was made to evaluate a panel of 10 candidate reference genes (glyceraldehyde 3-phosphate dehydrogenase (GAPDH), beta- actin (ACTB), ubiquitously expressed transcript (UXT), ribosomal protein S15 (RPS15), ribosomal protein L-4 (RPL4), ribosomal protein S9 (RPS9), ribosomal protein S23 (RPS23), hydroxymethylbilane synthase (HMBS), β2 Microglobulin (β2M) and eukaryotic translation elongation factor 1 alpha 1 (EEF1A1) across 12 tissues (mammary gland, kidney, spleen, liver, heart, intestine, ovary, lung, muscle, brain, subcutaneous fat and testis) of riverine buffaloes. In addition to overall analysis, tissue wise evaluation of expression stability of individual RG was also performed. Three different algorithms provided in geNorm, NormFinder and BestKeeper softwares were used to evaluate the stability of 10 potential reference genes from different functional classes. The M-value given by geNorm ranged from 0.9797 (RPS9 and UXT) to 1.7362 (RPS15). From the most stable to the least stable, genes were ranked as: UXT/RPS9> RPL4> RPS23> EEF1A1> ACTB> HMBS> GAPDH> B2M> RPS15. While NormFinder analysis ranked the genes as: UXT> RPS23> RPL4> RPS9> EEF1A1> HMBS> ACTB> β2M> GAPDH> RPS15. Based on the crossing point SD value and range of fold change expression, BestKeeper analysis ranked the genes as: RPS9> RPS23/UXT> RPL4> GAPDH> EEF1A1> ACTB> HMBS> β2M> RPS15. Overall the study has identified RPS23, RPS9, RPL4 and UXT genes to be the most stable and appropriate RGs that could be utilized for normalization of transcriptional data in various tissues of buffaloes. This manuscript thus provide useful information on panel of reference genes that could be helpful for researchers conducting functional genomic studies in riverine buffaloes.

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

Selection of the reference genes for quantitative gene expression by RT-qPCR in the desert plant Stipagrostis pennata.

Publications

Selection of the reference genes for quantitative gene expression by RT-qPCR in the desert plant Stipagrostis pennata.

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