Dataset Information

Identification of optimal endogenous reference RNAs for RT-qPCR normalization in hindgut of rat models with anorectal malformations.

ABSTRACT: Background:Quantitative real-time polymerase chain reaction (RT-qPCR) is a sensitive method for quantifying mRNA abundance. With relative expression analysis, however, reliable data output is dependent on stably expressed reference genes across the samples being studied. In anorectal malformations (ARMs), there is limited data on the selection of appropriate reference genes. Purpose:This study was aimed to investigate the optimal reference genes for PCR in ARM rat models. Methods:We selected 15 commonly used reference genes (Rps18, Actb, B2m, Gapdh, Ppia, Hprt1, Pgk1, Ywhaz, Tbp, Ubc, Rps16, Rpl13a, Rplp1, Sdha, and Hmbs) as candidate reference genes and detected their mRNA expression in ARM samples by RT-qPCR. The expression stability and variability of these transcripts were subsequently evaluated using four methods (geNorm, NormFinder, comparative ?Ct, and BestKeeper). Results:The abundance of the candidate reference genes was qualified by RT-qPCR and the cycle threshold (Ct) values ranged between 14.07 (Rplp1) and 21.89 (Sdha). In the overall candidate genes, different variations existed across the different algorithms. A comprehensive analysis revealed that Rpl13a ranked first among the relatively stable genes, followed by Ywhaz, Rps18, Sdha, and Hmbs. Conclusions:The most stable reference genes for RT-qPCR were Rpl13a, Ywhaz, and Rps18 in ETU-induced ARMs in rat fetus. This study provided a foundation for reference gene selection for future gene expression analyses.

SUBMITTER: Long C

PROVIDER: S-EPMC6485207 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of optimal endogenous reference RNAs for RT-qPCR normalization in hindgut of rat models with anorectal malformations.

Long Caiyun C Xiao Yunxia Y Li Siying S Tang Xiaobing X Yuan Zhengwei Z Bai Yuzuo Y

PeerJ 20190423

<h4>Background</h4>Quantitative real-time polymerase chain reaction (RT-qPCR) is a sensitive method for quantifying mRNA abundance. With relative expression analysis, however, reliable data output is dependent on stably expressed reference genes across the samples being studied. In anorectal malformations (ARMs), there is limited data on the selection of appropriate reference genes.<h4>Purpose</h4>This study was aimed to investigate the optimal reference genes for PCR in ARM rat models.<h4>Metho ...[more]

PMID: 31065464

Similar Datasets

Project description:Anorectal malformations (ARMs) are among the most common congenital terminal digestive tract malformations. Circular RNAs (circRNAs), a novel type of endogenous non-coding RNAs, play roles in the development of the digestive system; however, their contributions to the pathogenesis of ARMs are not well-established. In this study, we explored the mechanism underlying ethylenethiourea (ETU)-induced ARMs by profiling circRNA expression via RNA-seq and constructing a regulatory circRNA-miRNA-mRNA network. Nine pregnant rats were gavage-fed a single dose of 125 mg/kg 1% ETU (ARM group) on gestational day 10 (GD10), and another 9 pregnant rats received a similar dose of saline (normal group) as a control. Embryos were obtained by cesarean section on the key time-points of anorectal development (GD14, GD15, and GD16). Hindgut samples isolated from the fetuses were evaluated by high-throughput sequencing and differentially expressed circRNAs were validated by reverse transcription-quantitative polymerase chain reaction, agarose gel electrophoresis, and Sanger cloning and sequencing. A total of 18295 circRNAs were identified in the normal and ARM groups. Based on the 425 differentially expressed circRNAs (|Fc| > 2, p < 0.05), circRNA-miRNA and miRNA-mRNA pairs were predicted using miREAP, miRanda, and TargetScan. A total of 55 circRNAs (14 up- and 41 downregulated in the ARM group compared to the normal group) were predicted to bind to 195 miRNAs and 947 mRNAs. Competing endogenous RNA networks and a Kyoto Encyclopedia of Genes and Genomes analysis revealed that novel_circ_001042 had the greatest connectivity and was closely related to ARM-associated signaling pathways, such as the Wingless Type MMTV integration site family, mitogen-activated protein kinase, and transforming growth factor-β pathways. These results provide original insight into the roles of circRNAs in ARMs and provide a valuable resource for further analyses of molecular mechanisms and signaling networks.

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:BackgroundA critical step in the RT-qPCR workflow for studying gene expression is data normalization, one of the strategies being the use of reference genes. This study aimed to identify and validate a selection of reference genes for relative quantification in Talaromyces versatilis, a relevant industrial filamentous fungus. Beyond T. versatilis, this study also aimed to propose reference genes that are applicable more widely for RT-qPCR data normalization in filamentous fungi.ResultsA selection of stable, potential reference genes was carried out in silico from RNA-seq based transcriptomic data obtained from T. versatilis. A dozen functionally unrelated candidate genes were analysed by RT-qPCR assays over more than 30 relevant culture conditions. By using geNorm, we showed that most of these candidate genes had stable transcript levels in most of the conditions, from growth environments to conidial germination. The overall robustness of these genes was explored further by showing that any combination of 3 of them led to minimal normalization bias. To extend the relevance of the study beyond T. versatilis, we challenged their stability together with sixteen other classically used genes such as β-tubulin or actin, in a representative sample of about 100 RNA-seq datasets. These datasets were obtained from 18 phylogenetically distant filamentous fungi exposed to prevalent experimental conditions. Although this wide analysis demonstrated that each of the chosen genes exhibited sporadic up- or down-regulation, their hierarchical clustering allowed the identification of a promising group of 6 genes, which presented weak expression changes and no tendency to up- or down-regulation over the whole set of conditions. This group included ubcB, sac7, fis1 and sarA genes, as well as TFC1 and UBC6 that were previously validated for their use in S. cerevisiae.ConclusionsWe propose a set of 6 genes that can be used as reference genes in RT-qPCR data normalization in any field of fungal biology. However, we recommend that the uniform transcription of these genes is tested by systematic experimental validation and to use the geometric averaging of at least 3 of the best ones. This will minimize the bias in normalization and will support trustworthy biological conclusions.

Dataset Information

Identification of optimal endogenous reference RNAs for RT-qPCR normalization in hindgut of rat models with anorectal malformations.

Publications

Identification of optimal endogenous reference RNAs for RT-qPCR normalization in hindgut of rat models with anorectal malformations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets