Dataset Information

Selection and validation of reference genes for normalization of quantitative real-time reverse transcription PCR analysis in Poria cocos (Schw.) Wolf (Fuling).

ABSTRACT:

Background

Quantitative real-time reverse transcription PCR (qRT-PCR) requires a stable internal control to avoid misinterpretation of data or errors for gene expression normalization. However, there are still no validated reference genes for stable internal control in Poria cocos (Schw.) Wolf (Fuling). This study aims to validate the reference genes of P. cocos.

Methods

This study firstly collected the 14 candidate reference genes by BLASTP from the genome of P. cocos for qRT-PCR analysis to determine the expression levels of 14 housekeeping genes (GAPDH, MAPK, β-Act, RPB2, RPB1-1, RPB1-2, his3-1, his3-2, APT, SAMDC, RP, β-Tub, EIF, and CYP) under different temperatures and in response to different plant hormones (indole-3-acetic acid, abscisic acid, 6-benzylaminopurine, methyl jasmonate, and gibberellic acid), and the threshold cycle (Ct) values. The results were analyzed by four programs (i.e., geNorm, NormFinder, BestKeeper, and RefFinder) for evaluating the candidate reference genes.

Results

SAMDC, his3-2, RP, RPB2, and his3-1 were recommended as reference genes for treating P. cocos with indole-3-acetic acid, abscisic acid, 6-benzylaminopurine, methyl jasmonate, and gibberellic acid, respectively. Under different temperatures RPB2 was the most stable reference gene. CYP was the most stable gene for all 90 samples by RefFinder.

Conclusion

SAMDC, his3-2, RP, RPB2, and his3-1 were evaluated to be suitable reference genes for P. cocos following different treatments. RPB2 was the most stable reference gene under different temperatures and CYP was the most stable gene in the mycelia under all six evaluated conditions.

SUBMITTER: Zhang X

PROVIDER: S-EPMC4774131 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Selection and validation of reference genes for normalization of quantitative real-time reverse transcription PCR analysis in Poria cocos (Schw.) Wolf (Fuling).

Zhang Xin X Xu Zhi-Chao ZC Xu Jiang J Ji Ai-Jia AJ Luo Hong-Mei HM Song Jing-Yuan JY Sun Chao C Hu Yuan-Lei YL Chen Shi-Lin SL

Chinese medicine 20160302

<h4>Background</h4>Quantitative real-time reverse transcription PCR (qRT-PCR) requires a stable internal control to avoid misinterpretation of data or errors for gene expression normalization. However, there are still no validated reference genes for stable internal control in Poria cocos (Schw.) Wolf (Fuling). This study aims to validate the reference genes of P. cocos.<h4>Methods</h4>This study firstly collected the 14 candidate reference genes by BLASTP from the genome of P. cocos for qRT-PCR ...[more]

PMID: 26937250

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Project description:(1)Objective: In this study, a quantitative analysis of chemical groups (the triterpenoids, water-soluble polysaccharides, and acidic polysaccharides) and quantitative high liquid performance chromatography (HPLC) fingerprint of Poria cocos (Schw.) Wolf (PC) for quality control was developed. (2)&nbsp;Methodology: First, three main chemical groups, including triterpenoids, water-soluble polysaccharides, and acidic polysaccharides, in 16 batches of PC were evaluated by ultraviolet spectrophotometry. Afterward, the quantitative fingerprint of PC was established, and the alcohol extract of PC was further evaluated. The method involves establishing 16 batches of PC fingerprints by HPLC, evaluating the similarity of different batches of PC, and identifying eight bioactive components, including poricoic acid B (PAB), dehydrotumulosic acid (DTA), poricoic acid A (PAA), polyporenic acid C (PAC), 3-epidehydrotumulosic acid (EA), dehydropachymic acid (DPA), dehydrotrametenolic acid (DTA-1), and dehydroeburicoic acid (DEA), in PC by comparison with the reference substance. Combined with the quantitative analysis of multi-components by a single marker (QAMS), six bioactive ingredients, including PAB, DTA, PAC, EA, DPA, and DEA, in PC from different places were established. In addition, the multivariate statistical analyses, such as principal component analysis and heatmap hierarchical clustering analysis are more intuitive, and the visual analysis strategy was used to evaluate the content of bioactive components in 16 batches of PC. Finally, the analysis strategy of three main chemical groups in PC was combined with the quantitative fingerprint strategy, which reduced the error caused by the single method. (3)&nbsp;Results: The establishment of a method for the quantification of chemical groups and quantitative HPLC fingerprint of PC was achieved as demonstrated through the quantification of six triterpenes in PC by a single marker. (4)&nbsp;Conclusions: Through qualitative and quantitative chemical characterization, a multi-directional, simple and efficient routine evaluation method of PC quality was established. The results reveal that this strategy can provide an analytical method for the quality evaluation of PC and other Chinese medicinal materials.

Project description:Poria cocos (Schw.) Wolf is a saprophytic fungus that grows around the roots of old, dead pine trees. Fushen, derived from the sclerotium of P. cocos but also containing a young host pine root, has been widely used as a medicine and food in China, Japan, Korea, Southeast Asian countries, and some European countries. However, the compound variations at the different growth periods and in the different parts of Fushen have not previously been investigated. In this study, an untargeted metabolomics approach based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) and targeted quantitative analysis was utilized to characterize the temporal and spatial variations in the accumulation of specialized metabolites in Fushen. There were 119 specialized metabolites tentatively identified using the UPLC-Q/TOF-MS. The nine growth periods of Fushen were divided into four groups using partial least squares discrimination analysis (PLS-DA). Four different parts of the Fushen [fulingpi (FP), the outside of baifuling (BO), the inside of baifuling (BI), and fushenmu (FM)] were clearly discriminated using a PLS-DA and orthogonal partial least squares discrimination analysis (OPLS-DA). Markers for the different growth periods and parts of Fushen were also screened. In addition, the quantitative method was successfully applied to simultaneously determine 13 major triterpenoid acids in the nine growth periods and four parts. The quantitative results indicated that the samples in January, March, and April, i.e., the late growth period, had the highest content levels for the 13 triterpenoid acids. The pachymic acid, dehydropachymic acid, and dehydrotumulosic acid contents in the FM were higher than those in other three parts in March, whereas the poricoic acid B, poricoic acid A, polyporenic acid C, dehydrotratrametenolic acid, dehydroeburicoic acid, and eburicoic acid in FP were higher beginning in October. These findings reveal characteristics in temporal and spatial distribution of specialized metabolites in Fushen and provide guidance for the identification of harvesting times and for further quality evaluations.

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 and validation of reference genes for normalization of quantitative real-time reverse transcription PCR analysis in Poria cocos (Schw.) Wolf (Fuling).

Background

Methods

Results

Conclusion

Publications

Selection and validation of reference genes for normalization of quantitative real-time reverse transcription PCR analysis in Poria cocos (Schw.) Wolf (Fuling).

Similar Datasets

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